Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
ASEAN Trade Flow Analysis using Competing Approaches
Ruhul Salim and Shahriar Kabir
This article analyses trade potential of a prospective ASEAN currency
union by using the event study approach. The model specification captures
the possible trade benefit for ASEAN currency union at a hypothetical level
of ASEAN economic integration similar to the European Union. The
empirical results show that the projected trade benefit is 21 per cent of
ASEAN GDP indicating that an ASEAN currency union would be beneficial
if ASEAN members reach the desired integration level at a cost less than
21 per cent of their GDP.
Key Words: Event Study, Currency Union, Trade Integration
JEL Classifications: C23, F10, F15
1. Introduction
Interest in the formation of a common currency union in the East Asian region has
been stimulated after the Asian financial crisis in 1997/98 and it remains a major
topic of research since then. However, controversy exists over the feasibility of
common monetary policy around the region, particularly after the economic struggle
of the European Union (EU) members in recent years. Existing trade literature
typically applies Gravity model as a basic analytical tool. A major departure of this
model takes place in 2000, when Rose (2000) added two new variables: the effect of
currency union on trade flows and the response of bilateral trade to nominal
exchange rate volatility. Rose’s modified gravity model was well enough to measure
EU’s common currency impact on trade for the post-Euro period. However, even
after further improvement of gravity model by Glick and Rose, 2002; Micco et al.,
2003 and more recently by Kalirajan (2008), it still remains less well-suited for exante analysis of trade benefits from a future currency union. Thus, the objective of
this article is to propose an alternative method, i.e. the event study approach to
assess the possible trade enhancement from a prospective currency union in the
ASEAN region. This approach takes ex-ante analysis of potential trade benefits from
a prospective currency union without going through rigorous modelling.
The foundation of the event study approach is the seminal works of Ashley
(1962), Ball and Brown (1968) and Fama et al. (1969). This approach has been
commonly used in Finance in general and stock market literature in particular to
examine the impact of a sudden event. We extend the event study model to include
trade variables and currency union effects. The outcome of the model is consistent
with that of gravity model. We further extend the model to capture the impacts of a
future currency union compared with a pre-selected benchmark. Thus, the event
study model of trade overcomes the drawback of gravity model.
__________
Ruhul Salim, School of Economics & Finance, Curtin Business School, Curtin University, Australia.
Email: Ruhul.Salim@cbs.curtin.edu.au
Shahriar Kabir, School of Business, Monash University Sunway Campus, Jalan Lagoon Selatan,
Bandar, Sunway, Selangor, Malaysia.
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
The remainder of the paper is organized as follows. Section 2 describes the
model, followed by a description of data and results in Section 3. The last section
concludes the paper.
2. Event Study Model of Trade: A Suggested Methodology
Following several steps involved in the event study approach as discussed in
Bowman, (1983), Dasgupta et al. (1998), Peterson (1989) and Mackinlay (1997) we
take the implementation of Euro as the event of interest for the analysis. The event
window is considered here as ten years prior to the event and ten years after the
event as suggested by Dasgupta et al. (1998). However, following Binder (1998), the
event period and one year prior and after the event have been excluded to avoid any
bias. Hence pre-Euro period is 1988 to 1997 and post-Euro period is 2001 to 2010.
Our objective here is to measure the trade enhancement factor from the Euro and
then apply that factor to the ASEAN economy. The method continues at three steps,
calculating trade intensities for each country pair, cumulating the intensities and then
taking the cumulative average.
The estimation begins by calculating the trade intensity for each of the EU members
Tradeit pre
Tradeit post
for the pre- and post-event periods, such as TIit pre 
and TIit post 
.
GDPit pre
GDPit post
Here, TI (Trade/GDP)1 is the trade intensity; i (=1, ……, 45) refers to number of
country pairs; tpre (=1988 – 1997) is the pre-event period and tpost (=2001 – 2010) is the
post-event period. A major advantage of using trade intensity is that both trade and
GDP are estimated in the same currency, hence the ratio itself neutralizes the effects
of any exchange rate changes.
The calculated trade intensities of the pre- and post-event periods are aggregated
across time for each of the country-pair events. This process returns the cumulative
trade intensity (CTI) as CTIi ( E pre ) 
1997
2010
t pre 1988
t post  2001
 TIit pre and CTIi ( E post) 
 TI
it post
. Here, Epre
stands for the per-event periods in the EU and Epost stands for the post-event periods
in the EU.
The aggregation across time and event is done by calculating the cumulative
1 N
average of trade intensity (CATI) as
and
CATI ( E pre )   CTIi ( E pre )
N i 1
1 N
CATI ( E post )   CTI i ( E post ) , where, N stands for the number of country pairs (45 in
N i 1
2
this case).
Finally, the trade enhancement has been calculated from the cumulative average
trade intensity (CATI) for the pre- and post-event periods:
CATI ( E post)
TradeEnhancement 
… … … (1)
CATI ( E pre )
1
Here GDP refers to the reporting country’s GDP.
To be consistent with Event study literature, unweighted averages are used rather than averages based on
country size in terms of trade or GDP.
2
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
For ASEAN, a hypothetical event of currency union is assumed in the year 2012.
Hence, the pre-event period is taken as 2001 to 2010. Thus the relation for trade
Tradeit pre
intensity stands as TIit pre 
. Here, the number of country pairs i ranges from
GDPit pre
1 to 35 and the pre-event period tpre ranges from 2001 – 2010. Following this, the
relationships for cumulative trade intensity (CTI) and the cumulative average of trade
2010
1 N
intensity (CATI) are CTIi ( Apre )   TIit pre and CATI ( Apre )   CTIi ( Apre ) , where
N i1
t pre  2001
Apre stands for the per-event period for ASEAN and N is the number of country pairs
(35 in this case).
Following the definition of trade enhancement used in the trade literature trade
enhancement can be calculated as:
CATI ( Apost )
… … … (2)
TradeEnhancement 
CATI ( Apre )
where, Apost stands for the post-event period for ASEAN.
The core hypothesis of this study is to observe the impact of an ASEAN currency
union on members’ intra-regional trade, provided the level of ASEAN regional
integration can achieve the same level as that of the EU. This assumption returns
the trade enhancement of the EU and ASEAN to be equal. Thus, the relation
CATI ( Apost ) CATI ( E post)

becomes
, which is rewritten as:
CATI ( Apre ) CATI ( E pre )
CATI ( E post)
CATI ( Apost) 
 CATI ( Apre ) … … … (3)
CATI ( E pre )
Equation (3) calculates the amount of cumulative average trade intensity that can be
achieved from an ASEAN currency union if the ASEAN level of regional integration
reaches a similar level of integration as did the EU.
We verify the calculated trade enhancement factor by applying a standard gravity
model:
ln trade12t    1 ln gdp1   2 ln gdp2   3 ln dist12   4 cu t   5 clb12   6 cl12   12t … (4)
Here, trade12t is the trade variable between country 1 (reporting country) and country
2 (partner country) at time t while gdp1t (gdp2t ) is a measure of income of country
1(2) at time t. dist12 is the distance between countries 1 and 2, α is constant, β ( i
=1,...,6 ) are parameters of the equation, and ε12t is a white noise disturbance term.
All variables are in logs, meaning the estimated coefficients are interpreted as
elasticity. cut is a currency union dummy variable. The variable takes on the value of
1 to show the implementation of currency union and 0 otherwise. Clb12 is the
common land border dummy which is 1 if two countries share the border and 0
otherwise. Cl12 is the common language dummy, which is 1 if people of two countries
talk in same language and 0 otherwise.
3. Data and Estimation of Model
The initial Euro members are selected to represent the EU. Out of the 12 initial
members, Belgium and Luxembourg are excluded due to inconsistency in data. The
rest ten EU countries are Austria, Finland, France, Germany, Greece, Ireland, Italy,
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
Netherlands, Portugal and Spain; returning a total of 45 country-pairs for the EU. For
ASEAN, all 10 members are included. These members are Brunei Darussalam,
Cambodia, Indonesia, the Lao PDR, Malaysia, Myanmar, the Philippines, Singapore,
Thailand and Vietnam. However, to maintain consistency in data, 35 country-pairs
are finally selected.
The annual data on exports and imports are collected from the IMF Direction of
Trade Statistics (DOTS) against other partners in the selected country-pairs. A small
amount of export and import data are unavailable in the IMF series, which are
collected from the UN COMTRADE database and adjusted with the IMF series. Data
on GDP are collected from the World Bank World Development Indicator (WDI)
database.
Benchmark selection: EU’s trade benefit from the Euro
Trade intensities of the 45 selected EU country-pairs are calculated for the preimplementation (1988–1997) and the post-implementation (2001–2010) period of the
Euro (Appendix Figure 1 and 2). For both periods, most pairs show trade intensities
of less than 0.05, suggesting that trade of these country-pairs are mostly at or below
5 cents for every dollar of the respective reporting country’s GDP. However,
exceptions are apparent, such as, Austria-Germany, Finland-Germany, FranceGermany and Portugal-Spain.
The next step is cumulating the trade intensities and calculating the cumulative
average. The first part of Table 1 (column 2 and column 3) shows the calculation for
the trade enhancement factor achieved by the European Union by developing the
Euro zone.
Table 1: Calculation of Possible ASEAN Trade Enhancement from a Currency
Union
European Union
Cumulative
Average
Trade
Trade
Enhancement
Intensity
Factor
(CATI)
Pre-Event
period
Post-Event
period
0.223627097
ASEAN
Cumulative
Average Trade
Intensity
(CATI)
Possible Trade
Enhancement
0.6057567712
1.342377104
0.300191894
0.2073972491
0.81315402
The CATI for the European Union are calculated as 0.2234 for the pre-Euro period
and 0.3002 for the post-Euro period. This result suggests that on average, 22 cents
of trade occurs between the EU members for every dollar of their GDP in the 10
years prior to the currency union, and an average 30 cents of trade occurs in the 10
years following the currency union. The resulting trade enhancement factor is 1.34,
implying that the currency union increases the average intra-regional EU trade by
nearly 34 per cent over the 10 year event windows. Next, the result is verified with
simple gravity model estimation.
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
The Testimony of Gravity
Table 2: Estimates from the Panel Gravity Model
Fixed Effects (“within”)
gdp1
gdp2
Random Effects GLS
***
.5252562
.5791037***
(.0415565)
(.0348475)
***
.4252856***
.4532994
(.0391534)
(.033455)
-1.258483***
dist
(.2231109)
***
cu
.2153714
(.020878)
.205265***
(.0208574)
.5832292*
clb
(.3068491)
cl
.3217259
(.6501944)
-4.238948***
4.477345***
(.3436844)
(1.70418)
R-sq: within
0.8081
0.8078
R-sq: between
0.8031
0.8481
R-sq: overall
0.7510
0.8431
_cons
Note:
***, ** and * denote
1%, 5% and 10% level of significance respectively.
Table 2 presents the results of gravity estimation. Results include both fixed effect
estimators and random effect estimators; however, the Hauseman test supports the
RE estimators. Except common language dummy, other variables are significant,
and the signs of the major variables are as expected. Our particular focus goes to β4,
which reflects the trade elasticity of common currency implementation. Following
Rose (2000), impact of Euro on intra-regional trade between the initial Euro
members is e0.205265 or 1.2278504. Thus the gravity estimation captures 23 per cent
trade enhancement by the initial Euro members following implementation of Euro.
This result is similar to the result of event study method.
Calculation of ASEAN trade prospect
Trade intensities of 35 selected ASEAN country-pairs are calculated from 2001 to
2010 (Appendix Table 3). ASEAN trade intensities appear to be more heterogeneous
with higher magnitudes than that of the EU. Among the country-pairs, a common
phenomenon is the sharp drop of trade intensities during 2009, which substantially
improve in 2010. This shows that ASEAN countries have recovered quickly from the
global financial crisis.
The second part of Table 1 presents the calculation of the possible ASEAN trade
enhancement from a currency union. For ASEAN, the cumulative average trade
intensity (CATI) from 2001 to 2010 is calculated as 0.6057, suggesting that on
average, 61 cents of trade occurs between ASEAN members for every dollar of their
GDP from 2001 to 2010. If hypothetically, ASEAN members adopt a common
currency in the year 2012 and achieve a trade enhancement factor equal to that of
the EU, the cumulative average trade intensity (CATI) in the post-currency union
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
period appears to be 0.8131. This result suggests that on average, 81 cents of trade
would occur between ASEAN members for every dollar of their GDP in the postcurrency union period. From the difference between the pre-event and the postevent cumulative average trade intensities, trade enhancement is calculated as
0.2074, suggesting that the ASEAN currency union would enhance their intraregional trade by about 21 cents for every dollar of their GDP.
The analysis summarizes two issues on ASEAN regional integration. First, the intraregional trade patterns of the European Union members are substantially
homogeneous in both the pre-Euro and post-Euro observation periods, while notable
heterogeneity exists in the case of ASEAN trade patterns. Second, intensity value for
ASEAN in terms of trade is much higher than for the European Union, indicating that
the currency union could have more impact on ASEAN trade. Particularly, the intraregional trade benefit of an ASEAN currency union is suggested to be 21 per cent of
ASEAN GDP, provided the change in their level of integration is similar to that of the
EU. Thus, an ASEAN currency union would be economically feasible if the cost of
achieving the desired level of ASEAN integration is less than 21 per cent of ASEAN
GDP. However, such substantial heterogeneity raises question about the cost
effectiveness of the economic correction process for ASEAN.
4. Conclusion
This article develops an alternative trade forecasting model based on event study
approach and applies it to investigate the possible trade benefit of an ASEAN
currency union. The empirical results show that a possible ASEAN currency union is
expected to provide about 21 cents of intra-regional trade benefit for every dollar of
their GDP assuming the similar stage of integration as in the EU. The policy question
arises whether the cost of the correction process of the existing asymmetry and
heterogeneity is less than the estimated trade benefit from the currency union. It may
be argued that an ASEAN currency union would be beneficial if ASEAN members
reach the desired integration level at a cost less than 21 per cent of their GDP.
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
Glick, R. and Rose, A. (2002) Does a currency union affect trade? The time-series
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Proceedings of 7th Global Business and Social Science Research Conference
13 - 14 June, 2013, Radisson Blu Hotel, Beijing, China, ISBN: 978-1-922069-26-9
Appendix
Figure 1: Per-Euro EU Trade Intensity
Figure 2: Post-Euro EU Trade Intensity
Figure 3: ASEAN Trade Intensity
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