Title

Trade in Intermediates and Final Products, Tradein Services and Goods : Implications ofDifferences in Their Determinants

Author(s)Yane, Haruka

Citation

国際公共政策研究. 2018, 23(1), p. 35-50

Version Type VoR
URL

https://doi.org/10.18910/70590

rights
Note

Osaka University Knowledge Archive : OUKA
https://ir.library.osaka-u.ac.jp/
Osaka University

35

Trade in Intermediates and Final Products, Trade in Services
and Goods― Implications of Differences in Their Determinants ―*Haruka YANE**

Refereed Article
Received: 26 March 2018, Accepted: 25 June 2018

Abstract
With the growing integration of the world economy, there have been more and more studies looking at trade and its
various effects on different aspects of the economy. Many of these studies, however, focus merely on manufacturingindustries or products. This paper attempts to show how different kinds of trade other than manufacturing also matter,and reveal their distinct features. This study uses product-level international bilateral trade panel data for the period
1995 to 2009, covering 40 countries. Constructing this dataset into a gravity model, this paper aims to clarify whatmakes trade in intermediates different from final products, and total trade in goods different from total trade in services.
This paper estimates this gravity model in its multiplicative form using the Poisson pseudo-maximum likelihood estimator. Estimation results show that the demand for intermediate inputs tend to be less subject to consumers’ preferences
and rather rely more on geographical proximity compared to final products. In addition, current regional trade agreements and trade partnerships still tend to be goods oriented and have scope to cover aspects of services trade. Consequently, this study illustrates the importance of distinguishing trade in different products and whether they are forintermediate use or final consumption.

Keywords: Trade in intermediates, services trade, gravity model, Poisson-pseudo-maximum likelihood (PPML) estimator, World Input-Output Database (WIOD)JEL Classification numbers: F13, F14, F15*

I would like to thank Professors Hiro Lee and Tsunehiro Otsuki for their helpful comments, and Susan Stone, Senior Advisor at the Organisation for
Economic Co-operation and Development (OECD), for introducing me to the World Input-Output Database. All remaining errors are my own.
**
Visiting Fellow, Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama, Toyonaka, Osaka 560-0043, Japan.
Email: h-yane@osipp.osaka-u.ac.jp.

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1. Introduction
In recent years we have witnessed the world economy rapidly integrating through international trade. Behind
this phenomenon is the increasing prominence of trade in intermediates and trade in services during the past two
decades. The international fragmentation of production in global value chains has been motivated by sourcing
intermediate inputs from more cost-efficient producers in order to enhance efficiency. As a result, domestic
production has been increasingly relying on foreign intermediate inputs. Further, rapid advances in informationand communications technology (ICT) and infrastructure growth have increased the tradability of many service
activities, thereby facilitating the sourcing of services from abroad.
Figures 1 and 2 depict how the interconnectivity of production processes has been increasing through trade inintermediate inputs. First, Figure 1 shows the increasing trend in the world’s ratio of foreign to domestic inputs.
Firms are increasingly purchasing and using intermediate inputs from abroad. According to Miroudot, Lanz and
Ragoussis (2009), trade in intermediates account for about 56% of world trade in case of goods and 73% in caseof services. Figure 2 reveals the increasing share of world’s intermediates trade in total trade, in contrast with thestagnant share of world’s final products trade in total trade. In addition, you can see how intermediates trade sharein total trade in East Asia is exceptionally high, leading the increasing trend in trade in intermediate products.
25%

20%

15%

10%

5%

0%

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2008

2009

World

Source: Author’s calculation based on WIOD.

Figure 1: Trade in Intermediates – Ratio of Foreign to Domestic Intermediates
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

1995

1996

1997

1998

1999

2000

World imports in intermediates

2001

2002

2003

East Asia imports in intermediates

2004

2005

2006

2007

World imports in final products

Source: Author’s calculation based on WIOD.

Figure 2: Trade in Intermediates and Final Products as a Share of Total Imports

Trade in Intermediates and Final Products, Trade in Services and Goods

37

Second, trade in services shows higher growth compared to trade in goods, as you can see from Figure 3. Figure
4 shows how trade in services has been representing a higher share of total trade than trade in goods. Moreover,what is striking is that both figures depict that services trade seems to have been affected much less by the financial
crisis compared to goods trade.
250

200

150

100

50

0

1995

1996

1997

1998

1999

2000

2001

2002

Goods

2003

2004

2005

2006

2007

2008

2009

2007

2008

2009

Services

Source: Author’s calculation based on WIOD.

Figure 3: Imports of Goods and Services (year 2000=100)
120
115
110
105
100
95

90
85
80

1995

1996

1997

1998

1999

2000

2001

Goods

2002

2003

2004

2005

2006

Services

Source: Author’s calculation based on WIOD.

Figure 4: Trade in Goods and Services as a Share of Total Imports (year 2000=100)

Innovations such as the internet and container shipping have revolutionized trade and value chains. Thanks tothese developments, we are seeing more and more of trade in intermediates and trade in total services, evenseemingly overpowering the traditional trade in final products and trade in total goods. There has been, however,comparatively much less attention paid to trade in intermediates and trade in services, mostly due to the lack ofstatistics. Therefore, this present study will try to present new additional evidence on the different implications fordifferent forms of trade, by using the World Input-Output Database (WIOD).
This paper’s main goal is to see the differences between what determines the amount of trade in intermediate
inputs and trade in final products, and between determinants of goods trade and services trade. For instance, what
factors affect trade in intermediates more as opposed to trade in final products, and do trade costs have asignificantly greater impact on trade in goods or trade in services? It is important to distinguish these trade flows
because first, purchasers for intermediate inputs are different from those for end-use final products. It is usually

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国際公共政策研究第 23 巻第１号

the firms who buy and use intermediate inputs for further production, and final products are sold to end-users,
often consumers.1 Second, goods and services also have distinctive characteristics2, for instance, services have an
intangible nature, and production and consumption must occur simultaneously (Kimura and Lee, 2006). Therefore,
although they are all international trade, their determinants should vary. This paper attempts to show theprominence of different kinds of trade in contrast with manufactured goods, and clarify their distinct characteristics.
Hence, this study uses the framework of gravity model of bilateral trade to analyze the differences in what
determines the amount of trade 1) between intermediates and final products, and 2) and between goods and services.
In order to account for the zero trade flows between countries, and also to control for the heteroskedasticity present
in the data, this study applies the Poisson pseudo-maximum likelihood (PPML) estimator to the gravity model (seeSantos Silva and Tenreyro, 2006).
The results from this study indicate how important it is to distinguish goods from services, and intermediate-use
products from final-use products. More specifically, they show that trade in intermediate inputs is more sensitive
to trade costs compared to trade in final products, and responds much more to regional trade agreements (RTAs).
One important characteristic of services trade to note is that current regional trade agreements have not played asignificant role for two countries to engage in services trade.
In this paper I will assess the differences between intermediate and final products (including both goods andservices products) trade, as well as differences between trade in services and trade in goods. The rest of the paper
is organized as follows. Section 2 provides background and reviews the literature. Section 3 describes the data,
gravity model for international trade, and estimation techniques to account for observations with zero trade flows
and to deal with the presence of heteroskedasticity under the assumption of a multiplicative error term. Section 4
discusses the estimation results of the model using the PPML estimator. Section 5 concludes.

2. Literature Review
2.1: Trade in Intermediates‘Global value chains’ have rapidly emerged as production processes have become more geographicallyfragmented since the 1990s. International trade and production are increasingly structured around these ‘global
value chains’. A ‘value chain’ can be simply defined as the full range of activities that firms and workers carry out
in order to bring a product from its conception to its end use and beyond (see Gereffi and Fernandez-Stark, 2011).
The term ‘global’ comes from the fact that these activities are increasingly spread over multiple countries. The
fragmentation of value chains has been motivated by sourcing intermediate inputs from more cost-efficient
producers, foreign or domestic, in order to enhance efficiency. As a result, domestic production has been
increasingly relying on foreign intermediate inputs, as shown in Figure 1.
With this increasing presence of trade in intermediates, there continues to be a rising demand for comprehensive
and trustable data on the various dimensions of the internationalization of production networks. The increasing
fragmentation of production across countries, however, has led current global production networks to be multiWhile final products highly rely on advertisement and promotion aimed for final consumers, intermediate inputs trade could be determined moreby other factors, such as preferences, customs and habits (Miroudot et al., 2009).
2
For more on trade in services and the four modes of supply, see the document for General Agreement on Trade in Services (GATS), available on
the World Trade Organization website (http://www.wto.org/english/tratop_e/serv_e/serv_e.htm).
1

Trade in Intermediates and Final Products, Trade in Services and Goods

39

country and back-and-forth in nature (Koopman, Powers, Wang and Wei, 2011), making it difficult to capture instatistics.
All official trade statistics are measured in gross terms, which include both intermediate inputs and final products.
They record the value of intermediate inputs traded along the value chain, crossing international borders several
times, back and forth, for further processing. These trade flows are thus counted multiple times. Consequently, thecountry of the final producer appears as creating most of the value of goods and services traded, giving themisleadingly wrong picture, overlooking the role of countries providing inputs upstream in the global value chain.
For example, an exported good may require considerable intermediate inputs from domestic manufacturers, who,
in turn, require considerable intermediate imports, and so, much of the revenue, or value-added, from selling theexported good may accrue abroad to reflect purchases of intermediate imports used in production.

2.2: Trade in Services
Globalization is no longer only about goods; it increasingly involves trade in services. Many service activities
are becoming internationalized. Services trade liberalization has reduced regulatory barriers in key sectors of the
global logistics chain, such as transport, finance and telecommunications. Business services, for example, are now
an integral part of the global value chain. According to Johnson (2014), the ratio of value-added to gross exports
for goods trade is lower compared to services trade. This is because manufacturing firms purchase services
activities as part of their production processes and gross manufacturing exports include value-added from theservices sector.
In spite of this increasing prominence of trade in services, however, much attention has been focused on theimpact of relevant factors on trade in goods. This is partly due to the unfortunate fact that official statistical data
do not provide much detailed information on services trade, compared to the goods sector, where they have detailed
and timely data available for a broad range of countries. In contrast, the quality of bilateral services trade statistics
by industry or product is unsurprisingly very low, with many missing observations.

2.3: Previous Empirical Studies
There has been a recent rise in attention to both trade in intermediate inputs and trade in services. Yet researchhas been hindered by the limited availability of reliable and adequate statistics. Coming up with a methodology todifferentiate between intermediate and final products, as well as measuring trade in services, has been challenging.
Due to these limitations in the international trade data, there have only been a handful of empirical studies on
trade in intermediate inputs or trade in services. The recent development in data quality and availability, however,
have helped boost the number of studies. This is all thanks to the attempts to devise methods of measuring valueadded trade in the empirical literature. Daudin, Rifflart and Schweisguth (2011) and Koopman et al. (2011) areamong the first to explicitly refer to a measurement of trade in value-added using an empirical framework.
One of the recent prominent empirical works is Miroudot et al. (2009), where they analyze trade in intermediate
goods and services, comparing intermediates trade and final products trade across goods and services industries,
using data at four- to five-year intervals (mostly years 1995, 2000 and 2005). They use disaggregate trade data at
the industry level. This present paper mainly follows their study because they also make a comparison between
the goods industry and services industry.
As for research focusing on trade in services, Kimura and Lee (2006) use a standard–type gravity equation to40

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assess the differences between trade in services and trade in goods, utilizing bilateral trade data for 26 OECDmember countries for years 1999 and 2000. Fukao and Ito (2003) estimate gravity equations to test whether Japan’s
market for services is more closed for establishment transactions, using data on U.S. services exports, for years
1992 and 1997.
All these existing empirical studies, however, have limitations that this study seeks to overcome. Former
empirical studies either used aggregate trade data, and/or data at four to five-year intervals. The present study
contributes to the intermediates trade and services trade literature because unlike previous research, this study uses
a panel dataset: annual data from 1995 to 2009 and trade data disaggregated at the product-level. Compared withformer studies, this should reveal a more consistent and detailed picture of the recent development in different
patterns of trade. This study also contributes to the literature by conducting empirical analyses for both trade inintermediates and trade in services. Moreover, this study attempts to re-examine the impact of regional trade
agreements in the context of different trade flows.

3. Methodology
3.1: Data
This study uses trade data from the World Input-Output Database (WIOD)3, which was released for the generalpublic in April 2012. The international supply and use table covers annual time-series data from 1995 to 2009 for
40 countries. Table 1 depicts country coverage by continent. In order to maximize observations for trade in services,trade data broken down by different products were used, instead of those classified by industries4. For trade inservices, I strictly followed and included all products covered by the General Agreement on Trade in Services5. As
a result, this dataset provides this analysis with a good coverage balance between goods and services trade.6 All
intermediate, final and total goods and services trade data are classified into 59 products, based on Statistical
Classification of Products by Activity (CPA). The classification of products into goods and services sectors isshown in Table 2.
Table 1: Country Coverage by ContinentContinent
EuropeAmericas

Country

ISO

CountryISO

Austria

AUT

BrazilBRA

Belgium

BEL

CanadaCAN

Bulgaria

BGR

MexicoMEX

CyprusCYP

United States

USA

Czech Republic

CZE

Obs.

138,060

3
The World Input-Output Database is available at http://www.wiod.org/database/index.htm.
The core of the database is a set of harmonized national supply and use tables, linked together with bilateral trade data in goods and services. These
two sets of data are then integrated into a world input-output table. See Timmer (2012) for the detailed framework and calculations.
4
The WIOD classifies 59 products (based on CPA) whereas for industries they only have 35 categories (based on NACE rev.1 (ISIC rev.2)
classifications).
5
The GATS services sectoral classification list is available at http://www.wto.org/english/tratop_e/serv_e/serv_e.htm or
http://www.wto.org/english/tratop_e/serv_e/mtn_gns_w_120_e.doc.
6
52.5% of the data are on goods trade and 47.5% are on services trade.

Trade in Intermediates and Final Products, Trade in Services and Goods

41

Germany

DEU

Denmark

DNK

Spain

ESP

Australia

AUS

Estonia

EST

China

CHN

Finland

FIN

Indonesia

IDN

France

FRA

India

IND

United Kingdom

GBR

Japan

JPN

GreeceGRC

South Korea

KOR

Hungary

HUN

RussiaRUS

IrelandIRL

Taiwan

TWN

Italy

ITA

Obs.

Lithuania

LTU

Percentage

Luxembourg

LUX

Latvia

LVA

Malta

MLT

Netherlands

NLD

PolandPOL

Portugal

PRT

Romania

ROU

Slovak Republic

SVK

Slovenia

SVN

SwedenSWE

TurkeyTUR

Obs.

Percentage

10%
Asia Pacific276,12020%

966,420

Percentage

70%

Table 2: Products Description with CPA Classification
Goods Sector

Services Sector

CPA

Product

CPA

Product

1

Products of agriculture, hunting and related services

40

Electrical energy, gas, steam and hot water2

Products of forestry, logging and related services

41

5

10

11

Fish and other fishing products; services incidentalof fishing
Coal and lignite; peat
Crude petroleum and natural gas; services incidental
to oil and gas extraction

45

50

51

Collected and purified water, distribution services ofwater
Construction work
Trade, maintenance and repair services of motor
vehicles and motorcycles; retail sale of a
Wholesale trade and commission trade services,
except of motor vehicles and motorcycle

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Retail trade services, except of motor vehicles and

12

Uranium and thorium ores

52

13

Metal ores

55

Hotel and restaurant services

14

Other mining and quarrying products

60Land transport; transport via pipeline services

15

Food products and beverages

61

Water transport services

16

Tobacco products

62

Air transport services

17

Textiles63

18

Wearing apparel; furs

64

19

Leather and leather products

65

20

Wood and products of wood and cork (except
furniture); articles of straw and plaiting mate66

motorcycles; repair services of person

Supporting and auxiliary transport services; travel
agency services
Post and telecommunication services
Financial intermediation services, except insurance
and pension funding services
Insurance and pension funding services, except
compulsory social security services

21

Pulp, paper and paper products

67

Services auxiliary to financial intermediation

22

Printed matter and recorded media

70

Real estate services

23

Coke, refined petroleum products and nuclear fuels71

24

Chemicals, chemical products and man-made fibers

72

Computer and related services

25

Rubber and plastic products

73

Research and development services

26

Other non-metallic mineral products

74

Other business services

27

Basic metals

75

28

Fabricated metal products, except machinery andequipment

Renting services of machinery and equipment
without operator and of personal and house

Public administration and defense services;compulsory social security services

80

Education services
Health and social work services

29

Machinery and equipment n.e.c.

8530

Office machinery and computers

90

31

Electrical machinery and apparatus n.e.c.

91

Membership organization services n.e.c.

92

Recreational, cultural and sporting services

93

Other services

95

Private households with employed persons

32

33

Radio, television and communication equipment
and apparatus
Medical, precision and optical instruments, watches
and clocks

34

Motor vehicles, trailers and semi-trailers

35

Other transport equipment

36

Furniture; other manufactured goods n.e.c.

37

Secondary raw materials
52.5%

Sewage and refuse disposal services, sanitation andsimilar services

47.5%

Trade in Intermediates and Final Products, Trade in Services and Goods

43

In addition to its disaggregate, product-level data characteristic, the advantage of this input-output database isthat it covers a period of 15 consecutive years, whereas most former studies had to rely on data at five-year intervals
more or less (Baier and Bergstrand, 2007; Johnson and Noguera, 2017).
In order to approximate non-policy trade costs in a standard gravity model, this study uses proxies discussed inJohnson and Noguera (2017), which are distance, common language, adjacency (common borders) and colonial
links. These data were obtained from CEPII’s database7. Descriptive statistics for continuous variables are shownin Table 3.
Table 3: Descriptive Statistics for Continuous Variables
Variable

Unit of measurein million US
Total imports
dollars
Intermediatesin million US
imports
dollars
Final products in million US
imports
dollars
Total goods
in million US
imports
dollars
Total services in million US
imports
dollars
Distance

km

Obs.

Mean

Std. Dev.

Min.

Max.

1,380,600

65.6

591

0

72,3001,380,600

39.7

365

0

59,7001,380,600

25.8

327

0

55,100725,400

104

778

0

72,300655,200

23.2

249

0

44,7001,380,600

5,133

4,433

59.62

18,523.81

3.2: Model
This study estimates bilateral trade using a standard gravity model, initially proposed by Tinbergen (1962).
Gravity equations have been favored in trade literature mainly because of their high explanatory power, empirically
succeeding at predicting bilateral trade flows. However, the model has been criticized for lacking respectable
theoretical foundations, causing it to suffer from incorrect specifications. Anderson and van Wincoop (2003)
developed a solution by augmenting the traditional equation with multilateral resistance terms (for example,
calculating remoteness indices, or adding exporter and importer fixed effects), adding strong theoreticalfoundations to the model. In addition, Head and Mayer (2014) state that importer and exporter fixed effects should
be time varying with dataset that span many years. The combination of being consistent with theory and relatively
easy to implement led to a surge in adoption in empirical work. Feenstra (2004) explains that gravity equation has
finally succeeded empirically in the field of international trade.
Inspired by Sir Isaac Newton’s work on Law of Universal Gravitation8, for which he published a paper in 1687,
Tinbergen (1962) linked the functional form to international trade flows, predicting a gravity relationship for tradeflows analogous to Newton’s law. It holds that the bilateral trade flows between two countries are directly
proportional to the trading countries’ respective economic sizes, and inversely related to trade costs. Trade costs
can be proxied by the geographical distance9 between the two trading countries, while their size of the market canbe proxied using their respective GDPs. Therefore, in other words, the underlying assumption of the gravity modelof bilateral trade is that trade flows should increase as a trading partner’s GDP rises, and should decrease if distance
CEPII databases are available at http://www.cepii.fr/anglaisgraph/bdd/bdd.htm.
This study uses contiguity indicator, common colonial origin indicator and common official language indicator from the CEPII Gravity Dataset.
Data on distance, measured as the simple distance between the capitals in the two countries, is from CEPII GeoDist.
8
A calculator based on this law can be found here: http://www.pythia.com.ar/?id=gravlaw.
9
Miroudot and Ragoussis (2009) add that in addition to transport costs, distance, used as a proxy for trade costs, also captures regulatory
differences as well as cultural differences between countries.
7

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between a trading partner is farther. This study takes the fixed-effects approach; hence exporter and importer fixed
effects are included in place of GDPs of importing and exporting countries, in order to account for multiple
resistance.
Following Miroudot et al. (2009)’s gravity regression model, the standard gravity equation to be estimated hereis as follows.10 Let 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 indicate the level of bilateral imports of intermediate or final goods and services
product p, between country i and country j in year t. Subscript g is the indicator of whether it is intermediate or

final goods and services trade:

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛼𝛼0 + 𝛼𝛼1 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑔𝑔 + 𝐗𝐗𝑖𝑖𝑖𝑖 𝜷𝜷𝟏𝟏 + 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝒈𝒈 𝐗𝐗𝑖𝑖𝑖𝑖 𝛃𝛃𝟐𝟐 + 𝜇𝜇𝑖𝑖𝑖𝑖𝑖𝑖 + 𝜈𝜈𝑗𝑗𝑗𝑗𝑗𝑗 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 .

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑔𝑔 is a dummy variable that takes the value 1 for final goods and services trade and 0 for intermediate goods

and services trade. 𝜇𝜇𝑖𝑖𝑖𝑖𝑖𝑖 and 𝜈𝜈𝑗𝑗𝑗𝑗𝑗𝑗 are destination-product-time and origin-product-time fixed effects respectively,in order to account for their consumer price indices as well as their sizes of economies. 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the error term.

Likewise, let 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 indicate the level of total goods or total services product p, between country i and country
j in year t:

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛾𝛾0 + 𝛾𝛾1 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑝𝑝 + 𝐗𝐗𝑖𝑖𝑖𝑖 𝜹𝜹𝟏𝟏 + 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑝𝑝 𝐗𝐗𝑖𝑖𝑖𝑖 𝛅𝛅𝟐𝟐 + 𝜅𝜅𝑖𝑖𝑖𝑖𝑖𝑖 + 𝜔𝜔𝑗𝑗𝑗𝑗𝑗𝑗 + 𝜉𝜉𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 .

𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑠𝑠𝑝𝑝 is a dummy variable that takes the value 1 for total goods trade and 0 for total services trade. 𝜅𝜅𝑖𝑖𝑖𝑖𝑖𝑖 and𝜔𝜔𝑗𝑗𝑗𝑗𝑗𝑗 are destination-product-time and origin-product-time fixed effects respectively. 𝜉𝜉𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 is the error term.

The vector of country-pair characteristics, 𝐗𝐗𝑖𝑖𝑖𝑖 , includes (1) the natural log of the geographic distance between

the capitals of countries i and j; (2) an indicator of whether countries i and j share a common land border; (3) anindicator of whether the country pairs share a common official language; (4) an indicator of whether the country

pairs ever had colonial ties; (5) an indicator of whether both countries have a regional trade agreement with eachother11; (6) regional indicators of whether both countries are in Europe, whether both countries are members of
the Association of Southeast Asian Nations (ASEAN) plus China, Japan, Korea and Taiwan, or whether bothcountries are members of the North American Free Trade Agreement (NAFTA).
Based on this standard gravity equation, this study will pool across products 12 . The dependent variable isbilateral trade in (1) intermediate goods and services, (2) final goods and services, (3) total goods, and (4) total
services, respectively, with the same regressors. This study follows Miroudot et al. (2009) and Kimura and Lee(2006) with regard to using the same gravity equation framework as goods trade for services trade.
This paper estimates import equations because the dataset consists of the same set of home and partner countries:
exports are defined as mirror flows from imports (i.e., values of exported products from country j to country i are
assumed to be equal to the values of imported products of country i from country j) in the WIOD trade data thisstudy uses (Timmer, 2012).
In addition, following Frankel (1997), I have also included regional binary variables for each year to test theeffects of membership in regional trade agreements. This study also includes Europe, East Asia (ASEAN plus
China, Japan, Korea and Taiwan) and NAFTA as broader trading bloc dummy variables. The regional variables
10
I would like to thank my anonymous referee for suggesting to use this methodology to make a comparative analysis between different tradeflows.
11
This variable was created by using de Sousa, J. (2012)’s Regional Trade Agreements data and programs (available at
http://jdesousa.univ.free.fr/data.htm).
12
For each importer-exporter country pair in a given year, 59 products for both trade in intermediates and final products; 31 products for trade ingoods, and 28 products for trade in services.

Trade in Intermediates and Final Products, Trade in Services and Goods

45

represent a country’s formal membership status of a trade agreement with the other country in the correspondingyear.

3.3: Estimation Techniques
Standard procedure to estimate multiplicative gravity models for trade is to take the logarithms so as to be able
to estimate it in linear form. This study, however, uses a dataset for which around 40% of the bilateral trade data
between country i and country j is in fact zero.13 Zero trade flows are actually quite common in the bilateralinternational trade matrix (see, for example, Haveman and Hummels, 2004; Santos Silva and Tenreyro, 2006;
Helpman et al., 2008). The proportion of zeros increases in line with country diversity (40*39 bilateral country
pairs for this study) as well as sectoral disaggregation (59 classified products in this analysis). On the one hand,presence of zeros tends to bias estimates (especially OLS gravity model estimates), but on the other hand,disregarding the zeros means throwing away some potentially interesting information. Although the literature is
still undecided as to the best way to deal with the zero trade flows, the following are among the few of theapproaches commonly taken in empirical studies.
One of the approaches is to simply drop the pairs with zero bilateral trade from the dataset by estimating the
log-linear form (for example, see Johnson and Noguera, 2017). 14 This means, however, that an important
observation is left out of the model: the zero trade flow. This is undesirable because the omitted observations
contain information as to why low levels of trade flow are observed, or why some countries trade in some products
while others do not.
In order to avoid throwing away observations with zero-values, Eichengreen and Irwin (1994; 1998) estimate
their model using the logarithm of (1 + Trade) as the dependent variable. However, Linders and de Groot (2006),
among others, showed that this approach is prone to yield upward biased estimates.
An alternative procedure is to use the sample selection correction introduced by Heckman (1979), which corrects
for this possible selection bias. Linders and de Groot (2006) make use of the Heckman selection model for a gravity
equation and conclude that compared to using OLS as the estimation methodology, it is preferable to use the sample
selection model. Imposing exclusion restrictions, Helpman et al. (2008) developed a model that accounts for the
self-selection of firms into engaging in international trade and their impact on trade volumes. Choosing true
identifying restrictions, however, is tricky. Other drawbacks of using the Heckman model is that it does not deal
well with heteroskedasticity. In addition, the fixed effects probit model in the first stage suffer from the incidental
parameters problem.
Lastly, the PPML estimator provides consistent estimates of the original nonlinear gravity model, since “it isexactly equivalent to running a type of nonlinear least squares on the original equation” (Shepherd, 2012), given
the assumption that the gravity model contains the correct set of explanatory variables is satisfied. The PPML
estimator is not only consistent in the presence of fixed effects, but also able to control for the presence ofheteroskedasticity under a multiplicative error term.
Indicating that log linear models cannot be expected to provide unbiased estimates under heteroskedasticity,
Santos Silva and Tenreyro (2006) proposed the PPML model as an econometric solution for the zero-value problem
in count data. This model, commonly used for count data, is known for its ability to deal with occurrence of zeros
13
14

38.9%, 44% and 37% of the observations are zero, for trade in intermediates, final products, and total goods and services, respectively.
Taking logarithms effectively drops such observations from the sample because log(0) is undefined.

46

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国際公共政策研究and discrete nonnegative nature of the dependent variable (see Greene, 2008). Conveniently, it can appropriately
be applied to the gravity model since it is a pseudo maximum likelihood estimator. Hence, the PPML estimatorcan produce unbiased and consistent estimates of the variables of interest. Consequently, this study applies the
PPML estimator.

4. Main Results
4.1: Trade in Intermediates and Trade in Final Products
Table 4: Trade in Intermediates and Trade in Final Products: PPML Estimation Results
TradelnDistancelnDistance×Dfinal
Adjacency
Adjacency×Dfinal
Common language
Common language×Dfinal
Colonial ties
Colonial ties×Dfinal
RTA dummy
RTA dummy×Dfinal
European bloc
European bloc×Dfinal
East Asian bloc
East Asian bloc×Dfinal
NAFTA bloc
NAFTA bloc×Dfinal
DfinalConstant
R-squared
Observations

Intermediates Imports & Final Products
Imports-0.945***(0.00)
0.319***(0.00)
1.928***(0.00)-0.914***(0.00)
0.516***(0.00)
0.219***(0.00)
0.563***(0.00)
0.036***(0.00)
0.288***(0.00)-0.181***(0.00)
0.802***(0.00)
0.284***(0.00)
0.761***(0.00)
1.391***(0.00)
0.407***(0.00)
1.613***(0.00)-0.727***(0.00)
17.411***(0.00)
0.172
2,761,200

Notes: Estimates are made with origin-product-time and destination-product-time fixed effects. Clustered standard errors
are shown in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Trade in Intermediates and Final Products, Trade in Services and Goods

47

Table 4 presents the results for trade in intermediates and trade in final products. First, the negative impact of
distance on intermediate inputs trade is relatively higher, compared to that on final products trade. Similarly,
adjacency is more important for intermediates trade compared to final products trade. These differences may be
because intermediate inputs tend to be less subject to consumers’ preferences and rely more on geographicalproximity. Cultural ties, such as common languages and colonial ties, however, deemed to be more essential forfinal products trade, compared to intermediate products. This implies that smaller countries considering to open
up trade may have a higher chance of succeeding in the global market by exporting intermediate inputs rather than
final products, if they target countries that are near them.
Second, the RTA dummy has a positive effect that is statistically significant at the 1% level on both trade in final
products and trade in intermediate inputs. This positive impact of a regional trade agreement is stronger forintermediates trade. In addition, when both countries belong to the European bloc, they tend to trade final products
much more than otherwise similar countries. The same can be said for East Asian and North American countries.
This may suggest that there is an increasing trend for countries to specialize in assembling final products that theyexport to other countries in the same regional bloc.

4.2: Trade in Goods and Trade in Services
Table 5: Trade in Goods and Trade in Services: PPML Estimation Results
TradelnDistancelnDistance×Dgoods
Adjacency
Adjacency×Dgoods
Common language
Common language×Dgoods
Colonial ties
Colonial ties×Dgoods
RTA dummy
RTA dummy×Dgoods
European bloc
European bloc×Dgoods
East Asian bloc
East Asian bloc×Dgoods
NAFTA bloc

Goods Imports
Imports&-0.463***(0.00)-0.615***(0.00)
1.132***(0.00)-0.883***(0.00)
1.083***(0.00)-1.021***(0.00)
2.025***(0.00)
1.613***(0.00)-1.292***(0.00)
6.259***(0.00)
3.183***(0.00)-3.038***(0.00)-5.482***(0.00)
10.509***(0.00)-6.998***(0.00)

Services

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国際公共政策研究

NAFTA bloc×Dgoods
DgoodsConstant
R-squared
Observations-6.207***(0.00)-0.639(0.00)-26.246(0.00)
0.345
1,380,600

Notes: Estimates are made with origin-product-time and destination-product-time fixed effects. Clustered standard errors
are shown in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 5 shows the regression results with goods imports and services imports as dependent variables. First, trade
in services is affected less by distance compared to trade in goods. Although ICT innovation has indeed reduced
the role of distance for services trade, the innovation in infrastructure and container shipping have also decreased
the transportation costs for goods, notwithstanding it is still more costly than services.
Second, when two countries share the same official language, they tend to trade services much more, compared
to goods, than two otherwise similar countries.15 Colonial ties, on the other hand, play a greater role for total goods
trade. Third, members of the East Asian regional bloc tend to trade goods more than otherwise. This is consistentin that the East Asia region is known for its active manufacturing production processes and their fragmentation.
The results for the European regional bloc reveal an opposite trend; two European countries tend to engage morein services trade compared to goods trade. In the NAFTA region, both services trade and goods trade are affected
negatively when two members engage in trade. This indicates that any of the three pairs of countries trade muchless than expected, given their market size, distance and such.
As for services, regional trade agreements show that they have a negative impact on services trade. This suggeststhat the current agreements and partnerships still tend to be goods oriented and do not cover much regarding
services trade. This also means that with a little improvement, we may see a boost in services trade. The great
financial crisis has affected services trade much less than goods trade, as shown in the evident resilience of services
trade to the financial crisis in Figures 3 and 4, and also with Borchert and Mattoo (2009). They explain that services
trade has weathered the crisis much better than goods trade for two reasons: demand for a range of traded services
is less cyclical, and services trade and production are less dependent on external finance.

5. Concluding Remarks
This study assesses how the same factors, such as trade costs and regional trade agreements, can significantly
affect intermediate, final and total goods and services trade flows differently. With technological developments in
ICT and transportation, trade in intermediates and trade in services have been increasing their prominence ininternational trade. However, constrained by data limitations, there have not been that many empirical studies.
Therefore, this paper contributes to the literature by conducting empirical analyses for both trade in intermediate
inputs and trade in services. The main findings of this paper and policy implications based on them can besummarized as follows:
The formula to compute this effect is (𝑒𝑒 𝑏𝑏𝑖𝑖𝑖𝑖 − 1) ∗ 100%, where 𝑏𝑏𝑖𝑖𝑖𝑖 is the estimated coefficient. So here, for example, the computation will be[exp(1.083)-1.0]*100=195.4%] for services trade.

15

Trade in Intermediates and Final Products, Trade in Services and Goods49

1. Trade in intermediate inputs
First, demand for intermediate inputs tend to be less subject to consumers’ preferences and rather rely more on
geographical proximity compared to final products. This implies that smaller countries considering opening up
trade may have a higher chance of succeeding in the global market by exporting intermediate inputs and targeting
nearby countries. Second, regional trade agreements have a more positive effect on intermediates trade than final
products trade. This implies an alignment in the increase of the presence of global value chains and negotiations
for trade agreements.
2. Trade in services
First, although ICT innovation has reduced the role of distance for services trade, the innovation in infrastructure
and container shipping have also decreased the transportation costs for goods, notwithstanding it is still more costly
than services. Second, services trade is much more prominent in the European region while goods trade is much
more dominant in the East Asian region. Lastly, current regional trade agreements and trade partnerships still tend
to be goods oriented and have scope to cover aspects of services trade. Thus, with little improvement, we may see
a boost in services trade.
Through empirical analysis, this present study showed that intermediates and final products, and their trade indifferent sectors vary greatly in their characteristics using the gravity model. Therefore, it is necessary to
distinguish trade in different products and whether they are for intermediate use or final consumption, whenenforcing policies.

References
Anderson, J. E. and van Wincoop, E. 2003. Gravity with gravitas: A solution to the border puzzle. American
Economic Review 93(1): 170-192.
Baier, S. L. and Bergstrand, J. H. 2007. Do free trade agreements actually increase members’ international trade?
Journal of International Economics 71: 72-95.
Borchert, I. and Mattoo, A. 2009. The crisis-resilience of services trade. World Bank Policy Research Working
Paper Series 4917.
Daudin, G., C. Rifflart and Schweisguth D. 2011. Who produces for whom in the world economy? Canadian
Journal of Economics 44(4): 1403-1437.
Eichengreen, B. and Irwin, D. A. 1998. The role of history in bilateral trade flows. In Jeffrey A. Frankel, ed., The
Regionalization of the World Economy, pp.33-62. Chicago, IL and London: University of Chicago Press.
Eichengreen, B. and Irwin, D. A. 1994. Trade blocs, currency blocs and the reorientation of world trade in the
1930s. Journal of International Economics 38: 1-24, July 1993, revised July 1994.
Feenstra, R. C. 2004. Chapter 5: Increasing returns and the gravity equation. In His Advanced International Trade:
Theory and Evidence, Princeton University Press.
Frankel, J. A. 1997. Regional Trading Blocs in the World Economic System. Washington D.C.: Institute forInternational Economics.
Fukao, K. and Ito, K. 2003. Foreign direct investment and services trade: the case of Japan. In T. Ito and A. O.
Krueger, eds., Trade in Services in the Asia Pacific Region, NBER East Asia Seminar on Economics (EASE),
Volume 11, pp.429-480. Chicago, IL and London: University of Chicago Press.

50

国際公共政策研究第 23 巻第１号

Gereffi, G. and Fernandez-Stark, K. 2011. Global Value Chain Analysis: A Primer. Center on Globalization,
Governance & Competitiveness (CGGC), Duke University, North Carolina, USA.
Greene, W. H. 2008. Econometric Analysis. New Jersey: Pearson Education, Inc.
Haveman, J. and Hummels, D. 2004. Alternative hypotheses and the volume of trade: The gravity equation andthe extent of specialization. Canadian Journal of Economics 37(1): 199-218.
Head, K. and Mayer, T. 2014. Gravity equations: Workhorse, toolkit, and cookbook. Handbook of International
Economics, Vol.4.
Heckman, J. 1979. Sample selection bias as a specification error. Econometrica 47: 153-61.
Helpman, E., Melitz, M. and Rubinstein, Y. 2008. Estimating trade ﬂows: Trading partners and trading volumes.
Quarterly Journal of Economics 123: 441-487.
Johnson, R. C. 2014. Five facts about value-added exports and implications for macroeconomics and trade research.
Journal of Economic Perspectives 28(2).
Johnson, R. C. and Noguera, G. 2017. A portrait of trade in value added over four decades. Review of Economics
and Statistics 99(5).
Kimura, F. and Lee, H.-H. 2006. The gravity equation in international trade in services. Review of World Economics
142(1): 92-121.
Koopman, R., Powers, W., Wang, Z. and Wei, S.-J. 2011. Give credit where credit is due: Tracing value added in
global value chains. NBER Working Papers Series 16426, September 2010, revised September 2011.
Linders, G. J. M. and de Groot, H. L. F. 2006. Estimation of the gravity equation in the presence of zero flows.
Tinbergen Institute Discussion Paper No. 06-072/3.
Miroudot, S. and Ragoussis, A. 2009. Vertical trade, trade costs and FDI. OECD Trade Policy Working Paper No.
89.
Miroudot, S., R. Lanz and Ragoussis, A. 2009. Trade in intermediate goods and services. OECD Trade Policy
Working Paper No. 93.
Santos Silva, J. M. C. and Tenreyro, S. 2006. The Log of Gravity. Review of Economics and Statistics 88(4): 641658.
Shepherd, B. 2012. The Gravity Model of International Trade: A User Guide. Thailand: United Nations Publication.
de Sousa, J. 2012. The currency union effect on trade is decreasing over time. Economics Letters 117(3): 917-920.
Timmer, M. P. (Ed.) 2012. The World Input-Output Database (WIOD): Contents, sources and methods. WIOD
Working Paper No. 10.
Tinbergen, J. 1962. Shaping the World Economy. New York: Twentieth Century Fund.