Trade Creation or Diversion? An ASEAN Perspective

A Panel Gravity Model Approach

MASTER THESIS WITHIN: Economics NUMBER OF CREDITS: 30 PROGRAMME OF STUDY: Economic Analysis AUTHOR: Nithin Gopalakrishnan

JÖNKÖPING May 2020

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Master Thesis in Economics

Title: Trade Creation or Diversion? An ASEAN Perspective

Authors: Nithin Gopalakrishnan

Tutor: Johan Klaesson & Emma Lappi

Date: 2020-05-18

Key terms: Bilateral trade, regional trade agreement, trade creation, trade diversion, ASEAN, AFTA

Abstract

The objective of this paper is to assess the bilateral exports from an origin to a destination, in the

context of countries belonging to the Association of South-East Asian Nations (ASEAN), and

whether or not the ASEAN Free Trade Agreement (AFTA) leads to trade creation or trade

diversion, or both. To study this, a panel gravity model is employed with 135 countries, from 2000-

2014, using a Poisson Pseudo-Maximum Likelihood method (PPML). To study the impact of

AFTA on trade creation/diversion, a set of three dummy variables are used, denoting whether the

origin country belongs to ASEAN, whether the destination country belongs to ASEAN and finally,

whether both origin and the destination countries belong to ASEAN. Along with AFTA, five other

Regional Trade Agreements (RTA) are also taken into account. The main finding of this paper is

that there is no pure trade creation nor pure trade diversion due to AFTA, but rather a significant

export trade creation, that is, ASEAN’s exports to the rest of the world is positive and significant.

Future policy implications could include measures to strengthen the regional economic cooperation

amongst the members of ASEAN.

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Table of Contents

1. Introduction ............................................................................................... 1

2. Background ................................................................................................ 4

2.1. RTA, Intra- and Extra- Trade Patterns...................................................................... 5

3. Theory and Hypothesis ............................................................................. 9

3.1. International Trade Theory ...................................................................................... 9

3.1.1. From the beginning to the 19th Century .............................................................. 10

3.1.2. The 20th Century: Customs Union Issue and H-O Theory ...................................... 11

3.1.3. Post Vinerian 20th Century: Gravity Model and New Trade Theory ........................ 12

3.1.4. The Empirical Gravity Model .................................................................................. 14

3.2. Previous Literature ................................................................................................. 16

4. Data and Methodology ........................................................................... 19

4.1. Variables ................................................................................................................. 20

4.2. Methodology .......................................................................................................... 24

5. Results and Discussions .......................................................................... 28

5.1. AFTA and other RTA Estimates ............................................................................... 28

5.2. The Traditional Gravity Estimates ............................................................................ 33

6. Conclusion ................................................................................................ 36

References List.............................................................................................. 37

Appendices .................................................................................................... 43

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Figures

Figure 1 - ASEAN’s top export partners in 2018 ................................................................. 5

Figure 2 - Trade-to-GDP ratios of ASEAN members (in %) ............................................. 43

Figure 3 – Relationship between trade and GDP of origin and destination........................ 45

Figure 4 – Relationship between trade and Geographical Proximity (Distance).................. 46

Tables

Table 1 - Intraregional trade concentration index, 2000-2018 ................................................... 4

Table 2 - List of variables used in the gravity model, and their sources and brief description .... 16

Table 3 - Descriptive Statistics of the time-variant variables ..................................................... 19

Table 4 - Regression results....................................................................................................... 23

Table 5 - Shifts in ASEAN’s top 10 export destinations…........................................................ 40

Table 6 - List of countries used in the sample, along with which FTA they belong to .............. 41

Table 7 - Correlation matrix of the variables ............................................................................ 42

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1. Introduction

This section covers the background of the study, and briefly touches upon the purpose of the study. The

background includes the reasons for studying international trade patterns in the light of Regional Trade

Agreements (RTA), and narrows the study to the Association of South East Asian Nations (ASEAN) trade

bloc – AFTA (ASEAN Free Trade Agreement). This is followed by the description of the purpose of the

research, and what the intended findings are.

An important international trade theorist, Jacob Viner, in his 1950 book The Customs Union

Issue first used the words “trade diversion (creation)” (TD/TC), about the impact of preferential

trade agreements in light of post-world war 2 international politics1. Viner’s (1950) book laid the

ground works of the contemporary debate over the effect of trade creation and trade diversion due

to regional trade agreements (RTA) such as the European Union, North American Free Trade

Agreement (NAFTA) and the Asia-Pacific Trade Agreement (APTA). This paper will compare and

analyse in brief the studies that were conducted on the post-establishment impact of RTAs.

Regional trade agreements (RTAs) are formed with the intention of expanding economic

cooperation between different countries, or in a geographical region, through eliminating barriers

to trade. Trading agreements enable countries to substitute local production of goods that they do

not have a comparative advantage with, with imports (Ricardo, 1817). This enabling is boosted by

RTAs in the sense that the additional tariffs and non-tariff barriers to trade that are associated with

a trading transaction is eliminated, and ensures smooth flow of goods into the trading countries in

an agreement (Medvedev, 2006). Coming closer in history, RTAs enable countries to exploit the

varieties of manufactured goods from different countries. Regional Trade Agreements support the

“love of variety” effect that is associated with a monopolistically competitive economy (Krugman,

1979).

Several authors have studied a post-establishment impact of an RTA, and how it has contributed

to increased FDI and economic growth (Vamvakidis, 1998; Hur & Park, 2012; Liu, 2015). In the

international trade context, several authors have used the gravity model in order to study the post-

1 Note that the first Customs Union in the world was formed in 1910 in Southern Africa. This explains why post-

colonial or common colonial history may have an effect on trade levels today, in the gravity equation.

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establishment impact of RTAs on bilateral trade itself (Endoh, 1999; Soloaga & Winters, 2001;

Egger, 2002; Carrere, 2006; Kien, 2009). These studies are about the trade creation and trade

diversion effects of establishment of an RTA. Trade creation translates to an increased welfare for

both the members of the RTA and the rest of the world. Trade diversion however, is an increased

welfare amongst the member countries of the RTA, often at the expense of the rest of the world.

There have been very few numbers of studies (Carrere, 2006; Kien, 2009) on the trade

creation/diversion effects of AFTA in a more recent time frame.

ASEAN as a region has a geopolitical and strategic importance. It contains one of the most

important hub (Singapore) through which a large share of world trade passes. ASEAN consists of

diverse economies in terms of development, and this directly characterizes which region each

member trades the most with. The developed ASEAN members trade more with the EU, USA,

Japan, etc. and the least developed ASEAN members (Cambodia, Laos, Myanmar, etc.) trade with

China, and the geopolitical importance of China has started to dictate trade flows in and out of

ASEAN. ASEAN is a fast-growing economy, and in the coming decades, is expected to be the 4th

largest economy (by 2030) (United Nations, 2020). In 2013, the ASEAN-5 (Indonesia, Malaysia,

Singapore, Philippines and Thailand) attracted more FDI ($128 billion) than China ($117 billion)

(IMF DoTS, 2020). Chinese companies have since been relocating production facilities in ASEAN

countries. China, as the primary competitor of the United States of America in terms of foreign

geopolitical and economic importance, sees ASEAN as its biggest enabler of trade, as a regional

partner, and economic stronghold neighbour.

Chart 1 and Table 5 in Appendix A2 show that China is the biggest trading partner for ASEAN

in 2018. This is why ASEAN plays an important role in world trade, as a growing economic region.

This study aims to study ASEAN’s trade, and whether or not ASEAN Free Trade Agreement leads

to trade creation or diversion. As mentioned above, the growing ASEAN importance in Asia and

the world is expected to translate into increase in welfare for the members of ASEAN, as well as

the rest of the world. This study however, will not examine who in “rest of the world” this increase

in “welfare” attributes to.

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Table 1 shows the intensity of intraregional trade between the members of ASEAN, and other

RTAs. With ASEAN’s lingering pattern of intraregional trade in the most recent decade, it is

interesting to see the effects of AFTA on a more recent time period, for a panel of countries. This

study adopts a panel RTA analysis with 135 countries from the time period of 2000 to 2014, with

a spotlight on the Asia-Pacific Free Trade Agreement (AFTA).

Thereby, the objective of this paper is to assess whether AFTA has led to trade creation (TC) or

trade diversion (TD), or both, in terms of exports. This could be pure trade creation, pure trade

diversion or both creation and diversion. With the aim in mind, a gravity equation is estimated

using the PPML method in order to find out the relative importance and influence of regional

trade agreements (preferential trade agreements) in explaining bilateral trade, along with the

traditional gravity equation determinants such as the origin (exporter) and destination (importer)

incomes, geographical proximities, origin and destination per capita incomes, common borders

and languages, etc. The final finding is the expected trade creation (TC) or trade diversion (TD)

due to AFTA. There is pure trade creation if the intra-AFTA trade and the AFTA’s exports to the

rest of the world are positive and significant. There is pure trade diversion when the intra-AFTA

trade is positive and significant, and AFTA exports to the rest of the world is negative significant;

and this negative value is greater than the positive intra-AFTA estimate. There is only export trade

creation (diversion), if the intra-AFTA trade is not significantly different from zero, and there is

an increase (decrease) in AFTA exports to the rest of the world.

The following sections are as follows: introduction – background, free trade agreements and its

importance, theoretical anchoring – international trade theories and the connection to the gravity

model, previous literature and the gravity model – summaries of literature on regional trade

agreements and determinants of trade flows, “theoretical” and econometric foundations of the

gravity model, data and methodology – data and variables, motivating the methodology and the

study-specific gravity equation, results and discussions -, and conclusions.

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2. Background

One of the most important international trade blocs and economic integration measures in the

continent of Asia, is the formation of ASEAN. Since the beginning of the establishment of the

ASEAN Free Trade Area (AFTA) in 1992 in Singapore with the aim to promote local

manufacturing and trade in the ASEAN countries (the ASEAN group consisted of 6 members

when AFTA was signed – Singapore, Indonesia, Malaysia, Brunei, Philippines and Thailand), the

members of ASEAN agreed to eliminate tariffs and non-tariff barriers to bilateral trade. The

primary goal of AFTA was to increase the region’s competitive edge. By 1999, all the 10 countries

of today’s ASEAN were part of AFTA (Vietnam joined in 1995, Myanmar and Laos in 1997, and

Cambodia in 1999)2. Perhaps the most interesting feature of the ASEAN group is that there is no

“leader”, and the decision-making roles and powers are evenly split among the members, making

it a resilient economic cooperation (ASEAN Stats, 2020). In today’s globalized world, a common

feature among developed (high-income) economies is that the smaller the country is, the more it

indulges in external trade. Singapore and Malaysia are good examples.

By trade volume of blocs in the world, ASEAN’s total trade in 2017 was worth $2.57 trillion

(ASEAN Stats, 2020), only behind NAFTA ($22 trillion), European Union ($16 trillion) and the

Southern Common Market in South America (MERCOSUR3) ($2.9 trillion), making ASEAN

the 4th largest trading bloc in the world (IMF-DoTS, 2020)4. ASEAN has also led the formation

of the forum APT (ASEAN Plus Three) which includes all ASEAN members, China, Japan and

Republic of Korea. This integrated effort of economic cooperation strengthened especially post the

Asian Financial Crisis (1997 – 1999).

The gravity model, which uses the ideology that the importer and exporter country trade more

with each other based positively on their economic sizes and negatively on their geographic distance

2 Brunei Darussalam and Viet Nam are the official names respectively, but referred as Brunei & Vietnam in this

paper. Brunei is the only country with a sultanate, and is also one of the safest South East Asian countries,

thanks to heavy infrastructural spending due to the preferential trade agreement with ASEAN members (Kien,

2009). 3 MERCOSUR, NAFTA & APEC – Southern Common Market (South American trade bloc), North American

Free Trade Agreement and the Asia-Pacific Economic Cooperation, respectively. 4 IMF’s Direction of Trade Statistics is among the most comprehensive databases for bilateral trade. Other data

used for gravity analysis are from United Nations Comtrade, WITS-World Bank, Correlates of War.

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(Tinbergen, 1962), can also be used to assess the increase or decrease in bilateral trade based on

formation of an RTA (Egger, 2002). Several studies have been done as an ex-post assessment of

trade creation/diversion effects of RTAs (Frankel, 1997; Soloaga & Winters, 2001; Carrere, 2006).

Carrere (2006) summarizes the impact of an RTA on the bilateral trade flows as follows: trade

creation due to an RTA must increase intra-regional (example, intra-AFTA) trade and the imports

from the rest of the world remain the same; and under trade diversion, the increase in intra-regional

trade is offset by a larger decrease in imports from the rest of the world. Celine Carrere (2006)

advocates the appropriate introduction of dummy variables in the gravity model in order to

account for the effects of trade creation and diversion, which will be covered later in this study.

2.1. RTA, Intra- and Extra- Trade Patterns

Table 1 shows the intraregional trade concentration index of select RTAs, over time. The meaning

of the index ratio is given below.

Table 1: Intraregional trade concentration index, 2000-2018

Source: Author’s Calculations based on data derived from UNCTAD5

This index shows a more meaningful version of just the share of each region’s intra-trade with

respect to the region’s total trade. The intraregional trade concentration index is calculated as the

5 United Nations Conference on Trade and Development (UNCTAD) data for each region’s intraregional

imports and exports are added to get total intra-trade, and the basic share of intra-regional trade is derived using

total trade of region; this data is then combined with the regional trade’s share of total world’s trade. The latter

becomes the denominator for the intensity/concentration index. Concentration of each region’s intra-trade =

(𝑥𝑖𝑡/𝑋𝑖𝑡)/(𝑅𝑡

𝑊𝑡), where 𝑥𝑖𝑡 is the intraregional trade of RTA, 𝑋𝑖𝑡 is the total trade of RTA;

𝑅𝑡

𝑊𝑡 is the share of the

RTA’s trade with respect to total world trade.

Year ---> 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018

Asia-Pacific Trade Agreement (APTA) 1.09 1.09 1.05 0.92 0.90 0.82 0.72 0.66 0.65 0.70

Association of Southeast Asian Nations 3.44 3.62 3.83 3.78 4.09 3.55 3.74 3.69 3.28 3.17

ASEAN Plus Three 1.63 1.70 1.64 1.51 1.53 1.40 1.43 1.36 1.26 1.32

Andean Community 19.50 24.60 20.52 14.76 12.95 12.15 9.33 10.73 13.69 11.91

Common Market for Southern and Eastern Africa (COMESA)

11.05 13.13 9.09 8.31 7.12 8.39 9.57 16.35 18.35 17.19

European Free Trade Agreement (EFTA) 0.27 0.24 0.24 0.25 0.33 0.29 0.24 0.24 0.20 0.22

European Union 1.74 1.57 1.61 1.73 1.73 1.84 1.88 1.81 1.74 1.77

Mercado Común del Sur (MERCOSUR) 9.76 6.28 5.99 6.09 5.92 6.45 5.66 6.48 7.35 6.74

North American Free Trade Agreement 2.94 3.30 3.92 3.92 3.93 3.80 3.79 3.83 3.64 3.75

South Asian Association for Regional Cooperation (SAARC)

4.54 4.69 5.76 4.81 4.42 3.36 2.96 3.30 3.37 3.84

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ratio of each region’s intra-trade as a share of its total trade, with respect to that region’s share of

total world trade. If a member of an RTA has a propensity to trade with its fellow member the

same as it does with any non-member, the ratio is close to 1. If there is higher propensity to trade

with its own member than with a non-member, the ratio exceeds 1 and exhibits higher values

(UNCTAD, 2020). This ratio is better in explaining intraregional trade than just the share of intra-

trade with whole trade of a region in a crucial way: the simple share of intra-trade may be skewed

by the number of members in an RTA, as well as the size of the trading member in an RTA. The

intraregional trade concentration however, shows the actual intensity of intra-trade within a region,

adjusted for the whole region’s size in international trade.

Table 1 is relevant to this study because, it shows the trend of intraregional trade concentration,

and tells us if an RTA is intra- trade oriented or extra- trade oriented, and this should reflect in the

regression results later on, with the dummy variable indicating both the origin and the destination

countries belonging to the RTA. If the intraregional trade concentration index is greater than 1,

indicating a higher intensity for the members of an RTA to trade among themselves, the dummy

variable indicating both the origin and the destination belonging in the RTA will be positive and

significant. For example, the ratio for COMESA in 2014 is 16.35, indicating an intra-overtrading

among the members of COMESA, and this reflects in the regression results through the dummy

variable, where both the origin and destination belonging to COMESA increases trade by 182%

than otherwise, and the COMESA’s exports to the rest of the world show a negative significant

estimate. The same reflection can be expected for all the other RTAs used in the regression analysis

as well, including AFTA, for the ASEAN countries.

The Asia-Pacific Trade Agreement (APTA) and the European Free Trade Agreement (EFTA) are

the only RTAs with concentration ratios lower than 1. The Andean Community and the Common

Market for Eastern and Southern Africa (COMESA) exhibit extremely high values, indicating an

“intense” intraregional trading relationship. ASEAN exhibits a moderately intense value. These

values are attributed to several factors influencing the intraregional trade intensities. Similar

income, for example, is an important factor. The higher the income of an RTA, the lesser they are

inclined to have higher intra- intensities. Similar incomes call for similar demand in the type of

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goods, for example, COMESA (Afesorgbor, 2016) and hence higher intraregional trade. This is

comparable with Krugman’s (1979) new trade theory of higher trade between similar income

economies, due to translated similar demand for goods. The varying intensity of ASEAN’s

intraregional trade is of particular interest to this paper. Even if ASEAN has an index value of 3.69,

which is greater than 1, the time trend shows no increase or decrease.

Another disadvantage of the table is that, it does not provide the same concentration index for

extra-trade. The reason for not including extra-regional concentration index is due to the fact that

the study is about trade creation and diversion based on post-establishment of an RTA, and the

increase in intraregional trade is of primary importance, than increase in the members of the RTA’s

exports/imports to the rest of the world. Contextually, in light of recent efforts to seal a trade pact

named the “Regional Comprehensive Economic Partnership” (RCEP) which is set to consist of all

10 ASEAN members, Australia, China, India, Japan, New Zealand and South Korea. The gravity

model is able to analyse the “anticipation effect” (Carrere, 2006) of a trade pact using historical

trade patterns. The “anticipation effect” is the fact that formation of some RTAs can be predicted

based on existing trade patterns and volume of trade. ASEAN as a whole merging as RCEP could

be a result of existing high trade with the other 5 members for ASEAN, and vice versa. There is

pure trade diversion when and if, there is an increase in intra-regional trade, but it is entirely offset

by a decrease in exports to the rest of the world. There is trade creation, if there is an increase in

intraregional exports as well as an unchanged or increased export to rest of the world. It is worth

to note that with ASEAN, the incomes and demands from outside ASEAN have a big impact on

ASEAN’s exports (example, ASEAN plus Three – China, Japan and South Korea) (Elliott &

Ikemoto, 2004). This is a possible indication to the insignificant intra-regional trade variable

coefficient taken in the analysis.

Formation of an RTA may be due to existing high or low levels of trade between a group of

countries. Countries with political similarities enter into an RTA and thereby eliminate tariffs

amongst them, promoting intra-trade, for example. The AFTA today shares 6.4% of the total

world’s trade (ASEAN, 2020). Baldwin et al. (2011) predicted that the increase in the number of

RTAs in the world in the future will lead to multilateral liberalization, and hence further

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fragmentation of production in all the countries. One other motivation for testing whether intra-

trade in ASEAN due to AFTA has been significant or not, could stem from findings of Baldwin &

Taglioni (2006) and Manchin & Pelkmans-Balaoing (2007). They find that since Asia’s

manufacturing sector was led by trade of parts and components in the 2000s (it is still the highest

exported good of Singapore – 2018), they found using sectoral disaggregated data, that the intra-

regional trade among the members of ASEAN was largely led by parts and components trade, with

further industrialization in the ASEAN countries. However, the administrative cost of facilitating

a transaction amongst the members is very high in ASEAN (Baldwin & Taglioni, 2006), and hence

the cost of complying to administrative rules make underutilising the RTA more preferable. This,

however will not be the focus of this study, as the focus is more on economic patterns of regional

trade agreements.

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3. Theory and Hypothesis

This section is devoted to a groundwork understanding of why two economic entities trade with each other.

This section covers how the theories of international trade have progressed since the beginning of economic

literature, and how the empirical gravity equation sums up the trade theories. Among the two sections in

this chapter, the first section covers the evolution of the international trade theory through time. The chapter

concludes with the previous research made regarding the gravity model and Regional Trade Agreements,

and subsequently, providing motivation for the research hypothesis.

3.1. International Trade Theory

International trade theory’s evolution is not so complicated to be explained meticulously, but it

isn’t simple either. But no matter what it is, it is interesting. Post mercantilism came the classical

international trade theory, by Adam Smith, in 1776, and David Ricardo, in 1817 – with absolute

and comparative advantage theories, respectively. John Stuart Mill in 1848 was the next in line to

make another breakthrough, by understanding the demand side of international trade, and how it

is possible for gains from trade to be unbalanced. Then came the proposal that trade is based on

factor endowments, in 1919, by Eli Heckscher, and further developed by his student Bertil Ohlin

in 1933. Assuming an economy is more labour intensive than capital intensive, that economy

would export only those goods which are labour intensive. Perhaps the key trade theorist pertaining

to this study, is Jacob Viner. Viner in his 1950s book Customs Union Issue first discussed the

benefits of free trade agreements post world war 2, in terms of welfare for the trading economies.

Going by the timeline, the gravity model was proposed in 1962 by Jan Tinbergen, as an empirical

method to test international trade flows and the determinants of it. A new trade theory, aiding this

successful empirical invention is the New Trade Theory, by Paul Krugman in 1979. Similar

income economies trade more with each other, as well as facilitating intra-industry trade, due to

monopolistic competition. This seminal work is still empirically valid, and thus began the rise of

modern geographical economics. The gravity model is still empirically successful in explaining

bilateral trade flows.

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3.1.1. From the beginning to the 19th Century

The basis for international trade theory has been existing since the times of proto-industrialization

period (16th to 18th century), when mercantilism was prominent. Using protectionism, an

economy must acquire wealth through exports, which must be larger than the imports. The first

classical development regarding the international trade theory came from the book The Wealth of

Nations, by Adam Smith (1776), who argues that increase in the output can be possible through

division of labour and specialization, and international trade was one of the special cases of the

specialization. A country, in a larger global setting with scarce resources and unlimited demand,

would produce only that product which can be produced with the lowest absolute cost. These

goods would then be traded with other countries for a product with lowest absolute cost to them,

and so on (Blonigen & Wilson, 2013). Relative cost of production is the next major development

in this theory given by David Ricardo in 1817. Contrary to mercantilism, where international

trade is a zero-sum game, and the only way a nation’s welfare is gained, is at the loss of another’s,

Ricardo’s (1817) comparative advantage theory gave that two countries can engage in mutually

beneficial trade with each other.

The comparative advantage model also introduced the concept of the direction of trade – who

exports and who imports, and what do they export/import. In his two-country, two-goods theory,

the country with lower opportunity cost of production of a good, relative to the other country, is

the country with comparative advantage of production of the said good. The comparative

advantage theory has now been extended in its usage to a multi-product trade theory as well as

multi-product and multi-county trade and production theory (Dornbusch, Fisher & Samuelson,

1977; Bernard et al., 2003). Once it was established, by Adam Smith in his absolute advantage

theory, that discouraging imports and promoting exports, like under the mercantilist ideology, is

not the most optimal solution for welfare in an economy, Ricardo’s (1817) comparative advantage

models paved the way for many extensions, that would develop and take further the trade and

production theories. The first of demand-based trade theories came from John Stuart Mill. Mill

(1848) stated that it is possible for the gains from trade to be in favour of one country more than

the other. Mill (1848) stated that the terms of trade between two countries will depend on the

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reciprocal demand for the goods considered, in their exchange ratio. So, these terms could change

based on the changes in the intensity of demand from each country, and any potential barriers to

trade. Mill’s theory diverted from Smith and Ricardo’s theories, in terms of international trade

being a positive-sum game, which diverted from the mercantilism theory that trade is a zero-sum

game. In Mill’s theory, international trade can be a fixed-sum-game, where the gains from trade

can be divided in favour of one country, in a two-country trade’s exchange ratio.

3.1.2. The 20th century: The Customs Union Issue and the H-O theory

The terms trade creation and trade diversion are closely associated to Jacob Viner (1950) in his

seminal book, The Customs Union Issue, in which he summarizes the welfare effects of new

members joining a customs union/trade agreement and non-members of the union. This effect is

particularly well seen doing a pre- and post- analysis of trade and welfare for a new member, and

comparing those effects with a non-member. Trade creation and diversion is studied using

comparative advantage (Ricardo, 1817). A country with comparative advantage over a product,

over other countries, may still suffer welfare losses due to a union formed by some of the inefficient

producers. This is due to that fact that any previous tariffs, are now reduced amongst the members,

and the efficient producer’s goods are no longer demanded. This is called trade diversion. Yeats

(1998) found in his study about MERCOSUR that the growth in intraregional trade in

MERCOSUR was for those products for which the members did not have a comparative

advantage. Moreover, the discriminatory tariffs of MERCOSUR against non-members further

proves trade diversion within the RTA.

Trade creation due to an RTA, results in an increase in welfare for the members of the RTA as well

as the rest of the world, where trade diversion results in an increase in the welfare of the members

of the RTA, but a reduction in the welfare of the rest of the world. Pure trade creation, is when

the inefficient producer in the union eliminates the tariffs to import a product from a more efficient

producer, thereby decreasing the average price of the product for its citizens, and thereby increasing

demand and consumption. Trade creation is a long-term welfare result of formation of a union,

for its members. However, many studies including RTAs as an explanation to bilateral trade omit

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the effects of trade creation and trade diversion, and focus only on the intra-trade levels of members

belonging to an RTA (Batra, 2006). Moving further on the theoretical foundation of international

trade, the Heckscher-Ohlin trade theory would now dominate it for decades to come.

The Heckscher-Ohlin trade theory, which would become a pathbreaking international trade

theory, which states that two countries will trade with each other based on their relative factor

endowments. Ohlin (1967) stated that given labour and capital, a country with relatively higher

endowment in capital must produce capital-intensive goods and then export them, in order to gain

that said comparative advantage in the Ricardo model. Ohlin’s professor, Heckscher (1919),

developed the factor endowments theory initially, which was then revised by his student. The H-

O theory is still considered to be empirically accurate, apart from the so-called “undermining”

research by Leontief (1953), who proved empirically, that the United States, being the most capital

abundant country, exported more labour-intensive goods, contradicting the H-O model. Leamer

(1980), however, gave an answer to the “Leontief Paradox”, by taking a multi-sector model of the

same analysis as Leontief (1953) using a model developed by Vanek (1968), and proved that the

factor-endowments theory worked. The H-O model, still remains to be empirically accurate and

valid, in explaining the causes of comparative advantage (Maneschi, 1998). One noteworthy

extension to the H-O model is perhaps the Stolper-Samuelson model. Stolper & Samuelson (1941)

and Samuelson (1948) took further Ohlin’s assertion that free trade will lead to factor price

equalization. This simply put, means that the trade in that good for which the resource is abundant,

will realize an increase in income, due to the increased price of the product, and conversely, the

resources for the product with the comparative disadvantage, will realize losses. So, in the long-

run, regardless of industry, there will be losses to some people in the domestic economy

(Samuelson, 1948).

3.1.3. Post Vinerian 20th Century: Gravity Model and New Trade Theory

Post the findings by Leamer (1980), there has only been little advancement in the proof of existence

of the factor endowments theory in empirical studies (Trefler, 1995). Parallelly in this time period,

the solution to international trade in an empirical approach would be developed: the gravity

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equation, proposed by Tinbergen (1962). The gravity equation stated that international trade

between two countries are positively determined by their economic sizes, and inversely related to

their geographical distance. The bigger and closer the economies are, the higher they trade with

each other. The issue for trade scholars in time periods decades beyond the introduction of the

gravity equation, was that there was no theoretical linkage to the gravity equation (Blonigen &

Wilson, 2013). The next big development in the trade theory is the new trade theory. Researchers

found in 1980s, that most of the world trade is limited to developed countries, and moreover,

developed countries with similar factor endowments: intra-industry trade. This is the basis for Paul

Krugman’s (1979) new trade theory. The two main features of Krugman’s theory are that the

global competition setting is monopolistic, and that there exists constant elasticity of substitution

(CES). This is the basis of the “love of variety” effect, where the production and trade of

monopolistically competitive countries trade in a single-good setting, different varieties of the same

good. The love of variety effect explained exactly what the comparative advantage models could

not: international trade between similar-income countries with intra-industry trade dominating.

The gravity model’s theoretical foundations by now, have been established through the works of

Anderson (1979), Krugman (1979), Helpman & Krugman (1985) and Bergstrand (1985; 1990).

To explain the basic independent variables in the gravity model, the origin and destination

countries’ GDPs explain the production & absorption capacities respectively, and the geographical

distance represents the transport costs associated with a bilateral transaction. The basic theoretical

model proposed by Anderson (1979) based on expenditure systems provided a base for further

economists to confirm the theoretical validity of the gravity model. The limitation for Anderson’s

(1979) basic structure is that the product differentiation was limited to the number of countries in

a hypothetical sample. Krugman (1979), Helpman & Krugman (1985) relax this assumption by

introducing the gravity equation to monopolistic competition. They used the framework of Dixit

& Stiglitz (1977) to provide a basis for international trade in a monopolistic competition6, and

6 The gravity model in the past has been tested for other market structures as well. Deardorff (1998) derived a

gravity equation from perfect competition structure, Feenstra et al. (1998) derived a gravity equation from a

reciprocal dumping model of trade with homogeneity of goods. The monopolistic competition and increasing

returns to scale structure however, is necessary to explain trade in industrialized economies (Evenett & Keller,

14

with increasing returns to scale. The amount of trade will now depend on the size of two economies

(Tinbergen, 1962) in a trading transaction, and endogenously determined by production costs,

and the demand for variety and of course, factor endowments (Heckscher, 1919; Ohlin, 1967).

This is where Bergstrand (1989) developed the gravity model to explain the new trade theory. The

gravity model explained the new trade theory better than the Ricardian comparative advantage

models. Deardorff (1998) however, attempted to explain the comparative advantage models using

the gravity-based empirical specification and found out that it can be specified. In this sense, these

are the theoretical foundations of the gravity model. The gravity model of international trade

started as an econometric method, and with successful empirical power, researchers successfully

tied it to trade theories as well (Anderson, 1979; Bergstrand, 1985). In the most recent decades,

researchers have been forced to look back at the comparative advantage models (Blonigen &

Wilson, 2013), since there is a growing tendency for firms in developed countries to exploit the

low wages by so-called “offshoring”. Offshoring firms belonging to developed countries, use the

low wage situation in the less-developed countries for unskilled tasks (Bernard, Jensen & Schott,

2006; Hummels et al., 2011). The most recent literature focuses on the effects of intensive margins

and extensive margins. Intensive margins are when there are changes in trade due to changes in the

intensity of an existing trade connection (formation of a new FTA), and an extensive margin is the

change in trade due to making new trade connections (Hummels & Klenow, 2005).

3.1.4. The Empirical Gravity Model

The gravity model can be easily used to test the impact of trade creation or trade diversion. Zipf

(1946), in his chapter “the P1P2/D hypothesis”, first proposed this idea for intercity movement of

people in the United States, where P1 and P2 were populations of two regions, and D being the

shortest possible distance between them. Tinbergen (1962) and Poyhonen (1963) introduced this

in terms of international trade: where the volume of trade (exports or imports) will depend on the

product of the economic sizes of the two countries as a ratio of the distance between the two

1998). After the theoretical establishments of the gravity model, researchers have now moved on to empirical

assessments of global trade events.

15

countries. Greater distance implies greater transport costs, and thus lower volume of bilateral trade.

The traditional gravity equation can be simply put:

𝑋𝑖𝑗 = 𝛼0𝑌𝑖𝑌𝑗

𝐷𝑖𝑗 ; 𝛼0 is the constant term, the two countries’ incomes/GDP are represented by 𝑌𝑖

and 𝑌𝑗 for i and j respectively, and 𝐷𝑖𝑗 being the distance between them. 𝑋𝑖𝑗 is the volume of trade

between i and j, or trade flow from i to j (see Appendix B1). More than the income sizes and the

distances alone, Bergstrand (1990), Matyas (1997) and Anderson and van Wincoop (2003) have

found several factors affecting bilateral trade and thus, kept refining the gravity equation’s

“standard” structure. The inclusion of per capita incomes along with GDP (Bergstrand, 1985) was

among the earliest appendages. Anderson and van Wincoop (2003) bring in the theory of product

specialization into the gravity equation, along with the idea of “multilateral resistance terms”

(MRTs) such as the remoteness index, affecting the trade flow between two countries. Multilateral

Resistance Terms (MRTs) in a gravity equation, can be common language dummies, common

religion dummies, political corruption indices or most famously, membership in the relevant

multilateral trade agreement, etc. Anderson and van Wincoop (2003) stated that excluding MRTs

which are positively associated with trade costs between two countries that affect trade will lead to

mis/incomplete interpretation of bilateral trade.

Viner (1950) proposed not a trade theory, but more of an idea. The implications of the formation

of a regional trade agreement does have theoretical linkages to it. The formation of an RTA for

example, can give country A the boost it requires to engage in trade with another country, B,

belonging to the RTA, in a product for which country A has lesser comparative advantage over.

This leaves country C, an existing trading partner of country A in that product, out of the picture,

leading to lesser trade for country A with country C, and more with B. This is an example of trade

diversion. The members of the RTA (countries A and B) now trade more with each other at the

expense of the rest of the world (country C).

Ricardo’s (1817) comparative advantage theories can indeed explain trade creation or diversion in

light of formation of an RTA. Another theoretical linkage to the formation of an RTA can be

derived from the factor endowments theory. Right from the formation of an RTA, to its

16

implications on trade creation and diversion, factor endowments could be key in explaining the

mechanisms behind the RTA formation. Countries that form an RTA together may be endowed

with similar factor proportions, and thus boosting trade amongst themselves (for example, the

Common Market for Southern and Eastern Africa, or COMESA). The case of NAFTA, with USA,

Canada and Mexico, show that the establishment of NAFTA has led to increased industrialization,

and more FDI and infrastructural developments in Mexico, the least capital intensive economy in

NAFTA, and leads to factor price equalization, as in Ricardo (1817) and Heckscher (1919) &

Ohlin (1967) (Fukao, Ishido & Ito, 2003; Tang, 2005).

Trade creation and diversion due to the formation of an RTA, can be explained in the new trade

theory, given by Krugman (1979) as well. In a monopolistic competition market structure, the

choice of countries forming an RTA may be dictated by similar incomes, and demand structures,

as well as high intra-industry trade, characterized by the “love of variety” effect. ASEAN countries

for example, saw a surge in trade of parts of components within the region after the formation of

AFTA, led by Singapore and Malaysia.

3.2. Previous Literature

Despite criticism in the beginning times regarding the theory as just a theory, gravity models gained

widespread attention and popularity in no time. It became the most chosen econometric method

to test for bilateral trade, immigration, trade flows of different commodities, and trade under

different circumstances, etc. (Deardorff, 1984). Frankel et al. (1997), acclaimed the success of the

gravity models to the fact that economists have finally understood that countries are geographical,

physical entities on the globe. Aitken (1973) and Endoh (1999) are one of the first researchers to

study the trade liberalization effects of RTAs on members and non-members. Considering the

study relevant to this paper, regional trade agreements are an important aspect studied throughout

history, in the gravity models. Ex-post assessment of Regional Trade Agreements can be captured

in a gravity equation using the correct specification of dummy variables (Carrere, 2006). Egger

(2002), Soloaga & Winters (2001), Carrere (2006) have contributed to the correct specification

dummy variables to capture the full effect of RTAs in the gravity model. Kien (2009) studies the

17

effects of ASEAN in an earlier time period, and found that the intraregional trade is positive and

significant for ASEAN. The time period Kien considers is 1988-2002. Trade creation and diversion

effects has been in study for a long time. Considering recent history, Endoh (1999) studied the

effects of TC and TD for 35 years from 1960-1994 for different European RTAs using OLS in

panel data. Breuss & Egger (1999) study the “trade potentials”7 for East and Western Europe using

cross sectional data. Matyas (1997) and Egger (2002) has proven that estimating the effects of

RTAs using a panel data is more appropriate. Studies similar to trade creation and diversion include

the effect of a country’s entry into a political or trade agreement on trade, for example, Greece’s

entry into the EU (Kepaptsoglou, Karlaftis & Tsamboulas, 2010).

One of the inspirational papers were written by Rose, Lockwood & Quah (2000), considering all

the RTAs and their effects on bilateral trade, using data for 186 countries. A common feature

among all the previous studies on TC and TD are the inclusion of other RTAs other than the

considered, in order to account for interdependencies in a “general equilibrium” setting, Fukao,

Ishido & Ito (2003), study the effects of NAFTA by including only the members of NAFTA in

the gravity specification. Later researchers found possible bias in this kind of estimation). Matyas

(1997), Egger (2002) and Glick & Rose (2001) contribute to the econometric specification of TC

and TD analysis. Similar to trade creation and diversion, Rojid (2006) studies the impact of

COMESA agreement on its members, intending to prove an “intra-overtrading” pattern.

All the papers stressed on the importance of a panel data, since the evolution of trade under an

RTA is dynamic in nature. Martinez-Zarzoso & Nowak-Lehmann (2003) & Garcia, Pabsdorf &

Herrera (2013) study the trade creation and diversion impact due to MERCOSUR. Roberts

(2004) studies the effect of China-ASEAN trade using cross-sectional data, and found that

ASEAN’s exports to the rest of the world is positive and significant, and led greatly by exports to

China. Other studies regarding the ASEAN members are Sohn (2005) who studies the effects of

Korea-ASEAN trade partnership, using a cross-sectional analysis, and finds that the ASEAN Plus

Three (including Korea) leads to significant intra-trade. Tang (2005) studies the effects of NAFTA

7 “Trade potential” is an econometric calculation post analysis, to find out the ratio of estimated value of trade

with actual trade, to see if there is under-trading/over-trading. The term “potential” is still under debate whether

or not it is the same as calculating overtrading/under-trading.

18

and ASEAN using a panel of 21 countries for 10 years, and finds that the impact of ASEAN’s

exports to NAFTA is greater than the intra-ASEAN trade, and Kien (2009) studies the effect of

ASEAN membership in a panel of 39 countries, and finds positive significant intra-ASEAN trade.

The common lacking aspect of all these previous studies is that the size of the panel is either too

small, or indicting cherry-picked sample panels, or a cross-sectional study. This study overcomes

this problem by taking a large panel of 135 for a reasonably small time period of 15 years, the most

recent time period, and includes 5 other RTAs apart from ASEAN, in an attempt to reasonably

provide a comparison with that of the ASEAN’s regional trading patterns, and with the previous

literature.

The inclusion of dummy variables with the appropriate model selection, allows us to capture the

intra-bloc trading effects, as well as whether or not the RTA has led to trade creation or trade

diversion. The main impact of an RTA, according to Viner (1950) include implications of welfare

on the members of the RTA, and the rest of the world. In the next chapter, the extensive meaning

of the RTA dummy variables used is given. The intraregional trade concentrations as per table 1

show a very mixed image of the time trend of ASEAN’s intra-trade. Nevertheless, the intra-trade

variable of AFTA is expected to be positive and significant. Given that the intra-AFTA variable is

greater than zero, the research question of the paper would then be: does this intra-trade between

ASEAN members (denoted by a dummy variable which takes value 1, if both origin and

destination country in a bilateral trade observation belongs to AFTA, zero otherwise) contribute

to trade creation or trade diversion?

This will depend on the signs of the other dummy variable for an RTA: the members’ exports to

rest of the world (denoted by the dummy which takes value 1 if the origin country is part of the

RTA, zero otherwise).

19

4. Data and Methodology

This section is devoted to the description of the variables used in the model, along with the methodology

used in the study, and the motivations behind the choice of method. The section concludes with the

econometric hypothesis of the study.

The main variables used in the gravity model according to empirical studies, are used here as well

(GDPs, per capita GDPs, bilateral distance, common language, etc.). The main data source is

Head, Mayer & Ries’s (2010) bilateral directional trade flows data from CEPII, and Head &

Mayer’s (2014) gravity dataset, containing the common gravity variables, also from CEPII. CEPII

provides a comprehensive gravity database, containing data from 2000 until 2014. The variables

list table given below explains the meaning behind each of the variables. This table is a brief

description of the basic structural gravity equation’s variables, so it does not contain the Regional

Trade Agreements dummy variables used in this model. The dataset consists of bilateral trade from

an origin country to a destination country, from 2000-2014, for 135 countries, and 18,090

country-pairs and 235,324 observations, (the actual number should be 135*134*15 = 271,350

observations. This is due to many missing values in the data).

Table 2: Variables List, excluding the dummy variables assigned for the RTAs

Note: o represents ‘origin’ and d represents ‘destination’ countries and ‘t’ represents time period. TRADEod is then, trade flow

(exports) from o to d. Apart from Contig, Comlang_off and Colony, all the variables in the model are logged.

8 The remoteness indexes are calculated as GDP weighted average distances for the origin and the destination.

For example, the origin (exporter)’s remoteness index = ∑(𝐷𝑖𝑠𝑡𝑜𝑑)

/(𝐺𝐷𝑃𝑑

𝐺𝐷𝑃𝑤𝑜𝑟𝑙𝑑)𝑑 , where the

𝐺𝐷𝑃𝑑

𝐺𝐷𝑃𝑤𝑜𝑟𝑙𝑑 is the share of the

destination (importer)’s GDP with respect to the world’s GDP. The remoteness index for the importer is

calculated in a similar way.

Dependent Variable Description Unit

TRADEod Exports directional from origin to destination US$ million

Independent Variables

GDPot GDP of origin country at time t US$

GDPdt GDP of destination country at time t US$

GDPPCot GDP per capita of origin country at time t US$

GDPPCcdt GDP per capita of destination country at time t US$

DISTod Great Circle Distance between origin and destination’s capital cities Kilometers

REMOTEot8 “Remoteness index” of origin at time t Weighted index

REMOTEdt “Remoteness index” of destination at time t Weighted Index

CONTIGod Equal to 1 if origin and destination share a border Dummy

COMLANG_OFFod Equal to 1 if origin and destination have common official language Dummy

COLONYod Equal to 1 if origin and destination ever had a colonial relationship Dummy

20

The regional trade agreements considered in this model are APTA (Asia-Pacific Trade Agreement),

AFTA (ASEAN Free Trade Agreement), COMESA (Common market for Eastern and Southern

Africa), MERCOSUR (Mercado Común del sur, or the Southern Common Market) and NAFTA

(North American Free Trade Agreement). Appendix A3 lists the entire list of countries taken for

the sample, in which the member nations belonging to each RTA are also stated. For each of the

five RTAs considered, three dummies are taken – i) an intra-RTA dummy, ii) destination

belonging to the RTA and iii) origin belonging to the RTA.

The dataset contains 135 countries, for the time period 2000 – 2014 (the last year for which the

data was available). For the panel identifier, a country-pair is formed. So, the panel setting is

structured as for example, export for a bilateral pair – USA to UK at time 2005. The trade flow

(exports), distance, contiguity, common language and colonial relationship variables are dyadic

variables. Per capita incomes, GDPs and remoteness indexes for the origin and destination

countries are monadic variables, identified with the respective country (origin or destination). The

aim of the initial year being 2000, is to assess the impact of intra-trade in ASEAN after all the

members have joined (the latest being Cambodia in 1999). In Table 2, all variables except distance,

contiguity, common language and common colonizer are time-variant. Some of the RTA variables

are time variant, and others are time invariant. In the time period considered, 4 out of the 6 RTAs

are time-invariant (meaning either the origin or the destination country or both part of the RTA

joined or left the RTA in the time period of this study). Except COMESA (Seychelles, Libya,

Namibia and Angola joined amid the time period considered) and APTA (China joined in 2001),

every other RTA dummy variable set is time invariant.

4.1. Variables

The bilateral trade flow data from CEPII describes the trade flow from the origin country to the

destination country. The limitation of this data is that some of the exports from A to B might have

been recorded as the imports of B from A, and hence these two values are not bound to be similar,

since exports are recorded FOB (Free-on-board) and imports are recorded CIF (Cost, insurance &

freight). This type of data mismatch has been avoided as much as possible (Head & Mayer, 2014),

by estimating the CIF, and adjusting the value of imports of B from A, to obtain exports of A to

21

B. The trade flows data contains zero values, and these could be for multiple reasons as well. There

might have been recording systems where extremely low values did not pass the cut-off amount to

be entered as a sale. The estimation method – the Poisson pseudo-maximum likelihood estimator

is to take care of this predicament, as mentioned in section 4.2.

The bilateral distance variable given by Head & Mayer (2014) in the CEPII database, as well as

used by several researchers (Santos Silva & Tenreyro, 2006), Kien (2009), Westerlund &

Wilhelmsson (2011), etc. is the geographic ‘Great Circle Distance’ calculated between two

countries’ as the shortest geo-spatial distance from the two countries’ capital cities, on a flat map.

The distance variable is a proxy for transportation costs, prominent in use in the gravity models

ever since its popularity surged. The income variables are straightforward in explaining. The notion

of the gravity model vis-à-vis the Newton equation, requires the size of the economy to be the

‘mass’, and hence, GDPs. The idea behind the GDP per capita variables is due to heterogenous

preferences which vary with income and standard of living. The industries of countries with higher

per capita incomes demand different types of products than those with lower per capita incomes.

This is in line with Krugman’s (1979) new trade theory. Anderson & van Wincoop (2003) stated

in their seminal work that it is very important for a person studying the gravity equation to include

multilateral resistance terms (MRTs) in the equation.

Krugman’s (1995) words on MRTs make it easier to understand. Two countries trade more with

each other the more remote they are with respect to the rest of the world, ceteris paribus. This is

due to the fact that if a pair of countries are remote from the rest of the world, even with greater

individual distance to the rest of the world, they trade more with each other. They are so otherwise

known as “remoteness indexes” (Wei, 1996). Hummels (2001) & Feenstra (2016) advocate the

use of origin and destination fixed effects, in a cross-sectional setting, to account for MRTs. Taking

it further into a panel setting, Olivero & Yotov (2012) advocate the use of directional (exporter-

time and importer-time) fixed effects, to account for the time-variant nature of MRTs. The usage

of directional fixed effects however, will also absorb individual observable and unobservable time-

varying variables such as GDPs, per capita GDPs, etc. Hence, in this study, the construction of

“remoteness indexes” is how the MRTs will be accounted for (Baier & Bergstrand, 2009). Baldwin

22

and Taglioni (2006) emphasize the importance of including MRTs in the gravity equation, and

calls omitting it the “Gold Medal” error.

Over the course of history, contiguity, common language and common colonial relationships have

proven to be part of the standard gravity model (Abbas & Waheed, 2019). Tinbergen (1962) in

the first gravity model of trade included common border and language variables in the analysis.

Although not backed by theoretical foundations, it is shown that sharing the same official languages

and sharing a border does boost cultural relationships (Tinbergen, 1962; Egger, 2002; Anderson

& van Wincoop, 2003), and thus, boost trade (even in hostile situations, such as India-China,

India-Pakistan, etc.). Table 3 below, reports the descriptive statistics of the time-variant variables.

Table 3: Descriptive Statistics of the time-variant variables

Variable Observations Mean Std. Dev. Minimum Maximum

Trade 235,324 464.8 3839.23 0 283660

GDPot 235,324 432162.2 1455334 201.8999 1.74E+07

GDPdt 235,324 432162.2 1455334 201.8999 1.74E+07

GDPpcot 235,324 14298.35 18409.38 108.0145 112481.2

GDPpcdt 235,324 14298.35 18409.38 108.0145 112481.2

DISTod 235,324 7434.23 4307.947 60.77057 19781.39

REMot 235,324 128.1646 34.17 80.02833 250.3022

REMdt 235,324 123.4544 34.17 80.02833 250.3022

Contig 235,324 0.021 0.144 0 1

Comlang_off 235,324 0.125 0.331 0 1

Colony 235,324 0.016 0.126 0 1

EUo 235,324 0.172 0.378 0 1

EUd 235,324 0.172 0.378 0 1

EUod 235,324 0.029 0.169 0 1

MERCOSURo 235,324 0.039 0.192 0 1

MERCOSURd 235,324 0.039 0.192 0 1

MERCOSURod 235,324 0.001 0.034 0 1

NAFTAo 235,324 0.023 0.150 0 1

NAFTAd 235,324 0.023 0.150 0 1

NAFTAod 235,324 0.0003 0.018 0 1

COMESAo 235,324 0.144 0.350 0 1

COMESAd 235,324 0.144 0.350 0 1

COMESAod 235,324 0.02 0.138 0 1

APTAo 235,324 0.038 0.190 0 1

APTAd 235,324 0.038 0.190 0 1

APTAod 235,324 0.001 0.033 0 1

AFTAo 235,324 0.080 0.266 0 1

AFTAd 235,324 0.080 0.266 0 1

AFTAod 235,324 0.005 0.072 0 1

23

The time invariant dummies of common border, language and colonizer will have the minimum

and maximum of zero and one respectively, and hence futile to describe. The interesting feature of

the table above is the trade variable. The trade variable’s mean and standard deviation show a heavy

skew in the data, indicating presence of heteroskedasticity. This is due to the presence of zero trade

values in some parts of the sample. The set of income variables are the same for the origin and

destination, since the sample contains two pairs of monadic variables for the same set of countries.

The data contains 192,180 observations with positive trade values, out of the total 235,324

observations, indicating 18.33% of the sample for trade containing zero values, and some missing

values for certain pairs. The GDP and trade coefficient’s correlation are 0.55, and the distance and

trade correlation are -0.26, showing that the basic gravity model variables are as expected. Appendix

B1 provides the correlation table for the Appendix B2 provides the scatter plot with a line fit for

the basic gravity relationships (positive relationship of trade with the GDPs of origin and

destination; negative relationship of trade with geographical proximity, i.e., distance). Amongst

the independent variables, naturally, there are significant correlations among GDPs and GDP per

capita of the origin country, and for the destination country, and for the sake of the specification

of the model chosen, the study will continue to suffer from this limitation. However, the

correlation among all the other variables in the model are very low. The RTA dummies and how

they answer the research question are given in the following paragraphs. The dummy variables are

defined as follows:

𝐴𝑆𝐸𝐴𝑁𝑜 = 1 if the origin is a member of AFTA; 𝐴𝑆𝐸𝐴𝑁𝑑 = 1 if the destination is a member of

ASEAN and 𝐴𝑆𝐸𝐴𝑁𝑜𝑑 = 1 if both the origin and destination are members of ASEAN. 𝐴𝑆𝐸𝐴𝑁𝑜

represents the bloc’s exports to the rest of the world, 𝐴𝑆𝐸𝐴𝑁𝑑 represents the bloc’s imports from

the rest of the world, and 𝐴𝑆𝐸𝐴𝑁𝑜𝑑 represents intra-bloc trade. The RTA dummy variables for

MERCOSUR, APTA, COMESA, EU and NAFTA are formed in a similar way, obtaining 18 total

RTA dummy variables. To explain the effect of trade creation and diversion, using these three

dummy variables will help:

𝛽1𝐴𝐹𝑇𝐴𝑜 = 1 𝑖𝑓 𝑜 𝑖𝑠 𝑚𝑒𝑚𝑏𝑒𝑟 𝑜𝑓 𝐴𝑆𝐸𝐴𝑁, where o is origin and d is destination.

𝛽2𝐴𝐹𝑇𝐴𝑑 = 1 𝑖𝑓 𝑑 𝑖𝑠 𝑚𝑒𝑚𝑏𝑒𝑟 𝑜𝑓 𝐴𝑆𝐸𝐴𝑁

24

𝛽3𝐴𝐹𝑇𝐴𝑜𝑑 = 1 𝑖𝑓 𝑜 𝑎𝑛𝑑 𝑑 𝑎𝑟𝑒 𝑚𝑒𝑚𝑏𝑒𝑟𝑠 𝑜𝑓 𝐴𝑆𝐸𝐴𝑁

𝛽1, 𝛽2 𝑎𝑛𝑑 𝛽3 are the coefficients for the three variables, o and d are origin and destination,

respectively. A positive significant 𝛽3 indicates more intraregional trade, which corresponds to

possible complement to domestic production, or to substitution to exports to the rest of the world.

So, in order to decide whether this attributes to trade creation or trade diversion, we need to look

at the signs of the coefficients 𝛽1 𝑎𝑛𝑑 𝛽2. A positive 𝛽3 with a low propensity to export to the rest

of the world, 𝛽1 being less than zero, indicates trade diversion from rest of the world. If this

increase in 𝛽3 is entirely consumed by the decrease in 𝛽1, it indicates pure trade diversion. If the

increase in 𝛽3 is larger than the decrease in 𝛽1, then there is both trade creation and diversion.

Finally, if 𝛽3 and 𝛽1 are both positive, then there is pure trade creation. If 𝛽1 is less than zero (trade

diversion) in terms of welfare for non-members is there is decrease in their welfare. Similarly, in

trade creation, there is increased welfare for non-members and members, due to increased trade

amongst the members of the RTA, as well as to the rest of the world.

4.2. Methodology

In any large database such as the gravity database, we must expect many zero values, as many

countries differ in their quality and efficiency of recording the values. Haveman & Hummels

(2004) find out in their study that about 1/3rd of their bilateral trade values are missing. Helpman

et al. (2008) show that half of the trade matrix has zero values. This paper as well, as mentioned in

the previous section, has 18.33% missing values. Despite this troublesome issue, researchers still

used the log-linear version of the gravity equation to estimate. This causes serious bias in the main

estimates (Santos Silva & Tenreyro, 2006; Westerlund & Wilhelmsson, 2011). Since log of zero

is undefined, there are bound to be missing values. Dropping the zero observations creates

informational bias. Truncated samples yield misleading interpretations. Martin & Pham (2015)

explained that the missing values were hardly emphasized due to the convenience provided by the

log-linear estimates. One solution is to use the log linear version of the dependent variable (trade

+ 1)9, so that the log of ones will now be zero. The estimation method used in this paper is the

9 Burger, van Oort & Linders (2009) show a similar analysis of comparisons between OLS with log of Trade + 1

and PPML with Trade and provides further conclusive results that the PPML method is able to take advantage

of the zero values in trade, and provides a robust result with both trade and trade > 0 as the dependent variable.

25

Poisson Pseudo-Maximum Likelihood (PPML) estimator. Proposed first by Santos Silva &

Tenreyro (2006), the PPML method gained a lot of popularity in the most recent decade. PPML

is a robust approach when considering heteroskedasticity, and with largely extant zero values. This

is because the dependent variable is in levels, and estimated in a multiplicative exponential method.

Yotov, Piermartini, Monteiro & Larch (2016) as part of the UNCTAD in association with the

WTO in their six recommendations on estimating a gravity model, suggest the PPML method.

The PPML model does not assume homoskedasticity, and follows the robust variance-covariance

matrix as in White (1980). Burger, van Oort & Linders (2009) also advocate the use of a fixed

effects Poisson PML estimator. Larch, Wanner, Yotov & Zylkin (2018) study the effect of currency

unions (CU) on trade and income using linear regression and the PPML regression, and find out

that the PPML estimates are statistically superior to the OLS. The PPML method is shown to

provide the impacts of trade policy on trade, consistent with the theory (Anderson, Rees & Sabia,

2015; Larch and Yotov, 2016). As a robustness test, recommended by Head & Mayer (2014), this

paper also shows the comparison of PPML results to that of OLS, with the RTA dummies. The

standard form of the PPML model in this study is:

𝑇𝑟𝑎𝑑𝑒 = exp(𝛼0 + 𝜇𝑡 + 𝛽0𝑙𝐺𝐷𝑃𝑜𝑡 + 𝛽1𝑙𝐺𝐷𝑃𝑑𝑡 + 𝛽2𝑙𝐺𝐷𝑃𝑝𝑐𝑜𝑡 + 𝛽3𝑙𝐺𝐷𝑃𝑝𝑐𝑑𝑡 + 𝛽4𝑙𝐷𝑖𝑠𝑡𝑜𝑑 + 𝛽5𝑙𝑅𝐸𝑀𝑜𝑡 +

𝛽6𝑙𝑅𝐸𝑀𝑑𝑡 + 𝛽7𝐶𝑜𝑛𝑡𝑖𝑔𝑜𝑑 + 𝛽8𝐶𝑜𝑚𝑙𝑎𝑛𝑔𝑜𝑑 + 𝛽9𝐶𝑜𝑙𝑜𝑛𝑦𝑜𝑑 + 𝛼0𝐴𝐹𝑇𝐴𝑜𝑡 + 𝛼1𝐴𝐹𝑇𝐴𝑑𝑡 + 𝛼2𝐴𝐹𝑇𝐴𝑜𝑑𝑡 +

𝛾0𝐴𝑃𝑇𝐴𝑜𝑡 + 𝛾1𝐴𝑃𝑇𝐴𝑑𝑡 + 𝛾2𝐴𝑃𝑇𝐴𝑜𝑑𝑡 + 𝛿0𝐶𝑂𝑀𝐸𝑆𝐴𝑜𝑡 + 𝛿1𝐶𝑂𝑀𝐸𝑆𝐴𝑑𝑡 + 𝛿2𝐶𝑂𝑀𝐸𝑆𝐴𝑜𝑑𝑡 +

𝜃0𝑀𝐸𝑅𝐶𝑂𝑆𝑈𝑅𝑜𝑡 + 𝜃1𝑀𝐸𝑅𝐶𝑂𝑆𝑈𝑅𝑑𝑡 + 𝜃2𝑀𝐸𝑅𝐶𝑂𝑆𝑈𝑅𝑜𝑑𝑡 + 𝜌0𝑁𝐴𝐹𝑇𝐴𝑜𝑡 + 𝜌1𝑁𝐴𝐹𝑇𝐴𝑑𝑡 + 𝜌2𝑁𝐴𝐹𝑇𝐴𝑜𝑑𝑡 +

𝜋0𝐸𝑈𝑜 + 𝜋1𝐸𝑈𝑑 + 𝜋2𝐸𝑈𝑜𝑑) ∗ 휀𝑜𝑑𝑡 (1)

𝜇𝑡 represents the time or business cycle fixed effects, which is common for both the origin and the

destination. 𝐶𝑜𝑛𝑡𝑖𝑔𝑜𝑑 is the dummy indicating whether the origin and destination share a

common border or not; 𝐶𝑜𝑚𝑙𝑎𝑛𝑔𝑜𝑑 is the dummy indicating if the origin and the destination

share a common official language or not; 𝐶𝑜𝑙𝑜𝑛𝑦𝑜𝑑 is the dummy indicating if both the origin

and the destination have ever had a colonial relationship; 𝐴𝐹𝑇𝐴𝑜𝑡 is the dummy whether the origin

belongs in AFTA (ASEAN) or not at time t, 𝐴𝐹𝑇𝐴𝑑𝑡 is the dummy whether the destination is a

member of AFTA or not at time t. 𝐴𝐹𝑇𝐴𝑜𝑑𝑡 is whether both are members of AFTA at t. The rest

of the RTA dummies are interpreted the same way. The multilateral resistance terms in the form

of directional (exporter-time and importer-time fixed) effects (Hummels, 2001; Feenstra, 2016) is

26

not used in this study. Inclusion of exporter-time and importer-time fixed effects will solve the

“Gold Medal Error”, but the inclusion of country-time specific variables such as GDP and GDP

per capita will not be possible. Olivero & Yotov (2012) show that the inclusion of these fixed

effects in lieu of absorption of the country-specific variables such as GDPs and exchange rates, etc.,

depends on the model specification and the study itself. In this paper, the inclusion of MRTs is

limited to remoteness indexes for the exporter and the importer, along with the inclusion of year

fixed effects, to account for business cycle effects in the time periods that may affect trade.

Also, it must be noted however, that PPML is not the only solution for zero trade. Eaton & Tamura

(1995) and Martin & Pham (2008) propose the Tobit estimation method. Another noteworthy

alternative to log linear OLS is given by Helpman, Melitz & Rubinstein (2008), using a two-stage

estimation technique, where in the first stage, using probit, determines the probability of a

particular country pair to have positive trade flows, and then estimates using OLS the positive

samples. Egger & Larch (2011) propose a similar method, by estimating a two-part gravity model,

by determining the decision to export to country at all exists or not. Among all these solutions, the

PPML method is the most convenient one to explain and interpret and execute. The PPML

method, amongst the other solutions, works consistently well even with large proportions of zero

trade values and missing values (Yotov, Piermartini, Monteiro & Larch, 2016). Perhaps the biggest

limitation of this study is the potential endogeneity of the RTA dummy variables. As stated by

Trefler (1993), two or more countries are likely to liberalize its trading policy with an RTA, given

that they are already significant trading partners. The use of instrumental variables to solve this

issue is another challenge, given that it is hard to find appropriate instruments (Baier & Bergstrand,

2007). Egger & Nigai (2015) show that the use of country-pair fixed effects10 are among the best

way to solve the problem of RTA endogeneity. However, this study will continue to suffer from

this issue, and is beyond the scope of this analysis.

10 Including pair fixed effects for PPML creates more than 17,000 pairs in this study, which creates more than

the limit of variables in statistical software used such as STATA. Moreover, the reason for including remoteness

indexes and multiple RTAs is to account for bilateral fixed effects for the largest trading countries in the sample.

27

To summarize the research hypothesis in terms of the variables chosen in the model for AFTA:

If 𝛼2 > 0, 𝑎𝑛𝑑 𝛼0 > 0, there is pure trade creation in terms of exports.

If 𝛼2 > 0, 𝑎𝑛𝑑 𝛼0 < 0, and if |𝛼0| is greater than |𝛼2|, there is pure trade diversion in terms of

exports.

If 𝛼2 > 0, 𝑎𝑛𝑑 𝛼0 < 0, and if |𝛼0| is lesser than |𝛼2|, there is both trade creation and diversion

in terms of exports.

In case 𝛼2 = 0, then a positive 𝛼0 indicates an export trade creation, and an insignificant

intraregional trade in AFTA.

28

5. Results and Discussions

Table 4 presents the regression results for proposed model in Equation 1 in the previous section.

The first two columns show the results for the PPML regressions, and the third and fourth columns

show the same results for OLS, all with year fixed effects.

Table 4 shows the regressions results of equation 1. Columns 1 and 2 show the results of the PPML

estimation (column 1 is estimated by PPML including all the zero observations in the trade

variable, and column 3 shows only the positive subsample of the trade variable). Columns 3 and 4

are the results of the OLS estimation of equation 1. These are provided solely for a robustness

check for the choice of PPML. Similar to the PPML estimation, the column 3 shows the OLS

results with log of trade as the dependent variable (this regression eliminates the observations with

value zero in the trade variable, when converted to log), and column 4 shows the OLS estimation

for the dependent variable trade, plus one. This includes the zero observations of trade as zero in

the log-converted variable, and thus includes all observations.

Columns 1 and 4 have the same number of observations, where columns 2 and 3 have the same

number of observations. The columns 3 & 4 show wider different results than columns 1 & 2 give.

This indicates that the choice of PPML in the presence of many zero values is optimal, and the

PPML method is indeed able to take advantage of the zero values.

5.1. AFTA and other RTA Estimates

The result of this study is in contrast to Kien (2009), in their study of ASEAN’s trade policy shows

that entering AFTA leads to significant pure trade creation in terms of exports. This means that

the 𝐴𝐹𝑇𝐴𝑜𝑑𝑡 & 𝐴𝐹𝑇𝐴𝑜𝑡 show positive significant results. As described in the previous section

regarding how the RTA dummy variables are interpreted, and how they correspond to trade

creation or diversion, it is important to look at all the three dummy variables pertaining to each

RTA. In light of this study about ASEAN, the variables 𝐴𝐹𝑇𝐴𝑜𝑑𝑡, 𝐴𝐹𝑇𝐴𝑜𝑡 & 𝐴𝐹𝑇𝐴𝑑𝑡. ASEAN’s

intra-trade is not significant, and ASEAN’s bloc exports are positive and significant, indicating that

there is high propensity for ASEAN countries to export to the rest of the world.

29

Table 4: Regression results (Equation 1)

Estimator (1) PPML (2) PPML (3) OLS (4) OLS

Dependent Var. trade trade>011 ln (trade) ln (trade + 1)12

Origin’s GDP 0.813*** 0.799*** 1.219*** 0.683***

(0.021) (0.021) (0.011) (0.007)

Destination’s GDP 0.776*** 0.763*** 1.087*** 0.632***

(0.022) (0.021) (0.011) (0.007)

Distance -0.736*** -0.728*** -1.544*** -0.913***

(0.041) (0.041) (0.029) (0.020)

Origin per capita GDP 0.078** 0.077** 0.123*** 0.101***

(0.033) (0.033) (0.017) (0.009)

Destination per capita GDP 0.106*** 0.106*** 0.063*** 0.054***

(0.026) (0.026) (0.015) (0.009)

Origin's remoteness 0.200 0.168 1.701*** 1.100***

(0.166) (0.163) (0.084) (0.052)

Destination's remoteness 0.261 0.255 0.535*** 0.647***

(0.170) (0.166) (0.067) (0.050)

Contiguity 0.465*** 0.468*** 0.560*** 0.827***

(0.098) (0.097) (0.113) (0.085)

Common Official Language 0.267*** 0.261*** 0.892*** 0.355***

(0.088) (0.087) (0.054) (0.033)

Colonial Ties -0.045 -0.038 0.657*** 0.960***

(0.144) (0.141) (0.105) (0.080)

𝑀𝐸𝑅𝐶𝑂𝑆𝑈𝑅𝑜𝑡 -0.239* -0.251* -0.474*** -0.271***

(0.141) (0.140) (0.085) (0.050)

𝑀𝐸𝑅𝐶𝑂𝑆𝑈𝑅𝑑𝑡 -0.281** -0.286*** 0.628** 0.740***

(0.111) (0.111) (0.291) (0.209)

𝑀𝐸𝑅𝐶𝑂𝑆𝑈𝑅𝑜𝑑𝑡 0.697*** 0.667*** -0.849*** -0.218***

(0.230) (0.232) (0.065) (0.071)

𝑁𝐴𝐹𝑇𝐴𝑜𝑡 -0.453*** -0.447*** 0.157* 0.334***

(0.097) (0.096) (0.088) (0.084)

𝑁𝐴𝐹𝑇𝐴𝑑𝑡 0.226 0.226 -0.483 1.612***

(0.144) (0.145) (0.429) (0.185)

𝑁𝐴𝐹𝑇𝐴𝑜𝑑𝑡 0.444** 0.453** -0.474*** -0.271***

(0.222) (0.222) (0.085) (0.050)

𝐸𝑈𝑜𝑡 -0.512*** -0.552*** 0.709*** 0.353***

(0.092) (0.091) (0.045) (0.030)

11 This column is to show the similarity between the regression results of column 1 and 2, where the second

column is the PPML regression results with only positive trade values. This shows that PPML can in fact take

advantage of the zero trade values, and provides robust results in presence of heteroskedasticity. 12 This column is the OLS attempt to solve the problem of zero trade, by taking ln(trade+1) as the dependent

variable. This column along with column 3 is to show the difference in results when the zero-trade problem is

avoided. This shows that OLS is not robust in presence of heteroskedasticity and produces biased results.

30

Table 4 - continued

𝐸𝑈𝑑𝑡 -0.290*** -0.316*** -0.048 0.030

(0.089) (0.088) (0.050) (0.033)

𝐸𝑈𝑜𝑑𝑡 0.600*** 0.632*** -0.228*** 0.794***

(0.112) (0.112) (0.071) (0.050)

𝐶𝑂𝑀𝐸𝑆𝐴𝑜𝑡 -0.400*** -0.341** 0.168** 0.440***

(0.152) (0.153) (0.065) (0.033)

𝐶𝑂𝑀𝐸𝑆𝐴𝑑𝑡 -0.132 -0.113 0.391*** 0.358***

(0.086) (0.086) (0.058) (0.031)

𝐶𝑂𝑀𝐸𝑆𝐴𝑜𝑑𝑡 1.036*** 1.063*** 1.024*** 0.344***

(0.242) (0.254) (0.176) (0.072)

𝐴𝑃𝑇𝐴𝑜𝑡 0.616*** 0.597*** 1.130*** 0.890***

(0.128) (0.129) (0.065) (0.054)

𝐴𝑃𝑇𝐴𝑑𝑡 0.532*** 0.517*** 0.703*** 0.549***

(0.117) (0.115) (0.081) (0.060)

𝐴𝑃𝑇𝐴𝑜𝑑𝑡 -0.719** -0.702** -1.577*** -0.443

(0.291) (0.294) (0.242) (0.270)

𝑨𝑭𝑻𝑨𝒐𝒕 0.769*** 0.740*** 1.076*** 0.626***

(0.100) (0.099) (0.070) (0.041)

𝑨𝑭𝑻𝑨𝒅𝒕 0.674*** 0.655*** 0.569*** 0.292***

(0.127) (0.126) (0.071) (0.042)

𝑨𝑭𝑻𝑨𝒐𝒅𝒕 0.109 0.095 -0.631*** -0.077

(0.164) (0.163) (0.216) (0.184)

Observations 235,324 192,180 192,180 235,324

Year Fixed Effects Yes Yes Yes Yes

R-squared 0.788 0.787 0.686 0.729

Note: Robust standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1

The time period considered in Kien (2009) is before and after all the members of ASEAN have

joined AFTA. The time period in this study, however, is after all the members have joined. Looking

back at Table 1, the intra-trade concentration of ASEAN has been more or less the same, wavering

higher and lower, and not having a pattern (like APTA & NAFTA). The intra-ASEAN trade is

not significant, and hence, even if the 𝐴𝐹𝑇𝐴𝑜𝑡 is positive and significant, we cannot say that there

is pure trade creation.

However, it is still worth looking at the sign of 𝐴𝐹𝑇𝐴𝑜𝑡. The coefficient of 𝐴𝐹𝑇𝐴𝑜𝑡 is 0.769 and

statistically significant at 1% level, showing that the bloc’s propensity to export to the rest of the

world is 115.7% higher than what the gravity model predicts. With intraregional trade not

significant and bloc’s exports to the rest of the world being positive significant, this attributes to

31

an export trade creation. In terms of welfare implications, there is an increased welfare for non-

members, but not for the members of ASEAN. This is in contrast to the result of Carrere (2006)

who used the Hausman-Taylor method to analyse the effects of RTAs15, and finds that the AFTA

causes both trade creation and diversion. In Carrere (2006), the dependent variable is imports, and

the main finding is a positive significant intra-ASEAN trade, and a negative significant ASEAN

imports from rest of the world. But, the negative coefficient of ASEAN’s imports is lesser than the

positive intra-ASEAN trade. This indicates presence of both trade creation and diversion, in terms

of imports in Carrere (2006).

In this study, this is not the case. The main finding of this study is that, there is an export trade

creation due to AFTA in ASEAN, but no significant intra-ASEAN trade creation, hence cannot

be classified as “pure trade creation”. This result is consistent with Blomqvist (2004), whose result

states that there is no significant intra-regional trade in ASEAN, but rather a positive significant

coefficient of ASEAN’s exports to non-members. ASEAN as a bloc saw an increasing rate of intra-

trade in the early 1990s, and until mid-2000s16. This is attributed to rising industrialization of

the member countries of ASEAN (especially Malaysia, Indonesia, Thailand and Vietnam).

Singapore as a driver of intraregional trade in ASEAN now slowly is decreasing, and the bloc’s

extra-regional trade is being dominated by China and the European Union17 (consistent with

Roberts (2004)). Considering the other regional trade agreements, MERCOSUR shows signs of

both trade creation and diversion, because the intra-RTA dummy is positive and significant, and

the exporter RTA dummy shows a negative sign, but not greater than the increase in intra-RTA

trade. A country pair belonging to MERCOSUR trades 100% more than a country pair not

belonging in MERCOSUR. This is in line with the findings of Martinez-Zarzoso & Nowak-

Lehmann (2003) and Garcia, Pabsdorf & Herrera (2013) who found significant intra-trade bloc

dummies, and contrast to the findings of Azevedo (2003), who found no significant impact of

intraregional trade.

In table 1, MERCOSUR’s intraregional trade concentration shows a stable ratio since 2002 until

the time period of this study. NAFTA on the other hand, shows significant pure trade diversion.

Intra-NAFTA trade is significantly 55.89% higher than a country pair not in NAFTA, but the

32

bloc’s propensity to export to rest of the world decreases by 36.42%. The decrease in NAFTA’s

propensity to export is not greater than the increase in intra-NAFTA trade, indicating both trade

creation and trade diversion. This is in contrast to Martinez-Zarzoso (2003), who shows pure trade

diversion for NAFTA. Rojid (2006) finds that there is intra-overtrading in COMESA, and that

there are possible expansions to trade with rest of the world, and other regions in Africa. This study

shows similar findings, with intra-COMESA trade being 181.79% higher than any country pair

not belonging to COMESA, and a 32.96% decrease in propensity of COMESA to export to the

rest of the world. This is due to the level of GDP, income per capita and the type of goods

demanded. COMESA is the most homogenous group in the RTAs considered in the paper, leading

to homothetic preferences as well, still in line with Krugman’s (1979) new trade theory.

A peculiar result of this study is the coefficients of APTA dummies. APTA shows significantly

negative intra-RTA coefficient, indicating that a pair of countries belonging to APTA have lesser

propensity to trade amongst themselves than they would with the rest of the world (Sen, Srivastava

& Webber, 2015). This is accompanied by a positive and significant export APTA dummy

coefficient. The biggest trading members of APTA are India, China and South Korea, who have

varying intensities of import and export economies. A regional trade agreement with lesser number

of members typically has lesser intensities of intraregional trade (Larch, Wanner, Yotov & Zylkin,

2018). This could be the result of delayed administrative procedures of facilitating an FTA, such

as rules of origin, etc. This is in line with Table 1, where we can see a constant downward trend of

intraregional trade concentration index of APTA.

With respect to the European Union, there is both trade creation and trade diversion. The

coefficient of 𝐸𝑈𝑜𝑑𝑡 shows that a pair belonging to the European Union trades 82% more than a

pair not belonging to the EU, all else remaining the same. This is accompanied by a reduced

propensity to export to the rest of the world by 40.07%. This result is consistent with Martinez-

Zarzoso & Nowak-Lehmann (2003), Martinez-Zarzoso (2003) and Carrere (2006). Ways to

improve this analysis would be to properly account for endogeneity of the RTA dummies, and

possibly reduce the bias in the estimates. As previously mentioned, there may exist reverse causality

between higher trade and formation of an RTA. A possible estimation alternative is the Hausman-

33

Taylor regression (Hausman & Taylor, 1981), which uses the lags of the endogenous variables as

instruments, and thereby, accounting for the possible endogeneity. The other way, which is

simpler, is to account for pair fixed effects.

5.2. The Traditional Gravity estimates

Santos Silva & Tenreyro (2006), Westerlund & Wilhelmsson (2011), Yotov, Piermartini,

Monteiro & Larch (2016) show that this is due to the fact that the PPML estimator works

consistently even in the presence of many zero values, whereas the OLS estimator becomes prone

to this issue, and shows biased standard errors under heteroskedasticity. Secondly, the theory-

consistent unit elasticity of income to trade is seen in OLS estimation13. The origin and destination

GDPs are close to 1 and statistically significant. The PPML estimation shows similar results, but

less exaggerated14. The variables GDP per capita and GDP of origin and destination show similar

results in PPML method, again, consistent with the theory. Higher the standard of living in a

country, higher they trade/demand goods from similar income countries. The per capita GDP of

the origin is 0.078, significant at 5% level, and for the destination, it is 0.106, significant at 1%

level. The coefficient of the importer’s GDP per capita is larger than that of the exporter’s GDP

per capita. One explanation to this may be that higher the standard of living in the importer

country, the more they demand from other countries which have a comparative advantage in the

high-value product. The GDP variable of the origin being higher than that of the destination’s

indicates higher domestic production, and hence more exports. But this coefficient is not as

different from the destination’s GDP. This also is in line with Krugman (1979), who postulated

that the levels of trade heavily depend on the income of the country that demands. The coefficient

of GDP of origin and destination in column 1 are 0.813 and 0.776 respectively. The coefficients

of the exporter’s GDP and the importer’s GDP stand with Krugman’s (1979) new trade theory

that higher trade is seen among similar income countries.

13 This is robust to excluding per capita incomes from the regressions (Santos Silva & Tenreyro, 2006; Abbas &

Waheed, 2019) 14 In presence of heteroskedasticity, which is heavily possible in a large panel gravity data, the OLS results are

biased, and hence provide a very low standard error. This is why almost all the estimates in the PPML

regressions are lower than that of the OLS estimates. PPML is a heteroskedasticity-consistent estimator.

34

The other initial gravity equation variable, log of distance is negative and significant, as expected,

at -0.736 in column 1. But this value is starkly different than the OLS estimate of -1.544 elasticity

in column 3. The PPML estimate suggests lesser role for transportation costs in determining

bilateral trade. In the OLS estimate, the exporter’s remoteness variable is highly significant, along

with the fact that exporter’s remoteness and importer’s remoteness coefficients are different. The

PPML estimates show similar coefficient values for exporter’s and importer’s remoteness indexes.

It is also not statistically significant. This may be due to the fact that after controlling for distance,

the remoteness variables become insignificant. This is cited as one of the limitations of using

remoteness indexes as MRTs, since the remoteness index is also a GDP-weighted distance variable

for both origin (exporter) and the destination (importer) countries (Head & Mayer, 2014). Albeit

being positive, which is consistent with theory, they are not significantly different than zero.

Further analysis in order to control for MRTs is possible by using the directional fixed effects

instead of remoteness indexes, and thereby, dropping the GDPs and per capita GDPs from the

equation (Head & Mayer, 2014).

The gravity dummies contiguity and common language dummies are positive and significant in

the PPML estimations. All remaining the same, two countries trade more if they have a common

language or share a border. The common colonial ties variable is positive but not significant. This

could be due to the fact in the time period considered for this model, 2000 to 2014, the memory

of higher trade due to common colonial past has faded away (Head, Mayer & Ries, 2010). Studies

in the past using a much earlier time period for the gravity model show significant results for this

variable. The interpretation of the contiguity variable and other dummy variables in the PPML

model is different than interpreting a logarithmic independent variable. The exponent of a log

independent variable is interpreted as it is in OLS. But the dummy variable coefficient for

contiguity in column 1 is: 𝛽7 = 0.465 => (𝑒0.465 − 1) X 100 = 59.201%, which means that two

countries sharing a border increases bilateral trade by 59.201%.

The other dummy variables and the RTA coefficients are interpreted in the same way (variables in

table 5). This is 59.679% in PPML with positive subsample of trade (column 2). There is not

much difference in these two estimates. The dummy variable common official language in column

35

one shows that a country pair having a same common language trades (𝑒0.267 − 1) X 100 = 30.6%

more than a country-pair that doesn’t share a common language. This estimate is 29.82% for the

positive subsample. Once again, there is very little difference. The differences in the positive trade

subsample of PPML and OLS (columns 1 & 4) are: the distance elasticity is larger in OLS than in

PPML; contiguity, common language and colony are all positive and significant in OLS, whereas

in PPML, colonial ties is not significant, and contiguity and common language, along with all

other coefficients, show smaller values than that of OLS.

The results are most in line with Krugman’s new trade theory, in an economic sense. Two countries

with similar incomes trade more with each other. The economic meaning behind the distance

variable as a proxy for the transportation costs being significant and negative in explaining trade,

is due to the fact that even with increased technology in the transportation sector over the decades

till today, the technology only brings down the level of transportation costs, but not the

proportions. For example, the reason for a negative significant value could be that a country pair

located far away from each other may face lower costs of transportation than it did several years

ago, but is still higher in proportion to a country pair located closer to each other. With regard to

the colony variable, as given by Head, Mayer & Ries (2010), the memory of economic cooperation

due to being in a colonial relationship slowly fades away as years pass.

The lacking aspect of this study is the use of remoteness index, which in the future studies could

be improved by proper accounting of the multilateral resistances affecting international trade.

Trade creation can be driven by similar demand structure in the countries involved in the RTA, as

seen by the estimates of the COMESA dummy variables. Largely agricultural economies trade

more within themselves in the same sector, thus exhibiting an intra-overtrading pattern. The main

finding regarding ASEAN, is that there is no significant positive intra-trade among the members,

but a positive significant bloc exports to the rest of the world, implying that the importance of

international trade when it comes to members of ASEAN themselves, are lower than that of the

opportunities provided by countries/regions such as China, Japan, USA, EU, etc., despite the

ASEAN members being part of AFTA. This asks the question further, of a possible expansion of

the RTA to include those members who have a higher existing trade with ASEAN

36

6. Conclusion

This paper set out to determine the post-establishment impact of the ASEAN Free Trade

Agreement AFTA, and find out whether AFTA leads to trade creation, trade diversion, or both.

The dependent variable of the model proposed in this paper is bilateral exports (from an origin

country to a destination country). The model is estimated using a Poisson Pseudo-Maximum

Likelihood Method (PPML). The traditional gravity variables such as GDPs, per capita GDPs,

bilateral distance, common border and common language show the expected positive signs. The

common colonial ties variable is insignificant in explaining bilateral trade, a result found consistent

with Head, Mayer & Ries (2010), that the memory of colonial relationships has decreased over

time. The multilateral resistance terms “remoteness indexes” are found to be insignificant. Perhaps

in the future studies, the inclusion of directional (exporter-time and importer-time fixed effects)

could solve this issue.

In finding out whether AFTA leads to pure trade creation or pure trade diversion, the main finding

of this paper is that there is neither pure trade creation nor pure trade diversion, but rather an

export trade creation. This is characterized by a positive significant coefficient in the ASEAN

members’ exports to the rest of the world dummy variable, and an insignificant intra-ASEAN trade

dummy variable. Had the intra-ASEAN trade dummy coefficient been positive and significant, it

would then be pure trade creation. But the intra-ASEAN trade dummy is not significantly different

from zero. This indicates that the members of ASEAN have no incentive to choose to trade

amongst themselves, compared to the rest of the world. In terms of welfare implications, there is

an increased welfare for the non-members of ASEAN, in terms of exports.

Future policy implications in order to strengthen the ties amongst the members of ASEAN, both

in terms of economic integration, as well as reducing the administrative cost of facilitating a

preferential trade agreement, must be undergone. ASEAN’s trading patterns show an increasingly

export-oriented trend, and an unsurprising expansion of ASEAN’s members into including China,

South Korea and Japan (ASEAN Plus Three) may give a kickstart to ASEAN’s insignificant

intraregional trade.

37

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43

Appendices

A1. Trade-to-GDP ratio

Chart 2: Trade-to-GDP ratios of ASEAN members (in %)

Source: Author’s calculations based on data from ASEANStats

A2. ASEAN Export Partners

Table 5: Shifts in ASEAN’s top 10 export destinations: 2004 - 2018

Rank 2004 2006 2008 2010 2012 2014 2016 2018

1 USA USA EU EU China China China China

2 EU EU Japan China Japan EU USA EU

3 Japan Japan USA Japan EU USA EU USA

4 China China China USA USA Japan Japan Japan

5 Malaysia Malaysia Malaysia Hong Kong Malaysia Hong Kong Hong Kong Hong Kong

6 Singapore Singapore Singapore Malaysia Hong Kong Malaysia Malaysia Malaysia

7 Hong Kong Hong Kong Indonesia Singapore Indonesia Singapore Singapore Singapore

8 Indonesia Indonesia Hong Kong Indonesia Singapore Indonesia Korea Rep. Korea Rep.

9 Korea Rep. Korea Rep. South Korea South Korea South Korea Korea Rep. Indonesia Indonesia

10 Thailand Thailand Australia Australia Australia Australia Thailand Thailand

11 Taiwan Australia Thailand India Thailand Thailand India India

12 Australia Netherlands India Thailand India India Australia Vietnam

13 Netherlands India Netherlands Taiwan Taiwan Taiwan Taiwan Taiwan

14 Germany Taiwan Taiwan Netherlands Netherlands Netherlands Vietnam Australia

15 UK Germany Vietnam Germany Vietnam Vietnam Netherlands Netherlands

0

50

100

150

200

250

2013 2014 2015 2016 2017 2018

Trade-to-GDP Ratio in %

Brunei Darussalam Cambodia Indonesia Lao PDRMalaysia Myanmar Philippines SingaporeThailand Viet Nam

%

Year

44

A3. List of Sample Countries

Table 6: List of countries used in the sample, along with which RTA they belong to

Antilles Estoniaf Iran Iceland Luxembourgf

Angola Costa Rica Honduras Mexicoc Saudi Arabia

United Arab Emirates Cyprusf Croatiaf Mali Sudane

Argentinad Czechiaf Hungaryf Myanmara Singaporea

Australia Germanyf Indonesiaa Mongolia Saint Helena

Austriaf Djiboutie Indiab Mauritiuse El Salvador

Azerbaijan Denmarkf Irelandf Malawie Somalia

Burundie Dominican Republic Iraq Malaysiaa Slovakiaf

Belgiumf Algeria Israel Namibia Sloveniaf

Bangladeshb Ecuador Italyf Nigeria Swedenf

Bulgariaf Egypte Jordan Netherlandsf Swazilande

Bahrain Eritreae Japan Norway Seychellese

Bosnia & Herzegovina Spainf Kazakhstan New Zealand Thailanda

Belarus Ethiopiae Kenyae Oman Turkmenistan

Bolivia Finlandf Laos Pakistan Trinidad & Tobago

Brazild Falkland Islands Cambodiaa Panama Tunisia

Bruneia Francef South Koreab Peru Turkey

Botswana Faroe Islands Kuwait Philippinesa Ugandae

Canadac Gabon Laosa Papua New Guinea Ukraine

Switzerland United Kingdom Libyae Polandf Uruguayd

Chile Ghana Sri Lankab Portugalf Uzbekistan

Chinab Gibraltar Lithuaniaf Paraguayd Venezuelad

Ivory Coast Equatorial Guinea Latviaf Qatar Vietnama

Congo Greecef Macao Romaniaf Yemen

Colombia Guatemala Morocco Russia South Africa

Comorose Hong Kong Madagascare Rwandae Zambiae

Dem. Congoe Taiwan Macedonia United States of Americac Zimbabwee

List of free trade agreements – cross check with list in table 7.

a – ASEAN Free Trade Agreement (AFTA)

b – Asia-Pacific Trade Agreement (APTA)

c – North American Free Trade Agreement (NAFTA)

d – Southern Common Market (MERCOSUR)

e – Common Market for Southern and Eastern Africa (COMESA)

f – European Union (EU)15

15 This sample excludes one EU member – Malta, due to very low levels of trade.

45

B1. Correlation Table

Table 7: Correlation matrix of the variables (dependent variable: Trade)

Note: All variables except Contig, Comlang and Colony are in logarithmic form.

B2. Basic Gravity Relationships

B2.1. Trade vs. GDP

Note: ltrade is Log of Trade

Graph 2: Positive Relationship between the combined GDPs of origin and destination and bilateral trade

(exports)

Correlation Trade GDP_o GDP_d Dist GDPpc_o GDPpc_d Rem_o Rem_d Contig Comlang Colony

Trade 1

GDP_o 0.547* 1

GDP_d 0.424* 0.037* 1

Dist -0.263* -0.004 -0.012* 1

GDPpc_o 0.331* 0.557* 0.044* -0.066* 1

GDPpc_d 0.243* 0.046* 0.571* -0.067* 0.045* 1

Rem_o 0.139* 0.074* -0.001 0.012* -0.078* 0.001 1

Rem_d 0.065* -0.015* 0.049* 0.018* -0.008* -0.077* -0.012* 1

Contig 0.174* 0.037* 0.037* -0.362* -0.005* -0.004 0.010* 0.009* 1

Comlang 0.034* -0.058* -0.059* -0.150* -0.055* -0.054* -0.060* -0.063* 0.142* 1

Colony 0.135* 0.084* 0.083* -0.068* 0.051* 0.052* 0.003 -0.001 0.117* 0.161* 1

46

B2.2. Trade vs. Distance

Note: ltrade is Log of Trade

Graph 3: Negative Relationship between geographical proximity (distance) and bilateral trade (exports)