i

Analysis of ASEAN FTA in the Dynamic East Asian Perspective: A

Gravity Equation Approach

Arisyi Fariza Raz

Institute for Development Policy and Management (School of Environment and

Development), University of Manchester, Manchester, United Kingdom

Email: arisraz@me.com

ii

Analysis of ASEAN FTA in the Dynamic East Asian Perspective: A Gravity Equation

Approach

ASEAN economies are amongst the emerging economies in the developing

world. Thus, it is necessary to investigate the impact of AFTA on East Asian

trade flows. Meanwhile, the revival of the gravity equation in the recent years has

turned it into a better tool in analysing regional trade flows. Therefore, the main

purpose of this paper is to analyse the determinants of trade in ASEAN 6

economies and the significance of AFTA in the East Asian regional trade by

augmenting the traditional gravity equation with new variables. The contribution

of this paper is three-folds. First, the empirical methodology used in this paper

corrects a major shortcoming that exists in the literature by introducing fixed

effects and random effects models. Second, it finds the main determinants of

trade flows in ASEAN 6 economies. Third, it shows the long-run impact of

AFTA on East Asian economies is net welfare gain.

Keywords: International trade, gravity equation, fixed effects and random effects,

ASEAN Free Trade Area, trade creation, trade diversion.

1

1. Introduction

Over the last two decades, the number of countries that have signed an FTA (Free Trade

Agreement) has dramatically increased. A report by the World Trade Organisation

(2000) identified that there were 148 free trade agreements in force and other 67

agreements were under negotiation. This indicates that regions around the world have

found it necessary to strengthen economic cooperation through formal trade agreements

in order to increase the competitiveness of local products in the world market through

the elimination of trade barriers. Even though, as suggested by Plummer (2009),

regional cooperation in East Asia has been characterised by “non-formal” and “market-

driven” integration, this increasing competition within international trade has made East

Asian countries realise the importance of establishing formal regional cooperation

agreements. As the consequence, ASEAN (The Association of South East Asian

Nations) was established in August 1967 and became the major regional organisation.

Initially aimed at strengthening regional political security cooperation, since

1970s, however, ASEAN member countries started to realise the importance of

strengthening regional economic cooperation through the coordination of industrial

policies and the promotion of intra-regional trade (Elliott and Ikemoto, 2004). In 1977,

its member countries established preferential trading arrangements (PTA’s) to promote

intra-regional trade. Nevertheless, empirical studies suggest that the PTA’s achieved

very limited success (see e.g. Ariff, 1994; Edwards and Skully, 1996; Garnaut and

Drysdale, 1994). In this regard, a study by Cuyvers and Pupphavesa (1996) points out

that the disappointing results of the PTA’s are due to the following factors: 1) the small

number of products included under the PTA’s relative to the total number of products

traded by ASEAN member countries and 2) the long exclusion lists maintained by

2

ASEAN member countries because they could easily exclude group products from the

PTA’s.

Figure 1. Intra-ASEAN Trade Flows (US$ Million)

Source: ASEAN Secretariat (2005)

However, rapid changes in global competition during the 1980’s and 1990’s

forced the ASEAN member countries to develop more serious regional economic

cooperation. In addition, the emergence of China’s economy intensified the competition

for attracting FDI from developed countries (Elliott and Ikemoto, 2004).

Consequentially, in January of 1992, six ASEAN member countries, Brunei

Darussalam, Indonesia, Malaysia, Philippines, Singapore and Thailand, signed a

declaration to achieve AFTA within 15 years.1 One year after the 1997 Asian Financial

Crisis, these countries agreed to move forward with trade liberalisation and thus AFTA

came into force among the ASEAN 6 countries at the beginning of 2002. The initial

goal of AFTA was to reduce tariffs on intra-ASEAN trade to between 0-5%. The

mechanism used to achieve this goal has been the reduction of the Common Effective

Preferential Tariff (CEPT), which includes agricultural products, manufactured goods

0!

30,000!

60,000!

90,000!

120,000!

150,000!

1993!1994!1995!1996!1997!1998!1999!2000!2001!2002!2003!2004!

Exports! Imports!

3

and services (Cuyvers and Pupphavesa, 1996). In this regard, some authors suggest that

the reason that AFTA is more encompassing than the previously established PTA’s is

because the former is easier to achieve because the approach of CEPT is reciprocal and

sectoral, whereas the latter adopted a product-by-product approach (Pangestu et al.,

1992; Elliott and Ikemoto, 2004).

Table 1. Shares of exports from the ASEAN 6 economies (value in US$ thousands)

Partner 1989 1992 1995 1998 2001 2003

Value 17,315,001 36,595,575 74,756,056 64,866,815 79,708,262 104,425,666

ASEAN 6

Share 14% 20% 24% 21% 22% 24%

Value 35,358,707 39,506,324 62,597,095 55,323,682 79,859,305 98,974,995 Korea,

China,

Japan Share 30% 22% 20% 18% 22% 22%

Value 66,968,247 104,395,395 173,992,587 194,896,019 203,770,589 238,890,177

ROW

Share 56% 58% 56% 62% 56% 54%

Value 119,641,955 180,497,294 311,345,738 315,086,516 363,338,156 442,290,838

World

Share 100% 100% 100% 100% 100% 100%

Source: Author’s calculation based on United Nations COMTRADE Statistics Database

After the establishment of FTA in 1992, there were changes in the direction of

the exports and imports of AFTA intra-regional trade as well as trade dependency with

outside trading partners. One obvious intra-regional effect of AFTA was the rapid

increase of intra-ASEAN trade flows. Figure 1 shows that the volume of intra-ASEAN

exports and imports were quadrupled in 2004 relative to the volume in 1993.

Nevertheless, statistics also show that there is a decreasing share of trade from the

ASEAN 6 member countries to the rest of the world. Table 1 presents the changing

4

shares of exports from the ASEAN 6 economies. It supports the statistics shown in

figure 1 by showing that there was an increasing trend of export shares of the intra-

ASEAN 6 economies from 1989 to 2003. On the other hand, during the same period,

the share of exports from the ASEAN 6 economies to China, Japan and South Korea

showed a downward trend, while the share of exports to the rest of the world tended to

be constant.

Despite these statistics, however, it is necessary to conduct a formal examination

in order to measure the welfare effect of AFTA. From the previous literature, one of the

most applicable methods for examining intra-regional and extra-regional economic

integration is to use the gravity equation. Since the revival of the gravity equation, many

authors augment the traditional gravity equation with some of the above variables,

depending on the data and purpose of the study. Further, the econometric methods have

also been improved from time to time, by estimating the equation with a more

sophisticated technique instead of ordinary least square, thus provides better

justification for the use of the model. The rest of the paper is as follows. The next

section provides the literature review regarding gravity equation, section 3 discusses the

methodology and estimates the augmented gravity equation. Section 4 and 5 provide the

estimated results and further analysis, respectively, while the last section concludes this

paper.

2. The Gravity Equation of International Trade

2.1. Review of the Previous Studies

The concept of the gravity equation in economics has been borrowed from

Newton’s law of gravitation. According to this law, the gravitational force that exists

between two celestial bodies depends positively on their mass and negatively on their

5

distance. In the 20th century, Tinbergen (1962) and Linneman (1966) adopted and

popularised this concept in the field of international economics to examine the patterns

of bilateral trade flows among developed countries. In contrast to its simple application

in physics, the application of the gravity equation in economics can be more

complicated. In this regard, Filippini and Molini (2003) suggest that there are two

important facts that are worth considering: 1) the concepts of distance and mass have to

be reinterpreted according to socio-economic phenomena, hence it is important to

consider the proxy carefully in estimating the equation; and 2) the relationship between

the dependent variable and the independent variables in the equation needs to be clearly

specified, usually in the form of natural logarithms in order to make the relationship

become linear. By taking into account these two facts, the gravitational force is

translated economically as the flows of goods from one country to another country.

Since its adoption, the gravity equation has become one of the most popular

methods used to investigate the flow of bilateral trade. One of the first and most

celebrated empirical works is a study by McCallum (1995), which investigates how the

Canada-US border affects regional trade patterns. After the work by McCallum (1995)

many economists used the gravity equation to find the determinants of trade of a

country. A study by Jugurnath et al. (2007) suggests that, in addition to the traditional

variables in the gravity model (i.e. GDP, distance, population, physical area and cultural

similarity), exchange rate movements and taxation rates also affect bilateral trade flows

in the Asia-Pacific region. Currency depreciation encourages exports and discourages

imports, whereas higher tax rates decrease bilateral trade. Most of other studies also

support the findings of Jugurnath et al. (2007) with respect to the relationship between

currency depreciation and trade flows (for instance, see Thursby and Thursby, 1987;

Carrère, 2006; Eita and Ashipala, 2008).

6

Melitz (2007) further investigates the role of geographical distance in the gravity

equation by deriving it into two variables: level of remoteness and internal distance. In

this regard, he suggests that internal distance has a significant negative influence on

trade flows, whereas remoteness of a country appears to be of only modest significance.

On the contrary, some other studies suggest that the remoteness of a country is

significantly and positively correlated with trade flows (Krueger, 1999; Carrère, 2006).

Moreover, Carrère (2006) also points out that countries with better levels of

infrastructure will trade more, while landlocked countries will trade less. Some studies

also analyse the role of adjacency in affecting trade flows. Most of these studies find

evidence that countries sharing the same border will have a greater propensity to trade

with one another (Endoh, 1999; Melitz, 2007). Further, Filippini and Molini (2003)

introduced technological distance into the equation to examine whether a technological

gap between two countries affects these countries’ trade flows. The results suggest that

countries tend to exchange more goods when they have less of a technological gap.

Other studies also investigate whether Linder’s (1961) hypothesis is really an

important determinant of trade flows. According to Linder (1961), two countries will

trade more with one another if they have more similar demand structures. The first

study to include the Linder hypothesis into the gravity model was that of Thursby and

Thursby (1987). The findings of this support the truth of the Linder hypothesis for

developed countries where manufactured goods account for a large proportion of

exports. This finding is also supported by that of Hallak (2006), who tested the

prevalence of the Linder hypothesis on bilateral trade amongst 60 countries in 1995.

Moreover, since the investigations of the Linder hypothesis usually use data from

developed countries, McPherson et al. (2001) argue that it is also necessary to test its

significance in developing countries. Their study examines the prevalence of the Linder

7

hypothesis in the traded manufactured goods in African countries and also concludes

that countries with similar per capita income levels tend to trade more intensively.

The gravity equation is also used to examine whether RTA causes trade creation

or trade diversion. Trade creation occurs when a trade alliance increases the trade flows

between member countries, which brings benefits to the member countries of the trade

alliance and thus increases their welfare. On the other hand, trade diversion is a shifting

of trade flows from being between a member country and a non-member country to

being between one member country and another member country of the trade alliance. It

decreases the welfare of the countries that do not belong to the trade alliance. A trade

alliance is working properly if the welfare-increasing effect is larger than the welfare-

decreasing effect, i.e. it causes a net increase in welfare. In the gravity equation, trade

creation and diversion can be measured by adding RTA dummies into the model.

Krueger (1999) investigates the trade effect under NAFTA and concludes that it was

trade-creating and not trade-diverting. The argument that RTA causes trade creation is

also supported by Endoh (2000), who investigated economic integration under APEC.

In his paper, he concludes that APEC promotes both trade with outer regions and trade

within APEC economies. Vicard (2011) conducted a global-scope case study that

covers five RTAs: the Andean community, AFTA, the EU, MERCOSUR and NAFTA.

In his study, he argues that the trade creation effect is higher if countries in the RTA

share many similar characteristics. In addition, he also adds that bilateral trade creation

between two members of an RTA is more efficient when the potential trade creation

among other partners is lower. On the other hand, Carrère (2006) suggests that an RTA

results in strengthened intra-regional trade, but it causes both import and export trade

diversions when a country trades with another country that does not belong to the same

RTA.

8

Some amount of literature also uses the gravity equation to study the main

determinants of trade flows in the ASEAN countries. Similar to many other gravity

studies, these studies also augment the traditional form of the gravity equation with

some new variables in order to test the determinants of trade. For example, a study Kien

and Hashimoto (2005) augments the equation with exchange rate in order to investigate

whether it plays an important role in determining trade in ASEAN. The result suggests

that currency depreciation in a country will increase its export competitiveness and thus

stimulate trade flows. A study by Hapsari and Mangunsong (2006) investigates the role

of tariff barriers in trade and suggests that tariff reduction has a significant effect on

increasing the bilateral exports of ASEAN member countries. It also points out that a

greater number of products may need to be included in the CEPT list to improve the

effectiveness of AFTA. Some studies also suggest that the so-called complementary

index of factor endowments is a significant factor in promoting trade flows in ASEAN

(Elliott and Ikemoto, 2004; Hapsari and Mangunsong, 2006).2

In addition to investigating the determinants of trade, the intra-regional and

extra-regional effects of AFTA also have been investigated. Elliott and Ikemoto (2004)

suggest that the effect of AFTA on trade flows was not immediate following the signing

of the AFTA agreement in 1992. They argue that this was due to the emergence of

credible competition for market share from the new exporting powers, such as China

and various South American countries. However, this result is contradictory to that of

Kien and Hashimoto (2005), who found that AFTA had an immediate impact on its

members trade flows in the years subsequent to 1992. Moreover, most gravity equation

studies also investigate whether AFTA causes trade creation or trade diversion. The

result shows strong empirical evidence that AFTA causes trade creation among its

country members (Elliott and Ikemoto, 2004; Kien and Hashimoto, 2005; Hapsari and

9

Mangunsong, 2006). Nevertheless, the evidence regarding whether AFTA exhibits

some trade diversionary effects is mixed. Hapsari and Mangunsong (2006) argue that

AFTA causes trade diversion and thus a shifting of trade from countries outside the

trade alliance to AFTA member countries. In spite of that, other studies suggest that

there is no clear evidence that supports the existence of trade diversionary effects after

the establishment of AFTA (see e.g. Elliott and Ikemoto, 2004; Kien and Hashimoto,

2005).

2.2. Gravity Equation and Economic Theories

As presented above, the gravity equation has gained empirical success because it

fits the data well. However, despite its empirical success, the gravity equation has been

criticised due to its lack of theoretical grounds. As a consequence, this leads to biased

estimations and a lack of understanding of what is causing the results (Anderson and

van Wincoop, 2003). To solve this problem, economists have been trying to find a

theoretical foundation for the gravity equation. Anderson (1979) was the first to derive

the gravity equation from economic theory. In his paper, he rearranges the Cobb-

Douglas expenditure system to derive the gravity equation by assuming that each

country produces only one specialised good and prices are the same in all countries.

Given these assumptions, the gravity equation can be obtained when consumers across

countries have identical homothetic preferences, subject to the incomes of both the

exporting and importing countries and costs related to distance.

After the work of Anderson (1979), more authors attempted to provide a link

between trade theories and the gravity equation. A study by Bergstrand (1989; 1990)

puts emphasis on product differentiation among firms rather than among countries in

order to explain bilateral trade flows. He suggests that the monopolistic competition

approach (and thus horizontal differentiation) might be a better model to explain trade

10

flows between similar countries. On the other hand, Deardorff (1995) conducted a study

from the neoclassical perspective, within a perfect competition setting. By using the

Hecksher-Ohlin (H-O) model, he argues that trade flows can be explained if product

differentiation exists at the national level. Therefore, his study is better suited to a case

in which factor endowments are different and intra-industry trade is limited, such as a

North-South type of trade.

A more recent work by Evenett and Keller (2002) further extends the idea of

product specialisation proposed by Anderson (1979) by focusing on intra-industry trade.

Their paper suggests that there are three types of trade models that differ in obtaining

product specialisation in equilibrium: 1) Ricardian models, 2) H-O models and 3)

increasing returns to scale (IRS) models. According to the logic of international trade,

countries around the world conduct exporting and importing activities because their

productions and demands are not equal in quantity and/or quality. In the process, each

country specialises itself in producing particular goods due to the different

advantageous factors acquired by each country. Evenett and Keller (2002) support this

proposition by suggesting that the volume of international trade is determined by the

extent of product specialisation, which can be due to any of the following factors:

technology differences (Ricardian models), different factor endowments (H-O models)

or increasing return to scale in production (IRS models). In this regard, they propose

that imperfect specialisation better explains the variation of trade flows, because perfect

specialisation requires large factor proportion differences, which are unnecessary. With

respect to this, they suggest that IRS models are the best theory in the gravity equation

context since they are consistent with the absence of factor proportion differences.3

Another reason why IRS models are the preferred models is that these models take into

account intra-industry trade, whereas H-O models only predict inter-industry trade.

11

In short, this part has shown that the gravity equation can be derived from a

large class of theoretical models. Nevertheless, trade flows cannot be solely determined

by any of these models since different theoretical models account for large proportions

of the observed trade flows in different circumstances (for instance, see Evenett and

Keller, 2002). Due to this, the empirical use of gravity equation to test any of these

theories is not necessary (Deardorff, 1995). Instead, many authors argue that empirical

study of the gravity equation should be focused on clarifying the determinants of trade

flows (for instance, see Filippini and Molini, 2003).

3. Methodology

3.1. Model Identification

In its traditional form, the gravity equation suggests that trade flows from country i to j

are positively related to the income of two countries and negatively related to the

distance between these two countries. For the purpose of estimation, this paper assumes

that the gravity equation is linear in natural logarithms, thus yielding the following

equation:

ln Xij = βo + β1 ln Yjt + β2 ln Yit + β3 ln Njt + ln β4 Nit + ln β5 Dij + ln uijt (1)

where Xij is the exports from country i to j, Y is income of both country i and j, N

is the population of both country i and j, and Dij is the geographical distance from

country i to country j and u is the error term.

Further, this paper also augments this traditional model of gravity equation with

some new variables to interpret the concepts of distance and mass from the economic

perspective in order to better analyse the trade performance of the ASEAN member

countries from an East Asian perspective. The first variable is income gap, which is

represented by the difference in GDP per capita between the exporting and importing

12

countries. The purpose of introducing this variable is to test the so-called Linder

hypothesis. In this case, per capita income is used as a proxy for the demand structure of

a country. Thus, countries with a smaller income gap will have more incentives to trade

with one other. In other words, this variable can be obtained by using the following

formula:

incgapijt = |GDP per capitait – GDP per capitajt| (2).

In addition to the income gap, this paper also includes two distance variables. In

its traditional form, the gravity equation only includes geographical distance to

represent the term “distance”. Nevertheless, this paper introduces three more “distance”

variables in addition to geographical distance because some authors argue that

geographical distance alone is not enough to represent the “distance” between the

exporting and importing countries (see e.g. Filippini and Molini, 2003; Melitz, 2007;

Herrera, 2010). The first variable is the cultural distance, which is represented by a

language dummy. The second is adjacency, which is characterised by a border dummy.

The third is the technological distance. Following the work of Filippini and Molini

(2003), this dissertation defines technological distance as the absolute difference of

technological indicators (TI) between the exporting and importing countries:

techdistijt = |TIit – TIjt| (3).

In this regard, Archibugi and Coco (2004) suggest that the TI or technological

capabilities of a country consist of three main dimensions, which are the creation of

technology (CoT), the technological infrastructure and the development of human skills

(i.e. TI is a simple average of these three dimensions). Firstly, the creation of

technology is a country’s capacity to produce technology. It is defined by estimating the

following formula:

(4)

13

where EXhtit are the high-tech exports of country i at time t, EXttlit are the total

manufactured exports of country i at time t, EXhtwt are the world high-tech exports at

time t and EXttlwt are the world total manufactured exports at time t.

Secondly, technological infrastructure is represented by a simple average of

Internet penetration, telephone penetration and electricity consumption. Lastly, the

development of human skills is calculated as a simple average of secondary and tertiary

enrolment rate plus literacy rate.

To make the second and third dimensions such that they can be expressed as a

value between 0 and 1, this dissertation adopts the formula used by Filippini and Molini

(2003) by converting these dimensions into indices, as follows:

(5)

where the actual value is the value of country i at time t, the maximum value is

the USA’s value at time t and the minimum value is zero.

Moreover, it is important to note that recent studies usually include the

remoteness of a country as another proxy for “distance” in order to take into account the

fact that some countries are located much further away from their trading partners than

other countries (see e.g Krueger, 1999; Anderson and van Wincoop, 2003; Carrère,

2006). Nevertheless, since this paper is concentrated on the ASEAN countries, which

are located in one geographic region, the inclusion of the remoteness of a country

becomes unnecessary (for instance, see Elliott and Ikemoto, 2004).

3.2. Econometric Specification

After taking into account the new variables presented in the previous section, the

augmented gravity equation is expressed in natural logarithmic form, as follows:

14

ln(exportsijt) = β0 + β1 ln(gdpjt) + β2 ln(gdpit) + β3 ln(popjt) + β4 ln(popit) +

β5 ln(distanceij) + β6 ln(techdistijt) + β7 ln(incgapijt) + β8

language + β9 border + αij + µijt (6)

where ln(exportsijt) is the value of exports from country i to country j in year t,

ln(gdpjt) is the GDP of the importing country j in year t, ln(gdpit) is the GDP of the

exporting country i in year t, ln(popjt) is the population of the importing country j in

year t, ln(popit) is the population of the exporting country i in year t, ln(distanceij) is the

geographical distance between the capital cities of exporting country i and importing

country j, ln(techdistijt) is the technological distance between exporting country i and

importing country j in year t, ln(incgapijt) is the gap in GDP per capita between country

i and country j in year t, language is a dummy variable that is 1 if two countries speak

the same official language and 0 otherwise and border is another dummy variable that is

1 if two countries share a common land border and 0 otherwise. β0 is the unknown

constant, αij is either a fixed or random unobserved country-pair effect and µijt is the log

normally-distributed idiosyncratic error term, where E(µijt) = 0.

From the estimation output of Equation (6), the predicted coefficients of GDP

and distance are clear due to the basic theory of gravity equation, i.e. positive

correlation for the former and negative correlation for the latter. However, in contrast to

GDP and distance, which have clear relationships with exports, the relationship between

exports and population is more complicated. If a large population represents a greater

reliance on a large domestic market because of the benefits from a large endowment of

resources, then a negative coefficient is expected. On the contrary, a positive coefficient

is expected if this huge domestic market gives an incentive for the exploitation of

economies of scale. Nevertheless, this paper assumes that the former has more influence

on trade, and thus population is expected to be negatively correlated with exports. This

15

paradox also applies to technological distance. In one hand, technological distance

could be a barrier to trade, since goods produced in one country might not be

technologically fit to be used in the other country. Nevertheless, it might also become

an incentive for trade, like the case of the four Asian Tigers with the developed world.

Hence, the expected coefficient of technological distance can be either positive or

negative. Further, the coefficient of income gap could also be positive or negative. The

sign depends on the prevalence of the Linder (1961) hypothesis in the ASEAN

countries. If the hypothesis is applicable to the ASEAN member countries, then the sign

of the income gap is expected to be negative. Otherwise, it is expected to be positive.

Further, the coefficients of border and language are intuitive, i.e. countries that share the

same language communicate smoother, where countries that share the same border can

trade less costly.

In addition to these main variables, we augment this model further with some

other dummy variables in order to examine whether AFTA causes trade creation or

trade diversion in terms of the trade interrelationships with other East Asian countries.

The first dummy is the AFTA dummy, which is 1 if both i and j countries belong to

AFTA and 0 otherwise. This paper selects a dynamic way of introducing the AFTA

dummy, following Kien and Hashimoto (2005). The objective is to capture the dynamic

formation, expansion or contraction effects of AFTA. The second and third dummies

are trade diversion dummies, i.e. AFTAim and AFTAex. The former reflects import trade

diversion (or extra-regional import bias) that occurs due to changes in the import

structure of AFTA. It equals to 1 if the importing country belongs to AFTA and 0

otherwise. On the other hand, the latter is meant to characterise the export trade

diversion (or extra-regional export bias) of AFTA to other East Asian countries. It

equals to 1 if the exporting country belongs to AFTA and 0 otherwise. In addition to

16

these FTA dummies, this paper also includes three country dummies: China03-09,

Korea03-09 and Japan03-09 to investigate whether there is a change in trade patterns

between the ASEAN member countries and their largest trade partners in the region.

4. Econometric Results and Interpretations

Equation (6) is estimated by using two sets of data. The first data set covers the ASEAN

6 member countries only. This data set consists of 30 bilateral trade flows. The use of

this data set in the estimation is aimed at seeking the main determinants of trade

amongst the ASEAN 6 economies. The second data set covers all nine countries

(ASEAN 6 + 3) and consists of 72 bilateral trade flows. From this data set, the purpose

of estimation is to examine the extra-ASEAN effects of AFTA in East Asia.

4.1. Determinants of Trade in ASEAN

This part presents the estimation results from the ASEAN 6 data set. As mentioned

earlier, the objective of this estimation is to seek the main determinants of the intra-

ASEAN trade flows. Nevertheless, before proceeding to the estimation results of

Equation (6), it is important to justify the stationarity of the variables included in the

model and thus unit root test is necessary. Following Carrère (2006) and Eita and

Ashipala (2008), this paper performs the Levin, Lin and Chu (2002) test for unit root to

test the stationarity of the following series: bilateral exports, GDP, population,

technological distance and income gap. In this test, the null states that the variable

contains a unit root. Table 2 below shows that the null of a unit is significantly rejected

at the 1% level for all variables, i.e. this confirms the stationarity of these variables.

This result implies that the cointegration test is not necessary. Thus, Equation (6) can be

estimated by using the fixed effects or random effects model.

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Table 2. Levin Lin Chu test for panel unit root (ASEAN 6 data set)

Variables t-star t-value p-value

ln(tradeijt) −16.265 −11.241 0.000

ln(gdp) −13.498 −8.968 0.000

ln(pop) −5.309 −3.770 0.000

ln(techdistijt) −14.503 −8.145 0.000

ln(incgapijt) −11.002 −5.565 0.000

Table 3 below shows the panel estimation results of Equation (6) for the

ASEAN 6 countries. The results of the fixed effects estimation are shown in the first

column (Reg. I) and those of the random effects estimation are shown in the second

column (Reg. II). As mentioned earlier, this paper intends to use the fixed effects model

because it allows heterogeneous effects in errors. The results of the F test strengthen this

argument because it strongly rejects the null, implying the presence of fixed effects.

Moreover, the Hausman test is also performed to ensure that the fixed effects are more

favourable than the random effects in estimating the model. Since the null is

significantly rejected at the 1% level, the test shows that fixed effects estimation is more

efficient for this model.

Table 3. Modified gravity equation: determinants of ASEAN regional trade

Variables Reg. I Reg. II Reg. IIIa

Constant −24.467 ***

(6.909)

−39.792 ***

(3.540)

−24.467

(32.387)

ln(gdpit) 1.667 ***

(0.315)

1.794 ***

(0.171)

1.667

(1.262)

ln(gdpjt) 2.693 ***

(0.315)

1.740 ***

(0.171)

2.693 ***

(0.577)

ln(popit) −1.425 *

(0.791)

−0.570 ***

(0.119)

−1.425

(3.838)

18

ln(popjt) −2.256 ***

(0.791)

−0.578 ***

(0.119)

−2.256

(1.767)

ln(distanceij) −

−0.856 ***

(0.302) −

ln(incgapijt) −0.326 ***

(0.108)

−0.357 ***

(0.091)

−0.326 **

(0.126)

ln(techdistijt) −0.293 ***

(0.050)

−0.283 ***

(0.050)

−0.293 **

(0.134)

language −

0.390

(0.315) −

border −

0.063

(0.361) −

F-test 85.29 80.62

LM test 726.06

Hausman test 35.47

R2 within 0.45 0.44 0.45

R2 between 0.01 0.84 0.01

R2 overall 0.03 0.74 0.03

No. of obs (NT) 660 660 660

No. of bilateral (N) 30 30 30

***, ** and * are significant at the 1%, 5% and 10% levels respectively and standard errors

are presented in parentheses. a For the third column, the figures in parentheses are the cluster-robust standard errors for the

fixed effects estimator.

Given the results of these specification tests, the interpretation is focused on the

fixed effects model. Reg. I shows that the results of the fixed effects estimation are

consistent with economic theory and expectations. The positive and significant signs of

the GDP of both the exporting and importing countries imply that wealthier countries

trade more. On the contrary, the coefficients of the population variables are negative

and significant, showing that large domestic markets represent bigger absorption effects

and less reliance on imported goods. Income gap is also statistically significant and

negatively correlated with exports, favouring the prevalence of the Linder hypothesis in

19

the ASEAN member countries. In other words, the ASEAN member countries that have

more similar levels of income per capita tend to trade more. Moreover, the coefficient

of technological distance shows a positive and significant sign at the 1% level. This

result is consistent with that of Filippini and Molini (2003) and implies that greater

technological gaps discourage bilateral trade flows. In addition to these time-variant

variables, there are three time-invariant variables in the model: geographical distance,

language and border. Nevertheless, these variables are dropped from the model because

the fixed effects model does not allow for estimating time-invariant variables.

To ensure that this result is consistent and unbiased, it is necessary to confirm

that the estimates do not suffer from serial correlation and/or heteroscedasticity. The

presence of either of these two problems violates one of the assumptions of the fixed

effects model: constant variance, i.e. V(ui|Xi, ci) = I, where > 0 and < ∞.

Therefore, the Wooldridge test for autocorrelation and the modified Wald test for

groupwise heteroscedasticity were conducted. The results show strong rejections of the

null in both tests and therefore confirm the presence of serial correlation and

heteroscedasticity in the data (see Appendix 1 for details). As the consequence, the

result of fixed effects estimation becomes inconsistent, and the estimated variances (and

thus standard errors) are no longer valid. To correct these problems, this dissertation

follows the approach used by Melitz (2007) by treating each country pair as a cluster in

order to estimate the correct standard errors with the Huber/White cluster-robust

covariance estimator.

The results of this model are shown in the third column of Table 3 (Reg. III).

Since this estimation causes the standard errors to be bigger, some variables become

insignificant, even though the signs remain the same. The coefficient of the exporting

country’s GDP becomes statistically insignificant. However, the coefficient of the

20

importing country’s GDP remains significant at the 1% rejection level. The populations

of both the exporting and importing countries are also no longer statistically significant.

Nevertheless, income gap and technological distance are still significant at the 5%

rejection level. The F-test also strongly rejects the null, which justifies the presence of a

correlation between the explanatory variables and heterogeneous effects in errors.

In short, this section of the dissertation has investigated the determinants of

intra-ASEAN trade. The estimation results in this part show that the importer’s GDP is

positively correlated with trade flows and statistically significant. In addition, it also

shows that more similar levels of technological advancement and per capita income will

increase the flow of trade between countries.

4.2. AFTA: Trade Diversion or Trade Creation?

Now, the focus of the estimation shifts from intra-ASEAN trade to extra-ASEAN trade.

Thus, the objective of the following estimations is to evaluate the impact of AFTA on

its member countries and their trade interrelationships with other East Asian countries.

Therefore, in addition to the ASEAN 6 member countries, it also includes three

additional non-member countries: China, South Korea and Japan. Since this data set is

different from the previous one, another Levin, Lin and Chu (2002) test for the unit root

is conducted. Table 4 shows the results of this test. Similar to the previous results, this

test significantly rejects the null of the unit root for all variables. As the stationarity of

the series are confirmed, this dissertation can now proceed to the panel data estimation,

without conducting a cointegration test.

Table 4. Levin Lin Chu test for panel unit root (ASEAN 6 + 3 data set)

Variables t-star t-star p-value

ln(tradeijt) −15.670 −8.605 0.000

21

ln(gdp) −9.828 −5.503 0.000

ln(pop) −4.547 −3.169 0.000

ln(techdistijt) −32.947 −26.506 0.000

ln(incgapijt) −10.287 −3.287 0.000

Table 5 presents the panel estimation results of Equation (6) that are augmented

with the AFTA and country dummy variables. First, the estimation is focused on the

impact of AFTA on the trade activities of the ASEAN member countries. To do this

estimation, three dummy variables are included in the model: AFTA, AFTAim and

AFTAex. Later, the model includes country dummies to investigate the changes in trade

patterns with China, Japan and South Korea.

Table 5. Modified gravity equation: Trade creation and trade diversion Variables Reg. IV Reg. V Reg. VIa Reg. VII Reg. VIII Reg. IXb

Constant −33.906 ***

(5.419)

−31.081 ***

(2.336)

−33.906 ***

(21.256)

−41.305 ***

(4.658)

−26.916 ***

(1.881)

−26.916 ***

(13.621)

ln(gdpit) 1.276 ***

(0.108)

1.288 ***

(0.074)

1.276 ***

(0.346)

1.036 ***

(0.120)

1.244 ***

(0.065)

1.244 ***

(0.083)

ln(gdpjt) 1.765 ***

(0.108)

1.556 ***

(0.074)

1.765 ***

(0.248)

1.438 ***

(0.120)

1.388 ***

(0.065)

1.388 ***

(0.138)

ln(popit) 0.352

(0.369)

−0.349 ***

(0.078)

0.352

(1.613)

0.457

(0.341)

−0.326 ***

(0.064)

−0.326 ***

(0.104)

ln(popjt) −1.616 ***

(0.369)

−0.484 ***

(0.078)

−1.616 **

(0.760)

−0.464

(0.395)

−0.387 ***

(0.064)

−0.387 ***

(0.122)

ln(distanceij) −

−0.654 ***

(0.228) − −

−0.755 ***

(0.169)

−0.755 ***

(0.264)

ln(incgapijt) −0.185 ***

(0.057)

−0.240 ***

(0.052)

−0.185 **

(0.088)

−0.180 ***

(0.056)

−0.243 ***

(0.050)

−0.243 ***

(0.068)

ln(techdistijt) −0.040

(0.026)

−0.038

(0.026)

−0.040

(0.089)

−0.032

(0.025)

−0.019

(0.026)

−0.019

(0.087)

language −

1.271 ***

(0.360) − −

1.238 ***

(0.271)

1.238 ***

(0.380)

border −

0.275

(0.505) − −

0.240

(0.381)

0.240

(0.549)

AFTA 0.551 ***

(0.164)

0.512 ***

(0.161)

0.551 **

(0.240) − − −

AFTAim 0.024

(0.113)

−0.059

(0.103)

0.024

(0.136) − − −

22

AFTAex −0.510 ***

(0.113)

−0.324 ***

(0.103)

−0.510 **

(0.255) − − −

China03-09 − − −

0.518 ***

(0.111)

0.482 ***

(0.098)

0.482 **

(0.206)

Japan03-09 − − −

−0.195 **

(0.085)

−0.213 **

(0.088)

−0.213 **

(0.085)

Korea03-09 − − −

−0.097

(0.091)

−0.075

(0.090)

−0.075

(0.073)

F-test 221.04 117.57 223.04

LM test 7377.67 7832.72

Hausman

test

37.78 15.23

R2 within 0.57 0.57 0.57 0.57 0.57 0.57

R2 between 0.19 0.70 0.19 0.48 0.70 0.70

R2 overall 0.21 0.67 0.21 0.47 0.67 0.67

No. of obs

(NT) 1584 1584 1584 1584 1584 1584

No. of

bilateral (N) 72 72 72 72 72 72

***, ** and * are significant at the 1%, 5% and 10% levels respectively and standard errors are presented in parentheses. a For the third column, the figures in parentheses are the cluster-robust standard errors for the fixed effects estimator. b For the sixth column, the figures in parentheses are the cluster-robust standard errors for the random effects estimator.

Similar to the test results in the previous estimation, the results of the Hausman

test imply that the fixed effects model is the preferred model for this estimation. Hence,

the results interpretation is more focused on the fixed effects model (Reg. IV). Overall,

the results are consistent with the previous estimation. The coefficients of the GDP’s of

both the exporting and importing countries remain significant at the 1% level. The

coefficient of the population of the exporting country becomes positive, but statistically

insignificant. Nevertheless, the population of the importing country remains negatively

correlated with exports and significant at the 1% level. Income gap has a negative

coefficient and is statistically significant, implying that the presence of the Linder

hypothesis is still remarkable in the East Asian context. Technological distance,

however, is statistically insignificant, despite the negative coefficient.

The AFTA dummy, as expected, is significant and has a positive sign, thus

implying the presence of trade creation. The AFTAim dummy, however, is not

23

significant, despite the positive coefficient. This result shows that there is no evidence

of extra-regional import bias after the establishment of AFTA. On the other hand,

AFTAex is statistically significant with a negative sign, which implies export trade

diversion after the establishment of AFTA in 1992. In other words, this result suggests

that there has been a shift in the pattern of exports since some trade flows between the

ASEAN member countries and non-member countries were replaced with trade flows

amongst the ASEAN member countries.

Next, specification tests are conducted to check for the presence of

heteroscedasticity and serial correlation. This time, both heteroscedasticity and serial

correlation also exist because the nulls of homoscedasticity and no serial correlation are

strongly rejected at the 1% level (see Appendix 1.2). Hence, similar to the previous

cases, the model is re-estimated with the cluster-robust covariance estimator to obtain

the correct standard errors. The results of this estimation are shown in the third column

of table 5 (Reg. VI). Even after treating the problems of heteroscedasticity and serial

correlation, the results do not show any significant changes. All variables that were

significant in the ordinary fixed effects model are also significant in the clustered fixed

effects model. In short, this result confirms that there was trade creation among the

ASEAN member countries after the establishment of AFTA in 1992. Furthermore, it

also verifies the presence of export diversion, because the coefficient of AFTAex

remains statistically significant after the cluster treatment.

The last estimation in this dissertation includes all the country dummies in the

model and also follows the same estimation procedure and specification tests. After

estimating both the fixed effects (Reg. VII) and random effects (VIII) models, the

Hausman test is conducted. This time, the test fails to reject the null at the 5% level.

Therefore, the random effects estimators are used because the fixed effects estimators

24

have become inefficient. To support this result, the Breusch-Pagan Lagrange Multiplier

test is undertaken and the result rejects the null significantly at the 1% level, implying

that there is not equality of the individual effects, i.e. it accepts the presence of random

effects. Consequentially, the random effects model is used because it does not model

each individual effect explicitly. Moreover, similar to the previous results, the

specification tests show that heteroscedasticity and serial correlation are also present in

this estimation (see Appendix 1.3). As a consequence, the random effects model is re-

estimated using the Huber/White cluster-robust covariance estimator in order to obtain

the correct standard errors.

The results are presented in the last column of table 5 (Reg. IX). Despite the

bigger standard errors due to the clustering treatment, most of the variables are still

significant. The GDP coefficients of both the exporting and importing countries are

significant at the 1% level. The population coefficients are also statistically significant

with negative signs. However, a closer investigation shows that the magnitude of the

importing country’s population is much smaller as compared to that in Reg. VI. Other

variables show relatively similar results as compared to Reg. VI: income gap is

statistically significant with a negative coefficient, while technological distance is

statistically insignificant.

In addition, since the random effects model can estimate the time-invariant

variables, it takes into account the variables that are dropped from the fixed effects

model. These variables are geographical distance, language and borders. The coefficient

of geographical distance appears to be negative and statistically significant, as expected.

Hence, this result suggests that greater geographical distance causes higher

transportation costs and thus discourages bilateral trade between two countries. In

contrast, language, which represents cultural distance, is positively correlated with

25

exports and significant at the 1% level. In other words, language similarity allows both

countries to communicate more easily, hence it eases the process of trade transactions.

Another time-invariant variable is border. However, it is statistically insignificant, even

though it has a positive coefficient.

Another important factor is the country dummies. The coefficient of China is

positive and statistically significant. The coefficient of Japan is negative and significant

at the 5% level. This result is evidence that, from 2003 to 2009, Japan exhibited a

general negative propensity to export to and import from East Asian countries. On the

other hand, the rise of China has enabled it to take over larger shares of East Asian

markets from Japan during the same period of time. Lastly, the Korea dummy shows a

positive sign, but is not statistically significant.

To sum up, this section of the dissertation has shown the determinants of trade in

the ASEAN 6 + 3 economies. Most of the included variables are statistically significant

and have the expected signs. Moreover, it also detected the presence of both trade

creation and trade diversion after the establishment of AFTA, even though the

magnitude of trade creation is larger than that of trade diversion. Further, it shows

intensified trade flows between China and other East Asian partners between 2003 and

2009.

5. Further Discussions and Analysis

This part presents further discussion to clarify some of the issues found in the previous

section. First, even though the fixed effects estimations cannot include time-invariant

variables in the model, the random effects estimation (Reg. IX) shows that geographical

distance and language are significant determinants of trade in the context of the ASEAN

6 + 3 economies. While geographical distance represents the main impediment to trade

flows, language similarity increases the flow of trade. One remarkable fact is that, even

26

though global transportation infrastructure has been improving rapidly in the last

decades, some ASEAN member countries still have inadequate transportation

infrastructure. Improved transportation infrastructure, such as providing more efficient

customs, logistics and transportation procedures, may be able to reduce the effect of

geographical distance as an obstacle to trade. On the other hand, the positive coefficient

of language implies that two countries that speak the same language will be able to

communicate and conduct international transactions efficiently and thus increase trade

flows

The second discussion regards the significance of income gap. The negative

relationship between trade and income gap shows that this estimation is in favour of the

Linder hypothesis not only in the case of trade among the ASEAN 6 countries, but also

in trade interrelationships among the ASEAN 6 + 3 countries. Therefore, this result

supports most of previous studies that tried to detect the presence of the Linder

hypothesis in the flows of trade (see e.g. Thursby and Thursby, 1987; McPherson et al.,

2001; Hallak, 2006). In this regard, however, McPherson et al. (2001) point out that

there is one important note that is worthy of consideration regarding the inclusion of the

income gap variable into the gravity equation. In their paper, McPherson et al. (2001)

argue that the Linder hypothesis was originally intended to be applicable only to

manufactured goods. Thus, in the case where manufactured goods do not play an

important role in the flows of trade, the use of the income gap variable may lead to a

biased conclusion in regard to accepting or rejecting the presence of the Linder

hypothesis. In the case of the East Asian region, rapid industrialization in most East

Asian countries, which was followed by improved technological capability, has made

the region able to increase the share of manufactured goods in terms of total exports.

According to UN COMTRADE database (2011), manufactured goods accounted for

27

more than 60% of total traded commodities between 1988 and 2009 in ASEAN + 3

countries. As the consequence, this large share of manufactured goods in terms of total

exports confirms that the test to check for the presence of the Linder hypothesis is

appropriate for this study.

Third, unlike the income gap, technological distance is only significant in Reg.

III. One possible explanation for this result is due to the limited availability of data. As

mentioned earlier, the use of linear interpolation in filling in the missing values might

affect the data quality and estimation result. From a theoretical point of view, however,

this result requires further investigation.

Lastly, the discussion is aimed at analysing the effects of AFTA on trade in East

Asia. Kitwiwattanachai et al. (2010) point out that FTA’s in general result in both trade

creation and trade diversion, and if there are more countries that join the FTA, then both

of these effects tend to become larger. Therefore, what is more important is to examine

whether the establishment of AFTA has increased or decreased net welfare. The net

change in welfare can be obtained by calculating the difference between trade creation

and trade diversion. Reg. VI shows that the establishment of AFTA has increased intra-

regional trade flows to a higher level of 0.551, which implies that the intra-ASEAN 6

trade flows have increased by 73.5% since the establishment of AFTA, i.e. this result

shows that AFTA partners prefer to trade with other AFTA partners. This result is

smaller than that of the study by Elliott and Ikemoto (2004), but consistent with a more

recent study by Kien and Hashimoto (2005). One possible explanation for this

difference is partly due to the pooled cross-sectional model used by Eliiott and Ikemoto

(2004). As mentioned earlier, pooled cross-sectional models neglect the unobserved

heterogonous factors in errors. Thus, they may lead to a different estimation result.

28

In addition to trade creation, the estimation in this dissertation also shows the

presence of export trade diversion after the establishment of AFTA. The results in Reg.

VI exhibit the fact that extra-regional exports under AFTA have decreased to a lower

level of 0.510. In other words, it implies that AFTA has reduced export flows from the

ASEAN 6 economies to China, Japan and South Korea by as much as 66.5%. This

result meets the expectation of Cuyvers and Pupphavesa (1996) and supports the finding

of Hapsari and Mangunsong (2006), but is inconsistent some other studies (Elliott and

Ikemoto, 2004; Kien and Hashimoto, 2005). Elliott and Ikemoto (2004) argue that the

lack of any trade diversionary effects in their study is due to changes in the real

exchange rates of the ASEAN member countries after the 1997 Asian Financial Crisis.

The devaluation of the ASEAN currencies following the crisis that struck in 1997

caused exports to become less expensive and imports to become more expensive. This

might have increased the products competitiveness of the ASEAN member countries

and increased the flows of exports from these countries to other East Asian countries.

Nevertheless, their paper uses data that only cover the period between 1983 and

1999. Therefore, the estimation results only capture the impact of AFTA until two years

after the 1997 Asian Financial Crisis, when the ASEAN economies were still severely

affected by the crisis. On the other hand, this paper covers a longer time period. Thus, it

captures the effects of AFTA over a longer term in order to give a more comprehensive

understanding. Appendix 2 shows that, after they recovered from the crisis, there was a

modest currency appreciation in the ASEAN 6 countries between 2003 and 2008 (prior

to the 2008 Global Financial Crisis), particularly in Brunei Darussalam, Malaysia,

Singapore and Thailand. This appreciation might have reduced the export

competitiveness of the ASEAN member countries and led to export trade diversion.

Another explanation is simply that countries are usually averse to FTA’s in which they

29

are not included (Kitwiwattanachai et al., 2010). In this case, since China, Japan and

South Korea are not member countries of AFTA, this aversion occurs and causes the

trade diversionary effect.

In summary, from a welfare perspective, AFTA has increased the welfare of its

member countries through trade creation and simultaneously reduced the welfare of

non-member countries through trade diversion, particularly if the diversion is caused by

the shifting of the more efficient countries from outside the bloc to the less efficient

countries inside the bloc. Nevertheless, from the perspective of the overall East Asian

region, the net welfare impacts of AFTA are still welfare-increasing because the effects

of trade creation are still bigger than those of trade diversion.

6. Conclusion

This study has revealed important findings about the main determinants of trade in the

ASEAN 6 countries and the impacts of AFTA on East Asian trade flows. The main

finding of this paper shows that AFTA has brought many benefits to its member

countries through the rapid creation of trade. Nevertheless, the benefits to its member

countries have come at the expense of non-member countries, in this case: China, South

Korea and Japan. In order to minimise these trade diversionary effects on other East

Asian counties, this paper proposes the establishment of a broader scope FTA, such as

an ASEAN 6 + 3 FTA. Fortunately, an attempt at achieving this goal has been made by

expanding the FTA in the East Asian region through the aggressive formation of AC-

FTA. Therefore, further studies should investigate whether AC-FTA can reduce the

impacts of trade diversion on the East Asian region. In addition, it also should be

facilitated by more complete data availability in order to allow for a more sophisticated

study and obtain more efficient results.

30

1 Newer ASEAN members (Cambodia, Lao PDR, Myanmar and Vietnam) also joined AFTA in

the following years. These countries were given some flexibility in achieving its goals, due

to their less developed economies.

2 The complementary index is a variable used to examine whether factor endowments between

two countries are complementary. The positive and significant result of this variable in the

equation shows that higher complementariness of factor endowments of two countries

intensifies bilateral trade in these countries (for instance, see Deardorff, 1984; Ng and

Yeats, 2003).

3 In their paper, Evenett and Keller (2002) contend that product specialisation in the IRS models

occurs for arbitrary differences in factor proportions, whereas H-O models require large

differences in factor proportions to encourage product specialisation. Since the assumption

of large factor proportions differences is unnecessary, they suggest that IRS models are

preferred in the gravity equation context.

31

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Appendices

Appendix 1. Specification Tests

Appendix 1.1. Specification tests for Reg. I – Reg. III

Wooldridge test for serial correlation in panel data

Null Hypothesis F-test p-value

No first order serial correlation 74.219 0.000

Modified Wald test for groupwise heteroscedasticity

Null Hypothesis χ2 p-value

Constant variance (homoscedastic) 32508.65 0.000

Appendix 1.2. Specification tests for Reg. IV – Reg. VI

Wooldridge test for serial correlation in panel data

Null Hypothesis F-test p-value

No first order serial correlation 83.733 0.000

Modified Wald test for groupwise heteroscedasticity

Null Hypothesis χ2 p-value

Constant variance (homoscedastic) 61364.21 0.000

37

Appendix 1.3. Specification tests for Reg. VII – Reg. IX

Wooldridge test for serial correlation in panel data

Null Hypothesis F-test p-value

No first order serial correlation 82.832 0.000

Modified Wald test for groupwise heteroscedasticity

Null Hypothesis χ2 p-value

Constant variance (homoscedastic) 87333.84 0.000

38

App

reci

atio

n

Dep

reci

atio

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Appendix 2. ASEAN 6 Official Exchange Rates

Source: World Bank World Development Indicators online database

0!

0.5!

1!

1.5!

2!

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Brunei Darussalam!

0!

2000!

4000!

6000!

8000!

10000!

12000!

1993!

1994!

1995!

1996!

1997!

1998!

1999!

2000!

2001!

2002!

2003!

2004!

2005!

2006!

2007!

2008!

2009!

Indonesia

0!0.5!1!1.5!2!2.5!3!3.5!4!4.5!

1993!

1994!

1995!

1996!

1997!

1998!

1999!

2000!

2001!

2002!

2003!

2004!

2005!

2006!

2007!

2008!

2009!

Malaysia!

0!

10!

20!

30!

40!

50!

60!1993!

1994!

1995!

1996!

1997!

1998!

1999!

2000!

2001!

2002!

2003!

2004!

2005!

2006!

2007!

2008!

2009!

Philippines!

0!

0.5!

1!

1.5!

2!

1993!

1994!

1995!

1996!

1997!

1998!

1999!

2000!

2001!

2002!

2003!

2004!

2005!

2006!

2007!

2008!

2009!

Singapore!

0!

10!

20!

30!

40!

50!

1993!

1994!

1995!

1996!

1997!

1998!

1999!

2000!

2001!

2002!

2003!

2004!

2005!

2006!

2007!

2008!

2009!

Thailand!