Discussion Papers In Economics
And Business

ECONOMIC ANALYSIS OF ASEAN FREE TRADE AREA;
BY A COUNTRY PANEL DATA
Nguyen Trung Kien, Yoshizo Hashimoto

Discussion Paper 05-12

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANECONOMIC ANALYSIS OF ASEAN FREE TRADE AREA;
BY A COUNTRY PANEL DATA
Nguyen Trung Kien, Yoshizo Hashimoto

Discussion Paper 05-12

May 2005

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANECONOMIC ANALYSIS OF ASEAN FREE TRADE AREA；
BY A COUNTRY PANEL DATA*

Nguyen Trung Kien, Yoshizo Hashimoto†
Osaka School of International Public Policy
Osaka University
January 2005

Abstract

This paper is an attempt to use the Hausman-Taylor (HT) estimation to examine thedeterminants of trade flows of Asean Free Trade Area (AFTA). Based on the two-way
error component form of the gravity model, we employ the HT technique to estimate
export flows by a country panel data of 39 countries for the period 1988-2002. The
estimations have shown the following important results. Firstly, export flows among two
countries increase more proportionately with GDPs. More interestingly, trade is higher
between countries of identical preferences than those of different ones. Secondly, themostcrucial result is that AFTA has only produced the trade creation among its

members. Finally, this study suggests the importance of trade facilitation policy tosupport the targets of FTA.
Key words: AFTA, gravity models, panel data, trade.

JEL Classification: F1, F15, C23*

The authors would like to thank Professor Kenzo Abe for the valuable comments and suggestions, and participating
members of seminars at Osaka School of International Public Policy and Graduate School of Economics, Osaka
University, for their useful questions and opinions. However, we are solely responsible for any error to be left .
†Address: Osaka School of International Public Policy, Osaka University, 1-31 Machikaneyama-cho, Toyonaka-shi,
Osaka 560-0043, Japan. E-mail: kien@osipp.osaka-u.ac.jp.

iI. Introduction
Whether the formation of ASEAN1 Free Trade Area (AFTA) has intensified intra-regional tradein South East Asian region is still a controversy in the literature though AFTA has been in place for a
decade since 1993. For the one thing, some studies argue that intra-regional trade in ASEAN had
been strengthened in the 1990s (see Elliott & Ikemoto, 2004). What is more, is that these arguments
have not been consistent with other studies such as Endoh (2000) and Tran Van Tho (2003). More
importantly, Krugman (1991) argues that the formation of Free Trade Area 2

fromgeographically-close countries would stimulate intra-regional trade; the closer are countries to eachother, the larger the trade volume between them. Therefore, the purpose of this paper is to examine
the determinants of trade flows of AFTA members in the interdependent context with other FTAs in
the world. Furthermore, we are particularly interested in what kinds of trade effects does AFTA bringin play among its members and non-members.
In recent years, there has been renewal of interest in using a gravity model to estimate thedeterminants of trade flows, especially in the context of regional trade groupings. Typical examples
of application of the gravity model to investigate trade flows in the selected regional tradearrangements inclusive of AFTA consist of Endoh (2000) and Elliott & Ikemoto (2004), which havebeen estimated in a cross-section approach. Only few attempts have sofar been made by a panel dataapproach (See Cheng & Wall, 2002; Martinez-Zarzoso & Nowak-Lehmann, 2003).
In this paper, we develop an empirical specification of the gravity model by the panel data. Ourmodel is related to several recent papers. Endoh (2000) employed the gravity model with someinstitutional dummy variables to examine the feature and transition of trade relations in Asia-Pacificregion through cross-section data analysis, but the roles of heterogeneity in spatial and time effects
which can explain trade flows are neglected. Our two-way error component model concerns these
1
2

The Association of South East Asia Nations.
Hereafter referred to as FTA.

1

heterogeneous effects. On the other hand, Elliott & Ikemoto (2004), using the modified gravity
model, investigated AFTA intra-and extra-regional bias in bilateral trade flows for several distinct
time periods, but they do not explicitly mention a role of exchange rate on trade flows. Therefore,
we add both exchange rates and common languages as factors affecting transaction costs of trade in
our empirical model over a long continuous period. It is easy to make a transaction if two countries
share a similar language since the language factor can facilitate communication between them.
Based on this two-way error component form of the gravity model, we employ the
Hausman-Taylor estimation for the panel data of export flows among 39 countries over the period
1988-2002. Then, we provide different estimations in various contexts in which AFTA isinterdependent with other FTAs. Furthermore, the context which incorporates FTA impacts provides
us insights on what kinds of effects does AFTA influence export flows.
The paper is organized as follows. Section II mentions an overview of trade movement of AFTA .
Section III reviews gravity models of international trade and develops an empirical model. The
method, data, regression results and discussions are shown in Section IV. Finally, Section V presents
conclusions.
II. An overview of trade movement of AFTA
The ASEAN Free Trade Area (AFTA), including Brunei, Indonesia, Malaysia, the Philippines,
Singapore and Thailand, was established in January 1992 in order to strengthen economic
cooperation between ASEAN members. This agreement provided for the elimination of tariffs andnon-tariffs barriers within the 15 years from January 1994 with the completion date set at 2008.
Until then, tariffs will be reduced to less than 5%. The main instrument of the tariff reduction
scheme is the Common Effective Preferential Tariff (CEPT), covering manufactured goods,
agricultural products as well as services. However, following the financial crisis in 1997, the summit
meeting of ASEAN in December 1998 decided to further speed up the trade liberalization by2

accelerating the time-frame to 2002. In addition, in the mid-1990s four more countries joined
ASEAN: Vietnam in 1995, Myanmar and Lao PDR in 1998 and Cambodia in 1999. They are also
participating in AFTA with deadlines set for Vietnam in 2006, Myanmar and Lao PDR in 2008 andCambodia in 2010. In summary, AFTA that currently has a membership of ten countries and a
population of over 500 million has been in place for above a decade up to now.
Appendix A examines 1992-2002 changes in the direction of exports and imports of AFTA withother FTAs as well as its major trading countries. Overall, AFTA intra-regional trade shares for both
exports and imports increased significantly during 1992-2002, thus pointed out increased
dependency on intra-regional trade. Moreover, AFTA as a whole has become increasinglyinterdependent in trade with outside trading partners. On one hand, the United States as a key in
NAFTA, EU and Japan have been AFTA’s largest export markets. On the other, Japan, followed by
the U.S and EU, has been the largest source of AFTA imports. In summary, while increased
dependency on the intra-regional trade seems explicitly reflected in both directions of export andimport, it is a matter whether or not AFTA is really inclined to be less reliance on extra-regional
markets for its exports as well as imports.
III. Gravity models of International Trade
1.

Gravity Models

In international trade, the gravity model was first introduced by Tinbergen (1962) and Poylonen(1963) mainly to account for the patterns of bilateral trade flows among the European countries.
Accordingly, exports from a country i to a country j are explained by their economic sizes (measured
by GNP or GDP) and the geographical distance between them. Since then, the gravity model has
been widely used and increasingly improved in empirical studies of international trade. Specifically,
a population variable was incorporated to show a negative effect of it on trade flows (Oguledo &MacPhee, 1994 and Endoh 1999, 2000).

3

Furthermore, other development used a per capita income variable to provide a good proxy forthe level of economic development that has its positive effect on international trade (Frankel, Steinand Wei, 1995 and Elliott & Ikemoto, 2004). It should be noted that several kinds of dummy
variables have been added in the gravity models to account for specific factors that can support orhinder bilateral flows of goods such as geographical factors, cultural factors and institutional factors.
While gravity models used in empirical applications are known for their strong fit to the data, it
is in recent decades that their predictive ability began to be justified by many theoretical studies ofthe model. Since Anderson (1979) - the first attempt to provide theoretical foundations of the gravity
model, scholars have tried to demonstrate that the gravity model can be derived as a foundation fromvarious trade theory models such as Ricardian models, Heckcher-Ohlin models and Increasing
Returns to Scale (IRS) models; see,Bergstand (1985, 1989), Helpman & Krugman (1985) among

others. Especially, it is today recognized that the gravity equation can provide a useful multivariate
approach for assessing the impacts of regional trade agreements on the level and direction ofbilateral trade flows. Illustrations of the application to the analysis of the preferential tradeliberalization are refered to Aitken (1973), Endoh (1999, 2000) among others.
Aitken (1973) first included a dummy variable showing intra-regional trade to capture tradecreation between members. Later, other studies tried to add a second dummy variable to illustrate
extra regional trade effect with non-members. Recently, Endoh (1999) made good progress toincorporate 3 dummy variables in order to offer a simple and clear distinction between the tradecreation and trade diversion. We will follow his institutional dummy treatment; i.e. the first dummy
capturing the trade creation, the second one the import trade diversion and the third one the export
trade diversion.
2.

Specification of Empirical Model

We now look at the issue of econometric specification more closely. In most of previous

4empirical studies based on the cross section estimation techniques, exports from i country to j (Xij)are specified by the typical gravity equation for a given year as follows:

log X ij = β 0 + β 1 log Yi + β 2 log Y j + β 3 log Dij + log u ij

(1)

where Yi (Yj) indicates the GDP of the exporter (importer), Dij measures the geographic distance
between the two countries’ capital cities. The disturbance term log(uij) is assumed to be iid with zeromean E(log(uij)) = 0 and a constant variance.
However, it is remarkable to observe that heterogeneous factors may influence bilateral trade. An
exporting country would export different amounts of a certain good to two countries, even if GDPsof these two countries are identical and they have the same distance from the exporter. Therefore, thecross-section OLS estimations are likely to suffer from heterogeneous bias because they explicitly
fail to consider heterogeneous structural factors in errors. Furthermore, Matyas (1997) claims thatthe gravity model based on the specification (1) is misspecified.
Taking into account of unobserved heterogeneous factors in errors, an econometric specificationof the gravity model in our paper is a two-way error component model as follows:

log X ijt = β0 + αij + θt + β1 logYit + β2 logYjt + β3 log Dij + β4 log Nit + β5 log N jt +β6 log FXijt + β7 LANij + ∑ β8k FTA(ijk ) + ∑ β9k imFTA(ijk ) +∑ β10k exFTA(ijk ) + log uijt
t

t

k

k

(2)

t

k

where i, j denote the exporter, the importer respectively, t denotes time and k denotes the number of
FTAs. The explanatory variables are defined as:
Xijt denotes the value of export from a country i to a country j at time t.
Yit ,Yjt denote the GDP of country i, j at time t, respectively.
Dij denotes the geographic distance between capitals of two countries.
Nit , Njt denote the population of country i, j at time t, respectively.
FXijt denotes the exchange rate between country i and country j at time t.
LANij is a dummy variable which is unity if the two countries speak the same official language.

5

FTAijk is a dummy variable which is unity if both countries i and j belong to FTA k at time t, and0 otherwise.
imFTAijk similarly is a dummy variable which is unity if only the import country j belongs to
FTA k at time t, and 0 otherwise.
exFTAijk likewise is a dummy variable which is unity if only the export country i belongs to FTA
k at time t, and 0 otherwise.
β0 is an unknown constant.
αij is individual effects which imply direction of export effects from country i to country j.
θt is time-specific effects.
uijt is the log normally distributed idiosyncratic error term, where E(log(uijt)) =0.
Our econometric specification in (2) is different from the previous empirical models by Endoh(2000), Elliott & Ikemoto (2004) in two main issues. First of all, we take into account of unobserved
heterogeneous factors in errors, namely specific direction of export effects αij and specific timeeffects θt in the econometric model. The specification built in this way allows us to deal with both
spatial and time dimension. Whereas the time dimension makes it possible to monitor common
business cycle or globalization trend over our whole period, the spatial dimension has its ability tocapture time invariant direction of export effects. Therefore, our panel data approach leads us to a
totally different model in an econometric specification. As can be seen, Matyas (1997) and Egger(2000) strongly support the two-way error component one.
Secondly, we add two new variables to the explanatory in the model. The one is a common
language dummy. We are interested in language since this factor plays a role as cultural distance.
When two countries speak the same language, it will make communication easy and reducetransaction costs between them. Therefore, language proximity can promote bilateral trade. The
other is an exchange rate of which impact are easily estimated in the panel data approach.

6

Expectations for all explanatory variables would indicate as follows. Yi and Yj would havepositive coefficients, since the positive correlations between GDP and export supply as well as
import demand are expected. Coefficient on Dij would be expected to be negative, given that greater
distances increase transportation costs.
Next, Ni and Nj would be likely to be positive or negative, depending on which is dominant ofan absorption effect or economies of scale. On one hand, a large population may certainly indicate abig domestic market and large resource endowment, so that the bigger absorption effect of thisdomestic market causes less reliance on international trade transactions. In this case, a negative sign
would be justified. On the other, a large domestic market allows the advantages of economies ofscales to be fully exploited. It follows that opportunities for trade with foreign partners in a wide
variety of goods will increase, and the expected sign of this coefficient would be positive.
In addition, we consider the impact of exchange rate which is defined as the price of the country
j currency expressed in terms of the country i currency unit. A coefficient on FXij is predicted topossess a positive sign since depreciation in the country i will stimulate export flows from thecountry i to the country j.
Furthermore, the cultural distance measured by a dummy LANij would be expected to be positive
because two countries which speak the same official language will have an opportunity to make atransaction smoothly and quickly.
Note that a dummy variable FTA captures the intra regional trade creation. If a coefficient on thisdummy is positive and statistically significant, it can be showed that the members of FTA have
traded with each other more than the hypothetical level predicted by basic explanatory variables. The
dummy imFTA reflects any trade diversion occurring as a result of changes in the import structure of
FTA. It takes account of the import trade diversion defined by Balassa (1967). If a coefficient on thisdummy variable is negative and statistically significant, it can be argued that the members of FTA

7

have diverted their importing activities from non-members trading countries to member countries.
Finally, the dummy exFTA characterizes the extra-regional bias of FTA to the rest of world or theexport trade diversion defined by Endoh (1999). A negative and statistically significant coefficienton the exFTA variable indicates that integration has led a member country to prefer exporting toother members rather than non-members.
IV. Empirical investigations
1.

MethodHaving not considered the panel structure of the data, we can assume that all coefficients in theequation (2) βz, z =1, 2 ...are the same for all years. Therefore, we can pool all cross-section data tohave OLS estimators where β0, αij and θt can not be separately identified. However, the pooled OLS
estimators obtained by that way do not deal with the heterogeneous issue.
Hence, a proper estimation method should take both direction of export effects αij and timeeffects θt into consideration. Directions of export effects allow each direction of export to have its
own effects for unobserved time-invariant historical, cultural and diplomatic influences. It is clearthat αij showing direction of export from a country i to a country j is totally different from αji. These
unobserved effects might be correlated with some of the explanatory variables. On the other hand,time-specific effects imply unrelated influences on direction of export such as potential trend or
business cycle. Since most of these influences are usually unobservable, it is appropriate to include
these effects in the econometric model in order to control them.
To take into account heterogeneous effects in errors in our two-way error component model,
there are basically two available models: a fixed effects model (hereafter, referred to FEM) and arandom effects model (referred to REM). We use the Hausman test to examine whether the
specification of REM is correct.

8

It is interesting to note that we select the dynamic way3 to follow FTA introduction, in which a
set of FTA institutional dummies takes unity only for the effective years of FTA between two trading
countries. This way allows us to catch up the dynamic effect of the formation, expansion orcontraction of FTA.
We pay particular attention to the fact that this model contains not only time varying variables
such as GDPs, population, exchange rate and sets of FTA dummies but also time invariant variables
namely distance and language. It is worth noting that FEM does not allow for estimating these timeinvariant variables. On the contrary, REM has a merit of treating this kind of explanatory variables,
but produces inconsistent estimators since it neglects possible correlation between unobserved
effects in errors and the explanatory variables. A technique that can utilize this merit of REM andcorrect a plausible correlation of the heterogeneous effects in errors with the explanatory variables,
will be a good solution. Hausman and Taylor’s estimator (1981) suggests a way to solve this issue,
provided that a subset of the explanatory variables is correlated with the unobserved individual
effects (Greene, 2003).
Employing the Hausman and Taylor (henceforth HT) instrumental variable estimation techniquerequires us to clarify endogenous and exogenous variables in our model.4 We assume that Yi, Yj
correlate with unobserved direction of export effects αij while LANij has the same endogenous
property. Other remaining variables are exogenous, uncorrelated with αij. Furthermore, we identify
instrumental variables as follows. First, the time-varying variables are instrumented by thedeviations from their group means. The exogenous time-invariant variable Dij serves as its own
instrumental. Lastly, the endogenous time-invariant variable, the language dummy, is instrumented

3

An antimode way is different. It allows these variables to take a value of one for an overall observed period no matter when

FTAs occur. Note that AFTA has been effective since 1993, NAFTA started in 1994, and that MERCOSUR in 1995.
4

This model satisfies the identification requirement since the number of exogenous time-varying variables is bigger than thenumber of endogenous time-invariant ones.

9

by the group means for the exogenous time-varying variables (Ni, Nj, FXij).
2.

Regression results

In order to provide empirical results, we investigate a data set covering 39 countries of which 26
are members of four FTAs, namely, EU, AFTA, NAFTA and MERCOSUR5 for the overall 15-year
period, 1988-2002. Data sources are given in Appendix B.
We have estimation results in two contexts. Firstly, we estimate the gravity model (2) without
including any institutional dummy variables as the benchmark context. Then, we estimate it in a full
specification to measure the impacts of AFTA on its export flows.
2.1

Regression results in the benchmark context.

From Table 1 showing the regression results, we find that all coefficients on the levels of GDPs
and the distance show highly significant estimates at 1% level with the expected sign in all
estimation methods. It is remarkable that the coefficients on GDPs show the income elasticities oftrade in respect to an exporter’s and an importer’s income . At first glance at the pooled OLS result,
1% increase in an exporter’s GDP raises volume of exports on average by about 1.05 % while trade
is inelastic with respect to an importer’s GDP. Then, allowing for direction of export effects in FEM,
REM and HT greatly raises these estimated income elasticities of trade.
Our result shows that the income elasticities of trade are entirely different from Elliott &Ikemoto (2004) but are consistent with the previous studies by Cheng & Wall (2002), andMartinez-Zarzoso & Nowak-Lehmann (2003) who support that controlling the heterogeneous effects
in errors is likely to increase the estimates of GDPs. One possible reason for this is that growth involume of exports is partly attributed to unobserved heterogeneous factors in errors that seemed tobe neglected in the Pooled OLS.

5

Specific names for the set of institutional dummy variables in each corresponding FTAs are denoted as follows: AFTA, imAFTA,

exAFTA; EU, imEU, exEU; NAFTA, imNAFTA, exNAFTA and MER, imMER and exMER.

10

Table 1:

Gravity equation in the benchmark specification

Dependent variable : exports

Pooled OLS

FEM

REM

HT

An exporter ‘s GDP

1.051

1.322

1.140

1.318

(143.46)**(36.53)**(58.21)**(40.70)**

0.934

1.111

1.065

1.110

(128.06)**(84.61)**(92.29)**(86.26)**-0.907--------0.889-0.790(27.88)**(13.25)**

An importer’s GDP

Geographic distance(94.43)**
An exporter’s population

An importer’s population

Exchange rate

Language-0.126-0.026-0.114-0.135(18.79)**(0.79)(6.27)**(5.17)**-0.085-0.270-0.222-0.255(12.57)**(21.69)**(20.30)**(21.06)**

0.022

0.018

0.019

0.018

(10.38)**(8.14)**(8.82)**(8.24)**

1.124--------

1.089

3.054

(10.12)**(2.21)*(34.97)**
Constant-10.879-23.857-13.312-16.851(71.61) **(53.98) **(33.72) **(22.59) **Observations

21901

21901

21901

21901

Adjusted R-squared

0.75

F test (1493, 20400)

49.49

Hausman Test

127.59Hausman Test*

129.45

Notes:

Absolute value of t statistics is reported in parentheses.
* denotes significant at 5%; ** denotes significant at 1%.
All individual effects are not reported.

The coefficient on geographic distance shows that the distance negatively affects export flows.
Controlling for heterogeneous effects considerably lowers this coefficient value except for FEM
result although the effect of distance is still highly significant.
In turn, we actually reconsider specification test of estimation methods. We perform the F-test on
the null hypothesis of the same direction of export effects αij across countries and the same timeeffect θt over time. The F-statistic in Table 1 shows that we can not accept the null hypothesis of11equality of heterogeneous effects in errors. Then the Hausman Test gives us more insights. A highvalue of the statistic which follows the chi-squared test allows us to reject the null hypothesis that
explanatory variables and unobserved heterogeneity are uncorrelated. This leads us to prefer FEM
although time-invariant variables are absorbed into the fixed effects. Importantly, we use Hausman
Test* on the null hypothesis that some explanatory variables are uncorrelated with heterogeneous
effects in errors. Rejecting the null hypothesis further convinces us that this HT estimation is more
appropriate than others in order to satisfy the properties of explanatory variables in our model.
The coefficients on population reflecting for the market size are negative and highly significant
at the HT result. Regarding the exchange rate, its coefficient is generally positive and highly
significant. The time-invariant endogenous variable LANij is also significant at 5% with a positive
sign at the HT result. This result confirms our assumption that language factor can correlate withother unobserved time-invariant effects such as cultural and historical factors. This point also
highlights that two countries sharing common linguistic, cultural or historical features tend to trade
more than what they would otherwise.
2.2

Regression results in the FTA context

Table 2 provides partly the estimates of all explanatory variables similar to the benchmark
context. The estimates of GDPs at the HT result further strengthen our finding that trade increases
lightly more than proportional with the GDPs of the exporter and the importer. A close look at the
HT result reveals that the absolute estimate on distance is bigger than the one in the benchmark
context. This point can infer that the distance becomes more important impediment to export flows
under FTA impacts. One explanation for this may be that the impacts of FTAs have caused trading
countries to prefer doing transaction with their closer ones. Furthermore, the population variables
remain highly significant. At the same time, we find that the impact of an exchange rate is still
effective in spite of a little reduction in its coefficient value. It is noteworthy that the coefficient12

obtained for the language dummy presents an expected sign but appears statistically insignificant.
Table 2: Gravity model in the FTA context
Dependent variable: export
An exporter ‘s GDP

An importer’s GDP

Geographic distance

Pooled OLS

FEM

REM

HT

1.202

1.090

1.117

1.047

(156.92)**(29.20)**(63.56)**(40.50)**

1.008

1.118

1.064

1.088

(132.80)**(86.31)**(96.29)**(89.27)**-0.858-----------0.936-0.950(33.41)**(26.55)**(77.95)**
An exporter’s population

An importer’s population

Exchange rate

Language-0.223-0.030-0.139-0.104(33.54)**(0.93)(8.50)**(5.17)**-0.141-0.280-0.222-0.245(21.00)**(22.91)**(21.18)**(21.88)**

0.027

0.004

0.010

0.008

(13.81)**(1.88)(4.25)**(3.75)**

1.064----------

1.047

0.458

(11.37)**(1.22)(35.08)**Notes:

Absolute value of t statistics is reported in parentheses.
* denotes significant at 5%; ** denotes significant at 1%.
All individual effects are not reported.

One of our main objectives in this paper is an evaluation of FTA impacts on trade relationships.
Table 3 presents the regression results of all FTA dummy variables. In general, it is clear that FTA
regime has not brought the same impacts to every regional trade grouping. While it has had likely
similar effects on MERCOSUR and NAFTA, it has caused totally opposite effects on AFTA and EU.
But in another aspect, we realize that the trade creation has been appeared in all FTAs except for EU.
However, only the EU has resulted in the import trade diversion while all FTAs excluding AFTA has
yielded the export trade diversion.

13

Table 3:

Trade Creation and Trade Diversion
Pooled OLS

FEM

REM

HT

2.225

0.529

0.666

0.626

(29.50)**(8.49)**(10.81)**(10.24)**

0.681

0.169

0.223

0.204

(21.45)**(6.42)**(8.56)**(7.90)**

0.994

0.562

0.597

0.593

(31.35)**(20.76)**(22.84)**(22.76)**-0.456-0.053-0.187-0.156(11.93)**(1.07)(4.05)**(3.29)**-0.200-0.067-0.129-0.119(8.59)**(1.91)(4.09)**(3.66)**-0.5710.020-0.086-0.048(24.66)**(0.59)(2.73)**(1.48)

1.085

0.453

0.480

0.474

(8.77)**(4.93)**(5.22)**(5.20)**-0.255

0.556

0.486

0.508

(6.39)**(18.42)**(16.23)**(17.10)**-0.853-0.061-0.095-0.087(21.05)**(1.98)*(3.10)**(2.87)**

0.073

0.292

0.279

0.287

(0.45)(2.22)*(2.13)*(2.21)*-0.0001

0.143

0.126

0.134

(0.00)(4.33)**(3.83)**(4.12)**-0.982-0.160-0.199-0.181(22.83)**(4.82)**(6.04)**(5.53)**-13.335-21.084-12.473-11.801(86.21)**(46.17)**(35.41)**(24.80)**Observations

21901

21901

21901

21901

Adjusted R-squared

0.79

F-test (1493,20388)

41.52

Hausman Test

3331.54

Hausman Test*

130.73

AFTA

Import AFTA

Export AFTA

EU

Import EU

Export EU

MERCOSUR

Import MERCOSUR

Export MERCOSUR

NAFTA

Import NAFTA

Export NAFTA

Constant

Notes:Absolute value of t statistics is reported in parentheses.
* denotes significant at 5%; ** denotes significant at 1%.
All individual effects are not reported.

14

Our result showing the trade creation in AFTA is similar to Elliott & Ikemoto (2004) but differsfrom the previous studies such as Endoh (2000) and Tran Van Tho (2003). It is more important tonote that all FTA dummies were entered in the dynamic way. This point allows us to offer a new
finding that AFTA has significantly affected export flows in the subsequent years of its effective. The
HT result exhibits that the intra-regional trade for AFTA has increased to a higher level of 0.626,
implying that AFTA members have traded with each others about 87% above the level predicted in
the benchmark context.
Nevertheless, it can be seen, the magnitude of trade diversion effects involves two aspects. A
positive sign of the coefficient on the import AFTA dummy suggests that AFTA members have nottransferred their import transaction from non-member trading partners to member ones. It means that
there has been no import trade diversion over the period of 10 effective years. One possible
interpretation is that the dynamic network of domestic production together with foreign investment
projects in AFTA countries have caused these countries to prefer importing from non-members
outside the region.
On the other hand, we detect that AFTA have not given rise to the export trade diversion since
the significant coefficient on the export AFTA dummy is positive. Our plausible explanation for thisis that export-oriented strategies have been an engine of economic growth for these countries inmany years. Moreover, characteristics of production and consumption in all member countries may
have led them to persistently search for non-members as their export destinations.
On the whole, the test statistic for the significance of the heterogeneous effects in errors in ourmodels affirms our prior assumption on this issue. Furthermore, the results of the Hausman Test on
the uncorrelation between explanatory variables and heterogeneous effects in errors are rejectedfurther to convince us of the appropriateness of HT estimators. Therefore, this overall argument
proves the advantage of our preferred estimation method over the conventional OLS in the empirical

15

specification of the gravity model.
3.

Further discussions and implicationsFirstly, we notice the language impact in our empirical investigations. These coefficients on thelanguage dummy suggest that the less the cultural difference in two countries the more the volume ofexports between them. It would be better to say that if two countries have linguistic links, thesecultural ties will encourage bilateral trade. In addition, what has to be noticed is that the language
variable correlates with direction of exports effects at our specific assumption in the subsection
IV(1). In this respect, language and other cultural factors can symbolize tastes, habits and wants that
influence consumer choice. Therefore, all these things may suggest that cultural differences can
express differences in consumer preference in this paper. Viewed in this light, we can argue that two
countries of identical preferences would trade more than that of different ones. This finding issomehow similar to what account for intra-industry trade in the trade theory of monopolisticcompetition (Krugman, 1979; Helpman & Krugman, 1985).
Secondly, our investigation on export flows aims at examining an AFTA effect ininterrelationship with other FTA; thus, comparison among them may be useful. It is interesting tonote that all FTAs have not obtained the identical patterns of the trade creation and trade diversion.
Of four regional trade arrangements empirically analyzed, only NAFTA and MERCOSUR have thesame patterns of trade effects, while AFTA is completely contrary to EU in trade effects. We find
only trade creation in AFTA, but only trade diversion in EU. The reasons why such things happen
require further study and investigation.
Finally, it is noteworthy that the distance has increased its negative impact on export flows, andthat FTA has strengthen the distance impacts. One remarkable fact is that transport costs still take alarger share in transaction costs when compared with those of tariffs, and that they represent themain impediment to trade flows (World Bank, 2002). It is widely known, however, that transport16

costs have declined considerably in the past fifty years, especially in the last decade when a global
economic integration rapidly progressed (Word Bank, 2004). Hence, in order to further promote
export flows, an improvement in trade facilitation, which implies improved efficiency in theadministration, procedures, and logistics at ports and customs, is one of the most suitable policies
in transport. Without such improvement, FTA regime might be failed to achieve the targets ofpromoting intra regional trade.
V. Conclusion
This paper has investigated what factors can account for trade flows and particularly how
important is the impact of FTA relative to other factors in explaining trade flows of AFTA in theinterdependent context among other FTAs.
Empirical results plausibly indicate that our preferred method- HT fits the data remarkably andits estimated results are highly significant. Our first finding is that the GDPs, population andlanguage among other factors can explain export flows in our observation. We find that export flows
among countries increase more than proportionately with GDPs. This point reconfirms the result
obtained before by including the heterogeneous effects in errors but differs from Elliott and Ikemoto(2004). In addition, it seems reasonable to conclude that trade might be higher among countries inwhich the same language is used rather than the different.
Next, our crucial finding is that AFTA has only produced the trade creation among its members.
This finding is inconsistent with a number of previous studies but strengthens the finding of Elliottand Ikemoto (2004). More importantly, the dynamic way of following FTA effects allows us to reachanother finding that the impact of AFTA has immediately appeared in export flows in the subsequent
years of 1993.
Finally, we find that the geographic distance might impede trade rather more strongly in thecontext of FTAs. This result suggests the importance of improvement of trade facilities, which could

17strengthen the targets of FTA regime.

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Appendix A: Direction of AFTA trade in 1992, 1997 and 2002.
Trading partners(unit: million US dollars , %)

1992

1997

2002

Intra regional
Share of total exports (%)

39475.32
20.98

88773.44
24.95

95492.93
23.66

NAFTA
Share of total exports (%)

38817.94
20.63

68860.322
19.35

76839.078
19.04

EU
Share of total exports (%)

43196.11
22.95

87179.65
24.50

115707.80
28.67

MERCOSUR
Share of total exports (%)

447.290.24

1940.240.55

1150.56
0.29

Japan
Share of total exports (%)

31471.77
16.72

48132.13
13.5350654.16
12.55

China
Share of total exports (%)

4,006.92
2.13

10,709.20
3.01

21,316.82
5.28

Total exports to The World

188197.41

355867.85

403563.67

Intra regional
Share of total imports (%)

37931.71
18.77

76743.34
20.48

86100.55
24.24

NAFTA
Share of total imports (%)

31756.05
15.71

61653.48
16.46

49277.29
13.87

EU
Share of total imports (%)

30735.81
15.21

54673.41
14.59

39391.75
11.09

MERCOSUR

1662.992529.212406.71

Direction of exports

Direction of imports

Share of total imports (%)

0.82

0.68

0.68

Japan
Share of total imports (%)

47588.57
23.54

75211.29
20.07

58211.56
16.39

China
Share of total imports (%)

5794.232.87

13821.31
3.69

27154.69
7.65

Total imports from The World

202128.40

374667.55

355192.24

Source: Direction of Trade, IMF (2003)

19

Appendix B

Data Sources

The volume of trade (in FOB terms, American dollars) are derived from The Direction of Trade (DoT) CD-ROM
2003 by IMF, Population and real GDP (in constant 1995 American dollars) from the World Development Indicators
2004 by World Bank. The foreign exchange rata are obtained and converted from the International Financial Statistics
Database 2003 by IMF. In addition, the geographic distance between two capital cities has been downloaded fromthis website http://www.indo.com/distance and expressed in km. Information about the official language of countries
is extracted from the Encyclopedia Britannica..
Country coverage: 13 EU countries (Austria, Denmark, Finland, France, Germany, Greece, Ireland, Italy,
Netherlands, Portugal , Spain, Sweden and UK), 6 AFTA countries6 (Indonesia, Malaysia, the Philippines, Singapore,
Thailand and Vietnam), 3 NAFTA countries (United States, Canada and Mexico) and 4 MERCOSUR countries(Argentina, Brazil, Paraguay and Uruguay). The remaining 13 are Australia, New Zealand, Japan, Korea, China,
Hong Kong, India, Pakistan, Bangladesh, Turkey, Norway, Switzerland, and Chile.

6

AFTA has a total of 10 member countries, but other members such as Brunei, Myanmar, Lao PDR and Cambodia do nothave sufficiently necessary data.

20