Integrasi Perdagangan Dan Sinkronisasi Siklus Bisnis Antara Indonesia Dan Asean+6
TRADE INTEGRATION AND BUSINESS CYCLE
SYNCHRONISATION BETWEEN INDONESIA AND ASEAN+6
RIZKI PRAMITASARI
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
ii
STATEMENT OF ORIGINALITY
I, Rizki Pramitasari, hereby declare that the master thesis entitled “Trade
Integration and Business Cycle Synchronisation between Indonesia and ASEAN+6” is
my original work under the supervision of Advisory Committee and has not been
submitted in any form and another higher education institution. This thesis is submitted
independently without having used any other sources or means states therein. Any source
of information originated from published and unpublished work already stated in the part
of references of this thesis. Herewith I passed the thesis copyright to Bogor Agricultural
University.
Bogor, August 2016
Rizki Pramitasari
H151137154
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RINGKASAN
RIZKI PRAMITASARI, Integrasi Perdagangan dan Sinkronisasi Siklus Bisnis antara
Indonesia dan ASEAN+6. Dibimbing oleh IMAN SUGEMA dan FLORIAN PLOECKL.
Dalam beberapa tahun terakhir, sinkronisasi siklus bisnis telah
menimbulkan kekhawatiran di antara para pembuat kebijakan global. Penelitian
ini sangat penting dalam memutuskan apakah kerjasama regional menciptakan
serikat mata uang atau tidak. Manfaat dalam membuat area mata uang optimum
akan maksimal ketika siklus bisnis dari negara-negara anggota kelompok
memiliki kemiripan karakteristik. Hal ini diyakini bahwa faktor utama yang
mempengaruhi harmonisasi siklus bisnis adalah integrasi perdagangan. Saluran ini
secara langsung dapat mempengaruhi siklus ekonomi melalui perdagangan intra
industri dan intensitas perdagangan.
Sebagai negara berkembang, Indonesia memiliki tujuan untuk memperluas
kerjasama dengan beberapa mitra dagang, yaitu ASEAN+6. Teori menunjukkan
dua kesimpulan yang berbeda tentang pengaruh integrasi perdagangan pada
sinkronisasi siklus ekonomi. Peningkatan perdagangan dengan negara-negara lain
dapat menyebabkan siklus bisnis mereka bergerak di divergen atau konvergen.
Hal ini tergantung pada dominasi perdagangan antar-industri dan perdagangan
intra industri karena integrasi perdagangan dapat dihitung dari perdagangan intra
industri ataupun intensitas perdagangan. Perdagangan antar-industri mengarah
untuk mengurangi korelasi siklus ekonomi antara mitra dagang.
Integrasi perdagangan dapat diestimasi dengan perhitungan perdagangan
intra industri dan indeks intensitas perdagangan. Perdagangan intra industri diukur
dengan indeks Grubel-Lloyd (indeks GL), berdasarkan SITC (Standard
International Trade Classification) yang dibuat oleh Perserikatan Bangsa-Bangsa
(PBB) dan digunakan untuk mengklasifikasikan ekspor dan impor suatu negara
yang dapat dibandingkan di tahun yang berbeda. Sedangkan, intensitas
perdagangan adalah seberapa intens negara dalam menjalin perdagangan bilateral.
Pendekatan ini dapat diukur menggunakan data ekspor bilateral, data impor
bilateral dan gabungan keduanya.
Beberapa studi menunjukkan bahwa implikasi dari peningkatan
perdagangan dengan beberapa negara lainnya akan menyebabkan kenaikan
pertumbuhan pendapatan yang membawa sinkronisasi dalam siklus bisnis antar
negara tersebut. Siklus bisnis atau juga dikenal sebagai siklus ekonomi diyakini
akan bergerak sepanjang waktu sebagai dampak dari globalisasi. Pengaruh dari
mitra dagang negara lain telah menjadi faktor penting untuk menentukan fluktuasi
siklus bisnis karena hubungan perdagangan yang lebih intensif antara negara akan
mempercepat ekspor dan impor. Akibatnya, siklus bisnis berfluktuasi dan ini
terjadi karena peningkatan pendapatan negara. Hal ini berdampak pada
permintaan barang dari luar negeri yang juga meningkat.
Integrasi ekonomi ASEAN merupakan bentuk kerja sama ekonomi dengan
negara-negara di Asia Tenggara. ASEAN secara resmi diberlakukan oleh
ASEAN-6 negara (Indonesia, Filipina, Thailand, Singapura, Brunei, dan
Malaysia) pada 1 Januari 2003 dan yang terakhir, ASEAN-4 negara (Vietnam
mulai berlaku pada tahun 2006, Laos dan Myanmar pada tahun 2008. Saat ini,
kerja sama ini lebih besar dan anggota yang ditambahkan ke dalam ASEAN+6
yang diharapkan memiliki dampak positif yang besar dan meningkatkan
pertumbuhan ekonomi.
Oleh karena itu, penelitian ini bertujuan untuk menyelidiki adanya
hubungan antara integrasi perdagangan dan siklus bisnis antara Indonesia dan
ASEAN+6 selama periode 1990-2013 dengan menggunakan model panel fixed
effect. Hasil penelitian menunjukkan bahwa integrasi perdagangan Indonesia dan
ASEAN+6 masih lemah. Perdagangan intra industri menyebabkan dampak negatif
dari sinkronisasi siklus bisnis antara Indonesia dan ASEAN+6.
Selain itu, penelitian ini menemukan bukti pengaruh yang tidak signifikan
dari intensitas perdagangan antara Indonesia dan ASEAN+6 dengan sinkronisasi
siklus bisnis. Hasil ini, dengan demikian, menunjukkan bahwa peningkatan daya
saing intra industri yang lebih efektif dan efisien, sangat diperlukan untuk
memberikan dampak positif dari sinkronisasi siklus bisnis antara Indonesia dan
ASEAN+6. Akhirnya, pertimbangan ini bisa menciptakan konvergensi siklus
ekonomi antara negara-negara anggota Indonesia dan ASEAN+6.
Kata kunci: siklus bisnis, integrasi perdagangan, perdagangan intra industri,
intensitas perdagangan, panel fixed effect model
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SUMMARY
RIZKI PRAMITASARI, Trade Integration and Business Cycle Synchronisation
between Indonesia and ASEAN+6, Under supervision of IMAN SUGEMA and
FLORIAN PLOECKL.
In recent years, business cycle synchronization has gained interest and
caused concern amongst policy makers globally. This study is essential in
deciding whether regional partnership creates a currency union or not. Benefit in
making optimum currency area outweighs when the business cycle of the member
countries of a group is more similar. It is believed that the main factor affecting
harmonisation of business cycle is trade integration. This channel could directly
influence economic cycle through intra industry trade and trade intensity.
As an emerging country, Indonesia has a purpose to expand cooperation to
some trading partners, namely ASEAN+6. Theorists have prompt to draw the two
different conclusions about the effect of trade integration on synchronization of
economic cycle. An increasing trade with other countries could cause the business
cycle they move in divergent or convergent. It depends on the dominance of interindustry and intra industry. Inter-industry trade leads to reduce the correlation of
economic cycles between trading partners. For example if the trade occurs as the
theory of Heckser-Ohlin or Ricardo, the greater industry specialization result in
less synchronised of the business cycle because trade integration can be proxied
from intra industry trade and trade intensity. However, when intra-industry trade
is more dominated, the correlation of business cycles is more synchronize in the
region.
Trade integration can be estimated by Intra industry trade (IIT) and trade
intensity index (TII). IIT is measured by Grubel-Lloyd index (GL index),
determining specific industry, owned by a country in a given year. This specific
industry is based on SITC (Standard International Trade Classification) is a
classification of goods created by the United Nations (UN) and used to classify
exports and imports of a country which can be compared in a different year.
Whereas, trade intensity is how intense of countries in generating bilateral trade.
This approach can be measured using bilateral export data, bilateral import data
and the last is that mix of them.
Some studies suggest that an important implication of the rapid increase in
trade with some other countries is on an architecture of integrated trade, then the
latter, will lead to the rise in growth of revenue which bring synchronization in
business cycle. The business cycle or also known as the economic cycle is
believed to be moving all the time as the impact of globalization. The influence
from important country trading partners has become an essential factor to
determine business cycle fluctuations. The more intensive trade relation between
countries will accelerate the export and import. Consequently, the business cycle
fluctuates and this occurs owing to the increase of income of country. As a result,
demand of goods from abroad also increases.
ASEAN economic integration is a form of economic cooperation to
countries in Southeast Asia by establishing ASEAN Free Trade Area. ASEAN
was officially enacted by ASEAN-6 countries (Indonesia, Philippines, Thailand,
Singapore, Brunei and Malaysia) on January 1, 2003 and the latter, ASEAN-4
countries (Vietnam came into effect in 2006, Laos and Myanmar in 2008.
Currently, this cooperation is larger and the member is added into ASEAN+6
which is expected to have big positive impacts and boost the economic growth.
Therefore, this study aims to investigate the existence of relationships
between trade integration and business cycle co-movement between Indonesia and
ASEAN+6 over the period 1990 to 2013 using a panel fixed effect model. The
results indicate that trade integration of Indonesia and ASEAN+6 is still weak.
Intra industry trade leads to negative impact of business cycle coherence between
Indonesia and ASEAN+6. Also, this study finds insignificant impact of the trade
intensity index between Indonesia and ASEAN+6 nations to business cycle
coherence.
Since there was a contrary effect in this study, it is important to change the
trade pattern to alter the impact on business cycles from negative to positive.
Otherwise, increase in trade integration between Indonesia and ASEAN+6 will
lead to negative impacts on business cycle harmonisation among these nations.
Finally, these considerations could create convergence of economic cycle between
Indonesia and ASEAN+6 member countries.
Key words: business cycle, trade integration, intra industry trade, trade intensity,
panel fixed effect model
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TRADE INTEGRATION AND BUSINESS CYCLE
SYNCHRONISATION BETWEEN INDONESIA AND ASEAN+6
RIZKI PRAMITASARI
Master Thesis
As a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
x
Externally Advisory Committee Examiner: Prof. Dr. Ir. Rina Oktaviani, MS
Thesis Title
Name
NIM
: Trade Integration and Business Cycle Synchronisation between
Indonesia and ASEAN+6
: Rizki Pramitasari
: H151137154
Approved
Advisory Committee
Dr.Florian Ploeckl
Dr.Ir. Iman Sugema, M.Ec.
Agreed
Coordinator of Major
Economics
Dean of Postgraduate School
Dr. Ir. Dahrul Syah, M.Sc, Agr
Dr. Lukitawati Anggraeni, SP, M.Si
Examination Date: 5 August 2016
Submission Date:
ACKNOWLEDGEMENT
"Say, "Indeed, my prayer, my rites of sacrifice, my living and my dying are for
Allah , Lord of the worlds." [6:162], hereby I would like to express my greatest
gratitude to God Almighty for His blessing which bring me to accomplish this mini
dissertation. I would also like to extend my gratitude towards my supervisor, Dr. Ir.
Iman Sugema, M.Sc and Dr. Florian Ploeckl, for their excellent support, guidance and
brilliant contribution for this dissertation.
Additionally, I would express my warm thanks to Australia Awards Scholarship,
Ministry of Trade, Bogor Agricultural University and the University of Adelaide for
providing sponsorship to undertake my study.
I am thankful to my parents, my nuclear family, my mother in law and my beloved
husband, Ami Suhelmi for support me spiritually and cheer me up when I gave up in
writing my thesis.
Also, I thank my relatives, my peers, my big Indonesian family in Adelaide and
anybody who has supported and encouraged me throughout my study.
Last but not least, I also wish to thank my fellow colleagues, Aksa Nugraha and
Amelia Sitohang.
Bogor, August 2016
Rizki Pramitasari
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TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLE
LIST OF APPENDIXS
1 INTRODUCTION
Background
Problem Formulation
Research Objective
Research Benefit
Scope of Research
2 LITERATURE REVIEW
Indonesia and ASEAN+6
Trade Integration
Intra-Industry Trade
Business Cycle Synchronisation
Measuring Trade Integration and Determinants of Business Cycle
Link Between Trade Integration and Business Cycle
Research Hypothesis
3 DATA AND METHODOLOGY
Data
Operational Definition
Dependent Variable
Independent Variable
Setup Model Specification
Estimation and Methodology
Fixed Effects
Random Effect
Hausman Test
Wooldridge Test
4 RESULTS AND DISCUSSIONS
Intra Industry Trade between Indonesia and ASEAN+6
Trade Intensity Index between Indonesia and ASEAN+6
Panel Regression Model Test
Policy Implications for Indonesia
5 CONCLUSION
REFERENCES
BIOGRAPHY
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LIST OF FIGURES
1 Intra-Regional Trade of ASEAN, ASEAN+3 and ASEAN+6
2 Intra-Industry Trade between Indonesia and ASEAN+6
3 Forms of Economic Integration (Griffin &Pustay 2010)
4 The Business Cycle Phases (Schmidt 2004)
5 Business Cycle Synchronisation Channel (Mizstal 2013)
6 Intra Industry Trade between Indonesia and ASEAN+6
7 Trade Intensity Index between Indonesia and ASEAN+6 (export)
8 Trade Intensity Index between Indonesia and ASEAN+6 (import)
9 Trade Intensity Index between Indonesia and ASEAN+6 (export - import)
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LIST OF TABLES
1 Data Types
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LIST OF APPENDIXS
1 Result of Wooldridge Test
2 Result of Hausman Test
3 Panel Fixed Effect Regression Results
4 Panel Fixed Effect with Time Dummies Regression Results
5 Panel Random Effect Regression Results
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1 INTRODUCTION
Background
For over half a century, expansion of international trade activity has made a
substantial contribution to Indonesia’s economic growth. The influence from
important trading partners such as ASEAN+6 has become an essential factor for
Indonesia’s pattern of growth, and could lead to stronger economic integration.
Trade integration leads to wider market access and more effective trade and,
ultimately, has a positive impact on macroeconomic variables, resulting in
economic growth.
According to Misztal (2013), increased trade with other countries can cause
their business cycles to diverge or converge. Business cycle implies the long-term
cyclical part of output growth, which moves constantly because of globalisation
(Haan et al. 2008). Studies on business cycles or economic cycles have been taken
into account in an increasingly integrated global economy. As a consequence, one
internal or external variable shock will lead to worldwide economic fluctuations.
In the long term, the degree of this dynamic co-movement will form an economic
cycle known as the business cycle (Jansen and Stockman 2014).
There are several channels through which the harmonisation of business
cycles between two or more countries can be increased. One of these is through
trade integration. Many researchers believe that there is a strong correlation
between trade integration and the business cycle. When integrated nations have
highly dominating intra-industry trade, a cross-country spill over effect may
reduce the potential for asymmetric shock and create business cycles that are more
synchronised. However, if inter-industry trade dominates the trade pattern, less
harmonisation of the cycles would be expected.
Source: World Development Indicator, 2014
Figure 1. Intra-Regional Trade of ASEAN, ASEAN+3 and ASEAN+6,
by Country Group, 1990–2014
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In today’s globalised world, regional integration partnerships have
tremendous benefits, accelerating large markets and trade scale cooperation, and
developing vital aspects for economic growth, especially in trade in services and
merchandise and investment (Sen et al. 2013). EEU is the first role model which
successfully improve its members output. This regional partnership has inspired
other regions to embrace established integration partnership.
Problem Formulation
Indonesia is one of the largest countries in ASEAN and internationally is
considered to have high economic potential. This country has a strong
commitment to enhancing trade cooperation through a regional partnership. The
aim of this new formation, which is called ASEAN+6, is to accelerate economic
growth, through larger markets and scale of trade, and cooperation in energy,
foods and other vital aspects of economic development. This regional trade
network is considered the most suitable union for improving regional economic
performance, especially in trade and investment (Sen et al. 2013). As shown in
Figure.1, the share of intra-regional trade in ASEAN, ASEAN+3 and ASEAN+6
has shown a moderate rise in recent years, from about 16.97% in 1990 to around
24.01% in 2014 for ASEAN and from 33.04% in 1990 to 42.25% in 2014 for
ASEAN+6. The intra-regional trade share increased throughout the two decades.
In the future it is expected to rise and reach the integrated economic goal of
ASEAN+6. Because of Indonesia’s increased trade volume with ASEAN+6, it is
important to examine whether this has led to more integration in trade and
affected Indonesia’s business cycle coherence with ASEAN+6 member countries.
Research Objective
The aim of this study is to explore the impact of trade integration on the
business cycle synchronisation between Indonesia and ASEAN+6.
Research Benefit
The results of this research will be beneficial to the Indonesian and
ASEAN+6 partnership in deciding the next step in the integration, in particular, to
policy makers and the government of the Republic of Indonesia in boosting the
trade sector, and to societies in charge within the trade sector and practitioners
who run businesses within the ASEAN+6 boundaries.
Scope of Research
This study used samples from the five major ASEAN countries, namely,
Indonesia, Malaysia, Singapore, Philippines and Thailand (ASEAN-5), plus the
six members, namely, Japan, China, South Korea, India, Australia and New
10
Zealand. The variables were intra-industry trade, trade intensity index, demand
spill over, and fiscal and monetary coordination policies from 1990 to 2013.
Variables were analysed using panel data with econometrics software E-Views 8
and Stata 13 to investigate the influence of these variables on the business cycle
synchronisation between Indonesia and ASEAN+6.
This study is structured in six sections, starting with the introduction.
Section 2 provides the background of Indonesia and ASEAN+6 and an overview
of trade integration, intra-industry trade and synchronisation of business cycles.
Next, Section 3 reviews the theories on trade integration and business cycle
synchronisation. Section 4then presents the main specifications and data used in
the panel data, and Section 5 discusses the results of the analysis. Finally, Section
6 provides the final conclusions, including how the results can be used as policy
options to cope with the existing issues.
2 LITERATURE REVIEW
To begin with, it is important to outline Indonesia and ASEAN+6, trade
integration and business cycles, to increase understanding when assessing the
effects of trade integration on business cycle synchronisation between Indonesia
and ASEAN+6.
Indonesia and ASEAN+6
Indonesia is ranked as the ninth largest economy in the world (World Bank
2015). By the end of 2014, Indonesia’s gross domestic product (GDP) had
reached US$ 2.55 trillion. This position surpassed the GDP of the United
Kingdom, which was only US$ 2.4 trillion in 2014. Along with this improvement,
Indonesia strongly supports the establishment of economic integration among the
countries of Southeast Asia. ASEAN economic integration is a form of economic
cooperation among countries in Southeast Asia that was created by establishing
the ASEAN Free Trade Area. Currently, this cooperation has been expanded by
members being added to form ASEAN+6, which is expected to have a positive
impact and boost economic growth (Hamanaka 2014).
ASEAN+6 was given concrete shape by the East Asia Summit (EAS)
leaders, comprising 16 participating countries, namely, the 10 ASEAN countries,
Australia, China, India, Japan, the Republic of Korea and New Zealand. EAS
participants have agreed to maintain EAS as an open, inclusive, transparent and
outward-looking forum that enables participants to hold strategic discussions on
various actual themes in the region. Indonesia provides a major contribution and
is committed to strengthening regional trade in ASEAN+6. However, because of
the dramatic rise in trade between Indonesia and the other ASEAN+6 member
countries as shown in Figure 2, from about 48 Million in 1990 to roughly 347
Million in 2013. This increase in number does not merely lead to harmonising the
business cycle in these nations.
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Source: World Integrated Trade Solution, 2015
Figure 2. Intra-Industry Trade between Indonesia and ASEAN+6
Trade Integration
Moneta (2006) stated that economic integration has the same principles and
mechanisms as free trade, which refers to the elimination of national trade
boundaries. There are five different stages of economic integration, as
documented in Figure 3. Marrewijk (2007) asserted that the highest degree of
economic integration is political union, followed by economic union, common
market, custom union and free trade area. A deeper integration scheme such as the
European Union (EU) could be classified as a common market (Schwarze, 2006),
whereas a shallow integration, for instance, ASEAN+6, is integrated as a free
trade area, which can change in structure to become intense trade ties and closer
economic links. Factors in forming regional trade integration are:
1. Establishing security and policies to ensure trade diplomacy for neighbouring
countries. For example, the formation of the European Economic Community
was conducted through a trade cooperation policy in the region (Pentecote et
al. 2015; Rose and Stanley 2005). Trailing far behind is ASEAN+6 in a free
trade zone, established to reform mixed policies with a comprehensive pact
involving trade facilitation.
2. Managing the global value chain and trade friction. A feature of the ASEAN+6
trade scheme is its intra-regional production network approach. Indonesia, the
largest country among the ASEAN countries, has increased the efficiency and
effectiveness of trade production. China, Japan and Korea are among the
strongest economic powers in East Asia and are considered to be strengthening
the ASEAN+6 partnership. Similarly, India plays a prominent role in
enhancing this integration because it accounts for a large trade share within the
ASEAN countries. The remaining countries, Australia and New Zealand, have
liberalised their trade with ASEAN and its neighbours.
3. Increasing capacity building for development and cooperation in the region in
the form of eliminating discriminatory trade barriers to boost efficiency,
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productivity and competitiveness as well as reducing costs and trade and
investment risks. The formation of the ASEAN+6 Free Trade Area was
intended to reduce, even remove non-tariff barrier.
4.
Source: Griffin and Pustay, 2010
Figure 3. Forms of Economic Integration
More intensive trade relations between countries accelerates exports and
imports. As a result, the demand for goods from abroad also increases, leading to
a rise in the income of a country.
Intra-Industry Trade
By influencing business cycles, intra-industry trade is an essential aspect of
trade integration. Intra-industry trade is the trade in the same industry, while interindustry trade are global tradingin different product groups (Pittiglio 2012). Interindustry trade is based on traditional trade theory, such as the Heckscher–Ohlin
(H–O) model (Zhang 2006). However, in today’s growing world, intra industry
trade is more essential to expect how trade integration in influencing business
cycle coherence.
There are various classifications and definitions of intra-industry trade.
Grubel and Lloyd pioneered the research in intra-industry trade in 1975 and
certain empirical tests employ the Grubel–Lloyd (GL) index (Van and Vijk 2002).
According to Lancaster (1980), traditional trade reflects a natural and acquired
comparative advantage, and inter-industry trade notices endowment factors
following the proportional factor theory, which is based on the international trade
theory of Heckscher and Ohlin (Zhang 2006). Salvatore (1995) asserted that intraindustry trade reflects economies of scale. The emergence of intra-industry trade
is based on the advantageous consideration of economies of scale in the
production process. Intra-industry trade benefit consumers because it gives them
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access to a more diverse choice of products and lower prices as a result of
productivity improvement through economies of scale.
Business Cycle Synchronisation
Studies about business cycles or economic cycles have been taken into
account amongst researchers in an increasingly integrated global economy.
Business cycle implies the long-term cyclical part of output growth (Haan et al.
2008), where the real GDP of each country is used as the indicator. This
measurement relates to a global economy. As a consequence, one internal or
external variable shock will lead to worldwide economic fluctuations. In the long
term, this dynamic co-movement will form an economic cycle known as the
business cycle (Jansen and Stockman 2014). The business cycle can also be
defined as the deviation of output against expansion and contraction trends (Botha
2004; Misztal 2013).
Source: Schmidt, 2004
Figure 4. The Business Cycle Phases
As shown in Figure 4, the red line is the trend of potential GDP, calculated
from the blue line, which is the actual real GDP. Each cycle has two types of
turning points, the peak and the trough (Camacho et al. 2008). The peak point
shows the positive output gap, and the negative output gap is shown from the
trough or depression. Both turning points indicate the direction of the cyclical
movement, which becomes an indicator of change in contraction or expansion
from period to period. Schmidt (2004) stated that there are four stages in an
economic cycle, namely, the depression stage, the expansion phase, the phase of
contraction and the recovery phase. The first stage is the depression period, that is,
a period of rapid decline in aggregate demand, followed by a low level of output
and high unemployment rates, which gradually reaches the lowest point.
The expansion phase is the initial period of prosperity when the economy is
undergoing expansion that exceeds the height of the previous stage. Demand and
14
production rise, leading to a rise in inflation and interest rates. Business expansion
thereby reaches a peak followed by a phase of contraction, when production
factors such as sales, prices and employment begin to decline. The last phase is
the phase of recovery, when economic activity advances to a new period of
expansion and a rise in the business cycle. Recovery is a transitional phase
beginning from the lowest economic point and then returning to the original
starting point. In general, the strongest growth occurs in the recovery phase, but
the duration is shorter than a recession (Botha 2004).
Source: Mizstal, 2013
Figure 5. Channel through Which the Intensity of International Trade Exerts
Influence on Business Cycle Synchronisation
Business cycle synchronisation is important for ASEAN+6because of its
impact. According to the theory of the business cycle, when there is an economic
boom, credit moves uncontrollably, developing into moral hazards so that
prosperity turns into a crisis. In this condition, business cycle is considered an
imbalance in the economic rhythm. The Asian crisis in 1997 motivated
researchers to assess the business cycle and its leverage comprehensively.
Following the Asian crisis, Indonesian trade revived, mainly with other major
ASEAN and ASEAN+6 countries. Massmann and Mitchell (2004) stated that comovement of a business cycle in the same direction between two or more
countries indicates that the business cycle is synchronised. This synchronisation
reduces the cost of the economy.
As a result, the economy becomes more integrated, and then it induces the
fluctuation of business cycle. The theory of traditional trade predicts that trade
openness can lead to specialisation in production (Song 2011). Therefore,
integrated economy will result in a decline in the correlation of business cycles.
However, if trade is dominated by intra-industry trade, the business cycle will
become more symmetric (Dees and Zorell 2012).
In theory, there are three causes of the business cycle co-movement. First,
country-specific shocks are quickly spread to other nations. Secondly, external
shocks have the same effect for all nations. Third, specific shocks have in the
15
same effect on economic sector in different countries. Differences between
countries’ business cycles can occur for many reasons. Despite experiencing the
same shocks, each country may have a different effect or response, such as in the
reaction to national output. As the economies of Indonesia and ASEAN+6 are
forecasted to become more integrated, this investigation is needed to pursue
outstanding growth among the members.
Measuring Trade Integration and Determinants of Business Cycle
The baseline model of trade integration is approached by using trade
intensity and intra-industry trade (Misztal 2013). Trade intensity is the intensity of
countries in generating bilateral trade. This approach can be measured using
bilateral export data, bilateral import data or a mix of these. For bilateral exports,
trade intensity can be calculated as the bilateral export divided by the total exports
of both countries. Likewise, trade intensity for imports can be calculated as the
bilateral import divided by the total imports of both countries. Similar calculations
for export and import are done by dividing the bilateral export and import by the
total exports and imports of both countries.
Another parameter to measure trade integration is the intra-industry trade
index. It was observed by Reinert (2012) that approximately one-third of world
trade takes place as intra-industry trade, especially in manufactured goods among
the developed or high-income countries of the world, where it probably accounts
for up to 70% of trade. Globally, intra-industry trade is also becoming more
important over time, particularly in Asia. Intra-industry trade arises if a country
simultaneously imports and exports similar types of goods or services (Shelburne
2002). To measure intra-industry trade, the most commonly used method is the
GL index (Greenaway and Milner 1986), based on the standard international trade
classification (SITC). The SITC is a classification of goods created by the United
Nations (UN) used to classify the exports and imports of a country, which can be
compared across different years.
To measure a business cycle, the annual GDP is statistically smoothed using
econometric software, to obtain the actual GDP and potential GDP. The Hodrick–
Prescott (H–P) filter, first differencing and quadratic de-trending are amongt he
statistical smoothing techniques that are popular and widely constructed in studies
of potential output calculation (Saiki and Kim 2014). Following Frankel and Rose
(1998) and Cortinhas (2007), the H–P filter can shrink the effects of cyclical GDP
by removing trend components to estimate the non-trend (cyclical) component.
The non-trend (cyclical) component is considered the difference between the
original series and its trend. Instead of a long-term component, smoothing time
series can show output gap deviations by decomposing the actual GDP and
potential GDP. Hence, the H–P filter method is commonly used to identify long
time series economic trends with no structural breaks.
16
Link Between Trade Integration and Business Cycle
The theories of trade integration and business cycles have led to an increase
in the attention paid to these empirical questions. Krugman (1991), who follows
the Heckscher–Ohlin trade theory, asserted that growing trade activity has led to
higher specialisation of production. Krugman added that this trade pattern has
resulted in different direction co-movement of business cycles. Hence, trade
integration has a diverging effect on business cycles.
In contrast, a substantial number of empirical studies have documented a
significant and clear positive relationship between trade integration and business
cycle synchronisation. Research by Cortinhas (2007), who adopted the modified
variation of the Frankel and Rose (1998) model, led to an empirical result that
countered that of Krugman (1991). Corthinhas found that intra-industry trade is
the main factor leading to more harmonised business cycles in the economies of
ASEAN without Indonesia. He examined the effect of intra-industry trade on
business cycle synchronisation in five ASEAN countries over the period 1962–
1996, using panel two-stage least squares (TSLS) estimations. The developed
independent variables for intra-industry trade were the log of distance and a
dummy for land borders. According to his empirical findings, although it is the
largest economy, Indonesia appeared to be less integrated than other smaller
member countries. Hence, Cortinhas ignored Indonesia in his alternative
combinations, unless the trade would lead to reducing the correlation of business
cycles.
Similarly, Shin and Wang (2003) emphasised that an increase in whole trade
should be accompanied mainly by intra-industry trade; it can then induce more
positively converged business cycles. The data used in their paper were the annual
GDP from 12 Asian economies over two decades, from 1976 to 1997, and trade
volume data. They set up four coefficients in the regression: bilateral trade
intensity, employing Frankel and Rose’s (1998) model; intra-industry trade
intensity, adopting Grubel and Lloyd’s (1975) equation; and two policy
coordination channels. These policy shocks were split into the degrees of
coordination of fiscal and monetary policy. The fiscal coordination channel was
investigated by calculating the budget deficit ratio to GDP, and the degree of
monetary coordination was constructed by examining the correlation of the M2
growth rates. Consistent with recent investigations, in this empirically analysed
paper, these authors concluded that a rise in intra-industry trade between Korea
and other Asian countries has strengthened business cycle coherence.
In line with this, in a separate paper, Shin and Wang (2005) suggested that
the deeper the trade integration the closer the business cycle in East Asia. They
extended four possible variables, namely, demand spill over, inter-industry trade,
intra-industry trade and policy coordination of 12 Asian nations, during 1976 and
1997. Estimated coefficients were tested using panel ordinary least square (OLS)
fixed effect, and real GDP correlation was estimated using first differences in
logarithms and the H–P filter. Shin and Wang emphasised that in tackling
unobservable components that are country specific in time series data of trade,
OLS fixed effect regression is more suitable than panel regression with pooled
data. Their result confirmed that only the second channel, which lowered the
coherence of business cycles. Also, trade formation is predominantly integrated
17
by intra sector in East Asia. This finding relates to the authors’ previous work, in
2003, that found that intra-industry trade is the primary channel for fostering
business cycle coherence between Korean and Asian countries.
Following this study by Shin and Wang (2005), Rana (2007) reviewed the
importance of intra-industry trade and, to a lesser extent, monetary coordination in
synchronising business cycles in East Asia. The methods used in this paper were
value at risk (VAR) and pooling regression. Using the annual GDP data of 11
ASEAN+3 nations spanning a decade, between 1989 and 2003, and the H–P filter,
the author found similar results to those of Shin and Wang (2005). He also used a
different proxy by correlating short-term real interest rates to estimate financial
channels.
For the sake of comparison, Park (2013) revealed that monetary aggregate
was a key aspect in explaining business cycles in East Asia, notably in Korea,
Malaysia and the Philippines. Data used in this study were quarterly real GDP
data, real private consumption expenditure, real gross capital formation, real stock
of M2, real government expenditure, productivity, oil price, and total export and
import in eight nations of East Asia. Unlike previous literature on the bilateral
trading nexus, this study modified the dynamic factor model of Crucini, Kose and
Otrok (2011), which measures time-varying dynamic conditional relationships.
Departing from the existing study, Duval et al. (2014) proposed an
integrated trade and business cycle nexus in Asia by using 34 advanced countries
and 29 emerging economies over two decades. By retaining the dynamic factor
model with a gravity and panel approach, these authors found that worldwide
factors can explain business cycle evolution. Additionally, by examining trade
intensity, trade specialisation and intra-industry trade as a proxy for trade
integration, and banking and portfolio integration as a proxy for financial
integration, they found that statistically examined trade integration has driven
business cycles in Asia. Specifically, its impact in crisis periods outweighs that in
tranquil times. This result was confirmed in a later empirical approach by Dai
(2014). The method used in his paper was panel regression. Dai focused on how
newly industrialising economies, ASEAN-4, and Asian business cycles have comoved with the business cycles of China, Japan and the United States. He
attempts that trade and financial linkage indicate the importance of Japan, China
and the United States in Asian business cycles.
In line with this finding, Inklaar et al. (2008) stated that trade intensity,
financial similarities and specialisation (intra-industry trade) have robust effects
on business cycle coherence in certain Organisation for Economic Co-operation
and Development (OECD) countries. These effects were not as great as those
reported by Frankel and Rose (1998). Instead of using instrument variables, they
constructed a multivariate model. This approach enabled them to highlight
specialisations and degrees of economic policy similarities. The measures of
economic activity used in this study were the monthly Index of Industrial
Production and the quarterly GDP determined by the H–P filter.
Kumakura (2006) noted that the electronic industry as a specific
manufacture positively enhances business cycle coherence. He followed Frankel
and Rose’s model in 13 Asia–Pacific nations and computed the measures using
first differenced annual real GDP data, bilateral trade data and specific industries.
This business cycle is a key determinant in entering a unified currency area.
18
Hence, growing trade with no industry-specific shock influencing the business
cycle does not necessarily transform an optimum currency area in Asia–Pacific
nations.
According to Saiki and Kim (2014), business cycle co-movement affected
by intra-industry trade and vertical intra-industry trade increased in both Europe
and East Asia. However, increased intra-industry trade and vertical intra-industry
trade in East Asia were faster than those in the Eurozone because of the role of the
supply chain. The data used covered four decades, from 1970 to 2011. The pattern
of the business cycle was explained by quarterly real GDP filtered using year over
year growth, the Baxter–King filter and the H–P filter, and a detailed investigation
of intra-industry trade and vertical intra-industry trade were calculated using the
GL index. A separate empirical study by Rana, Cheng and Chia (2011) had a
similar empirical finding. The fixed effect panel method was used to estimate
trade and business cycles, whereas gravity equation was used to calculate the trade
pattern of countries.
However, a number of empirical studies suggest that an important channel
contributing to business cycle co-movement is trade integration. By estimating a
simultaneous equation model based on the six variables panel approach within the
27 EU countries from 1995 to 2012, Antonakakis and Tondl (2014) verified that
trade and foreign direct investment have fostered business cycle co-movement,
whereas specialisation has an insignificant impact on business cycles. In addition,
these writers found that poor fiscal coordination and exchange rate fluctuations
have impeded economic cycle synchronisation in the EU. This leads to further
policy implications.
In essence, business cycles have been playing a crucial role in the globalised
economy. In this regard, intra-industry trade is the most obvious parameter for
approaching trade integration. It is closely related with business cycle comovement. In contrast, influences from other channels, namely, financial and
policy channels, are not as strong as trade channels. A review of empirical
literature about trade integration and business cycles from different countries
indicates that the economic characteristics of member countries have an influence
on the strength and nature of the impact of trade integration on business cycle
harmonisation.
Research Hypothesis
Based on asymmetric information, an increase of trade will cause an
increase of business cycles synchronisation between Indonesia and ASEAN+6.
3 DATA AND METHODOLOGY
This section explains the data used and the econometric methods used to test
trade integration and the business cycle nexus. By employing the modified
variation of Frankel and Rose’s (1998) model, this empirical study assessed
whether intra-industry trade is the main factor influencing business cycle
harmonisation among the economies of Indonesia and ASEAN+6.
19
Data
This investigation used secondary annual, cross-sectional annual time series
data for 11 countries for the period 1990 to 2013. The data were obtained from
International Financial Statistics and the International Monetary Fund (IFS-IMF),
and World Integrated Trade Solution and the World Bank (WITS-World Bank).
The data set analysed in this study includes real GDP, total nominal exports, total
nominal imports, bilateral exports between countries, and bilateral imports
between Indonesia and ASEAN+6 countries. ASEAN-5 (Indonesia, Philippines,
Singapore, Malaysia and Thailand) were analysed; the other ASEAN countries
were not included in this study because of their low level of bilateral trade with
Indonesia, small economies and limitations of the source. In addition, the study
included the other six partner countries, namely, Japan, China, India, South
Korea, Australia and New Zealand. These countries were added to determine the
degree of trade integration of the six countries, in line with the background of
economic cooperation expansion of ASEAN into ASEAN+6.
Two models were constructed in this investigation. The first used three
subsample periods of equal length, 1990–1997, 1998–2005 and 2006–2013, and
the second model used three similar sub sample periods but excluded the year
1998/1999. These years are considered outliers, leading to potentially distorted
results. Each model was divided into two sub models to compare the significance
of the trade variables, that is, one model with policy variables and one model
without policy variables.
Operational Definition
This study has obtained data from International Financial Statistics (IFS)
and World Integrated Trade Solution (WITS)–World Bank. All data types used in
this study and the definitions are mentioned in Table 1.
Table 1.Data Types
No. Data
1
Real GDP
2
Total Export
3
Total Import
4
Bilateral Export
5
Bilateral Import
6
M2
7
Budget Deficit
Definition
national output by using constant price
to remove inflation factor
Amount of goods and services sold to
other countries
Amount of goods and services bought
from other countries
Amount of goods and services sold to
partner country
Amount of goods and services bought
from partner country
Amount of money supply including
savings and deposits
The difference between government
spending (G) and government revenue
from taxes (T) within a certain period
Source
IFS-IMF
WITSWorld Bank
WITSWorld Bank
WITSWorld Bank
WITSWorld Bank
IFS-IMF
IFS-IMF
20
Dependent Variable
The dependent variable in this regression is the bilateral correlation of
business cycles between Indonesia and other ASEAN+6 countries. Business cycle
is measured using the H–P filter. HP filter is flexible and detrending method
commonly used in economic research to remove the cyclical component of a time
series from raw real GDP data. In HP filter, a series of data can be separated into
two components, trend and cycle. It is used to obtain a smoothed-curve
representation of a time series, which is more sensitive to long-term than to shortterm fluctuations. Correlation of this output cycle can be calculated in each sub
period, taken from eight years.
Independent Variable
According to Grubel and Lloyd (1975), intra-industry trade is calculated
using the following equation:
IIT =1–∑i|exkjt–∑imkjt|/(exkjt + ∑imkjt)
(1)
Where exjt and imjt are the values of country j’s exports to Indonesia and the
values of country j’s import to Indonesia. The index ranges from zero to one. Zero
point means no intra-industry trade, while one implies its maximum level of trade
of intra industry. Intra-industry trade goes up when nations concurrently
merchandise similar types of trading services or goods. The same sector
classification is identified using the UN standard, namely, SITC.
Following Rose and Frankel (1998), three different proxies were used to test
how the intensity of bilateral trade between Indonesia and other ASEAN+6
countries affects the business cycle. The first is export data, the second is import
data and the last is a mix of these. This is also called the trade intensity index. The
trade intensity index is counted on an actual bilateral trade nexus between any
given two countries. The following equation uses the three options to measure TII
(trade intensity index):
TII EXjt = (exjt)/(EXind t + EXjt)
TII IMjt = (imjt)/(IMind t + IMjt)
TII EXind-jt = (extj + imjt)/(EXit + IMind t + EXjt+ IMjt)
(2)
(3)
(4)
EXit and IMit are country i’s total exports and import. Country I is
Indonesia’s trading partner country in ASEAN+6. Similarly, EXjt and IMjt are
total exports and imports of Indonesia’s trading partner country. An index value
that is greater indicates that a growing intensity of bilateral commerce occurs
between Indonesia and its trading partner country. However, this intensity index
still does not reveal the demand spill over effects. Thus, by adding a demand spill
over variable, as was done by Shin and Wang (2003), it can be determined
whether the business cycle is affected purely by the trade intensity index or
demand spill over.
21
Additionally, to compare the significance levels of trade integration in
influencing business cycles, two policy variables were added to this model. These
variables are fiscal and monetary policies, included to differentiate how
significant integration of trade is positively affecting economic cycles. Fiscal
linkage was approached using demand spill over and budget deficit. The other
policy variable, monetary policy, is the correlation of the M2 growth rates and real
GDP of the trading partner country.
Setup Model Specification
To analyse the relationship between the correlation output and trade,
Frankel and Rose (1998) formulated a panel regression equation as follows
(Misztal 2013):
Corrind-j = c + αTIIind-jt + βIITind-jt + εind-j
(5)
where Corrind-j is the correlation of business cycles between country ind
(Indonesia) and j (its trading partner country), TIIind-jt is the intensity index of
commerce between bilateral country ind (Indonesia) and j (its trading partner
nation) throughout the time period given and IITind-jt is the intra-industry trade
between bilateral country ind (Indonesia) and j (its trading partner country)
throughout 1990–2013. This research adapted the model developed above. Trade
integration between Indonesia and ASEAN+6 was expected to improve the
synchronisation of business cycles of these countries. Since trade integration
consists of two proxies, namely, IIT (intra-industry trade) and TII (trade intensity
index), adapted from the Shin and Wang model (2003), fiscal and monetary policy
variables were used to represent the level of significance and accuracy in the
previous model:
Corr (ind,j)t = α0 + α1 IIT (ind,j)t + α2 TII (ind,j)t + α3D_Spill(ind,j)t +
α4BD_fiscal(ind,j)t + α5mon(ind,j)t + εkt
(5)
where Corr (ind,j)t is the business cycle nexus between Indonesia (nation ind) and
other ASEAN+6 countries (country j) in period t. IIT (ind,j)t is the intra-industry
trade between Indonesia (country ind) and other ASEAN+6 countries (country j) in
period t, and TII is the trade intensity index between Indonesia (country ind) and
other ASEAN+6 countries (country j) in period t. D_Spill(ind,j)t is the demand
spill over between Indonesia (country ind) and other ASEAN+6 countries (country
j), which TII is multiplied with ratio of the real GDP of other ASEAN+6
countries. The remaining variables are fiscal and monetary variables.
BD_fiscal(
SYNCHRONISATION BETWEEN INDONESIA AND ASEAN+6
RIZKI PRAMITASARI
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
ii
STATEMENT OF ORIGINALITY
I, Rizki Pramitasari, hereby declare that the master thesis entitled “Trade
Integration and Business Cycle Synchronisation between Indonesia and ASEAN+6” is
my original work under the supervision of Advisory Committee and has not been
submitted in any form and another higher education institution. This thesis is submitted
independently without having used any other sources or means states therein. Any source
of information originated from published and unpublished work already stated in the part
of references of this thesis. Herewith I passed the thesis copyright to Bogor Agricultural
University.
Bogor, August 2016
Rizki Pramitasari
H151137154
iv
RINGKASAN
RIZKI PRAMITASARI, Integrasi Perdagangan dan Sinkronisasi Siklus Bisnis antara
Indonesia dan ASEAN+6. Dibimbing oleh IMAN SUGEMA dan FLORIAN PLOECKL.
Dalam beberapa tahun terakhir, sinkronisasi siklus bisnis telah
menimbulkan kekhawatiran di antara para pembuat kebijakan global. Penelitian
ini sangat penting dalam memutuskan apakah kerjasama regional menciptakan
serikat mata uang atau tidak. Manfaat dalam membuat area mata uang optimum
akan maksimal ketika siklus bisnis dari negara-negara anggota kelompok
memiliki kemiripan karakteristik. Hal ini diyakini bahwa faktor utama yang
mempengaruhi harmonisasi siklus bisnis adalah integrasi perdagangan. Saluran ini
secara langsung dapat mempengaruhi siklus ekonomi melalui perdagangan intra
industri dan intensitas perdagangan.
Sebagai negara berkembang, Indonesia memiliki tujuan untuk memperluas
kerjasama dengan beberapa mitra dagang, yaitu ASEAN+6. Teori menunjukkan
dua kesimpulan yang berbeda tentang pengaruh integrasi perdagangan pada
sinkronisasi siklus ekonomi. Peningkatan perdagangan dengan negara-negara lain
dapat menyebabkan siklus bisnis mereka bergerak di divergen atau konvergen.
Hal ini tergantung pada dominasi perdagangan antar-industri dan perdagangan
intra industri karena integrasi perdagangan dapat dihitung dari perdagangan intra
industri ataupun intensitas perdagangan. Perdagangan antar-industri mengarah
untuk mengurangi korelasi siklus ekonomi antara mitra dagang.
Integrasi perdagangan dapat diestimasi dengan perhitungan perdagangan
intra industri dan indeks intensitas perdagangan. Perdagangan intra industri diukur
dengan indeks Grubel-Lloyd (indeks GL), berdasarkan SITC (Standard
International Trade Classification) yang dibuat oleh Perserikatan Bangsa-Bangsa
(PBB) dan digunakan untuk mengklasifikasikan ekspor dan impor suatu negara
yang dapat dibandingkan di tahun yang berbeda. Sedangkan, intensitas
perdagangan adalah seberapa intens negara dalam menjalin perdagangan bilateral.
Pendekatan ini dapat diukur menggunakan data ekspor bilateral, data impor
bilateral dan gabungan keduanya.
Beberapa studi menunjukkan bahwa implikasi dari peningkatan
perdagangan dengan beberapa negara lainnya akan menyebabkan kenaikan
pertumbuhan pendapatan yang membawa sinkronisasi dalam siklus bisnis antar
negara tersebut. Siklus bisnis atau juga dikenal sebagai siklus ekonomi diyakini
akan bergerak sepanjang waktu sebagai dampak dari globalisasi. Pengaruh dari
mitra dagang negara lain telah menjadi faktor penting untuk menentukan fluktuasi
siklus bisnis karena hubungan perdagangan yang lebih intensif antara negara akan
mempercepat ekspor dan impor. Akibatnya, siklus bisnis berfluktuasi dan ini
terjadi karena peningkatan pendapatan negara. Hal ini berdampak pada
permintaan barang dari luar negeri yang juga meningkat.
Integrasi ekonomi ASEAN merupakan bentuk kerja sama ekonomi dengan
negara-negara di Asia Tenggara. ASEAN secara resmi diberlakukan oleh
ASEAN-6 negara (Indonesia, Filipina, Thailand, Singapura, Brunei, dan
Malaysia) pada 1 Januari 2003 dan yang terakhir, ASEAN-4 negara (Vietnam
mulai berlaku pada tahun 2006, Laos dan Myanmar pada tahun 2008. Saat ini,
kerja sama ini lebih besar dan anggota yang ditambahkan ke dalam ASEAN+6
yang diharapkan memiliki dampak positif yang besar dan meningkatkan
pertumbuhan ekonomi.
Oleh karena itu, penelitian ini bertujuan untuk menyelidiki adanya
hubungan antara integrasi perdagangan dan siklus bisnis antara Indonesia dan
ASEAN+6 selama periode 1990-2013 dengan menggunakan model panel fixed
effect. Hasil penelitian menunjukkan bahwa integrasi perdagangan Indonesia dan
ASEAN+6 masih lemah. Perdagangan intra industri menyebabkan dampak negatif
dari sinkronisasi siklus bisnis antara Indonesia dan ASEAN+6.
Selain itu, penelitian ini menemukan bukti pengaruh yang tidak signifikan
dari intensitas perdagangan antara Indonesia dan ASEAN+6 dengan sinkronisasi
siklus bisnis. Hasil ini, dengan demikian, menunjukkan bahwa peningkatan daya
saing intra industri yang lebih efektif dan efisien, sangat diperlukan untuk
memberikan dampak positif dari sinkronisasi siklus bisnis antara Indonesia dan
ASEAN+6. Akhirnya, pertimbangan ini bisa menciptakan konvergensi siklus
ekonomi antara negara-negara anggota Indonesia dan ASEAN+6.
Kata kunci: siklus bisnis, integrasi perdagangan, perdagangan intra industri,
intensitas perdagangan, panel fixed effect model
vi
SUMMARY
RIZKI PRAMITASARI, Trade Integration and Business Cycle Synchronisation
between Indonesia and ASEAN+6, Under supervision of IMAN SUGEMA and
FLORIAN PLOECKL.
In recent years, business cycle synchronization has gained interest and
caused concern amongst policy makers globally. This study is essential in
deciding whether regional partnership creates a currency union or not. Benefit in
making optimum currency area outweighs when the business cycle of the member
countries of a group is more similar. It is believed that the main factor affecting
harmonisation of business cycle is trade integration. This channel could directly
influence economic cycle through intra industry trade and trade intensity.
As an emerging country, Indonesia has a purpose to expand cooperation to
some trading partners, namely ASEAN+6. Theorists have prompt to draw the two
different conclusions about the effect of trade integration on synchronization of
economic cycle. An increasing trade with other countries could cause the business
cycle they move in divergent or convergent. It depends on the dominance of interindustry and intra industry. Inter-industry trade leads to reduce the correlation of
economic cycles between trading partners. For example if the trade occurs as the
theory of Heckser-Ohlin or Ricardo, the greater industry specialization result in
less synchronised of the business cycle because trade integration can be proxied
from intra industry trade and trade intensity. However, when intra-industry trade
is more dominated, the correlation of business cycles is more synchronize in the
region.
Trade integration can be estimated by Intra industry trade (IIT) and trade
intensity index (TII). IIT is measured by Grubel-Lloyd index (GL index),
determining specific industry, owned by a country in a given year. This specific
industry is based on SITC (Standard International Trade Classification) is a
classification of goods created by the United Nations (UN) and used to classify
exports and imports of a country which can be compared in a different year.
Whereas, trade intensity is how intense of countries in generating bilateral trade.
This approach can be measured using bilateral export data, bilateral import data
and the last is that mix of them.
Some studies suggest that an important implication of the rapid increase in
trade with some other countries is on an architecture of integrated trade, then the
latter, will lead to the rise in growth of revenue which bring synchronization in
business cycle. The business cycle or also known as the economic cycle is
believed to be moving all the time as the impact of globalization. The influence
from important country trading partners has become an essential factor to
determine business cycle fluctuations. The more intensive trade relation between
countries will accelerate the export and import. Consequently, the business cycle
fluctuates and this occurs owing to the increase of income of country. As a result,
demand of goods from abroad also increases.
ASEAN economic integration is a form of economic cooperation to
countries in Southeast Asia by establishing ASEAN Free Trade Area. ASEAN
was officially enacted by ASEAN-6 countries (Indonesia, Philippines, Thailand,
Singapore, Brunei and Malaysia) on January 1, 2003 and the latter, ASEAN-4
countries (Vietnam came into effect in 2006, Laos and Myanmar in 2008.
Currently, this cooperation is larger and the member is added into ASEAN+6
which is expected to have big positive impacts and boost the economic growth.
Therefore, this study aims to investigate the existence of relationships
between trade integration and business cycle co-movement between Indonesia and
ASEAN+6 over the period 1990 to 2013 using a panel fixed effect model. The
results indicate that trade integration of Indonesia and ASEAN+6 is still weak.
Intra industry trade leads to negative impact of business cycle coherence between
Indonesia and ASEAN+6. Also, this study finds insignificant impact of the trade
intensity index between Indonesia and ASEAN+6 nations to business cycle
coherence.
Since there was a contrary effect in this study, it is important to change the
trade pattern to alter the impact on business cycles from negative to positive.
Otherwise, increase in trade integration between Indonesia and ASEAN+6 will
lead to negative impacts on business cycle harmonisation among these nations.
Finally, these considerations could create convergence of economic cycle between
Indonesia and ASEAN+6 member countries.
Key words: business cycle, trade integration, intra industry trade, trade intensity,
panel fixed effect model
viii
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TRADE INTEGRATION AND BUSINESS CYCLE
SYNCHRONISATION BETWEEN INDONESIA AND ASEAN+6
RIZKI PRAMITASARI
Master Thesis
As a requirement to obtain a degree
Master of Science in
Economics Program
POSTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2016
x
Externally Advisory Committee Examiner: Prof. Dr. Ir. Rina Oktaviani, MS
Thesis Title
Name
NIM
: Trade Integration and Business Cycle Synchronisation between
Indonesia and ASEAN+6
: Rizki Pramitasari
: H151137154
Approved
Advisory Committee
Dr.Florian Ploeckl
Dr.Ir. Iman Sugema, M.Ec.
Agreed
Coordinator of Major
Economics
Dean of Postgraduate School
Dr. Ir. Dahrul Syah, M.Sc, Agr
Dr. Lukitawati Anggraeni, SP, M.Si
Examination Date: 5 August 2016
Submission Date:
ACKNOWLEDGEMENT
"Say, "Indeed, my prayer, my rites of sacrifice, my living and my dying are for
Allah , Lord of the worlds." [6:162], hereby I would like to express my greatest
gratitude to God Almighty for His blessing which bring me to accomplish this mini
dissertation. I would also like to extend my gratitude towards my supervisor, Dr. Ir.
Iman Sugema, M.Sc and Dr. Florian Ploeckl, for their excellent support, guidance and
brilliant contribution for this dissertation.
Additionally, I would express my warm thanks to Australia Awards Scholarship,
Ministry of Trade, Bogor Agricultural University and the University of Adelaide for
providing sponsorship to undertake my study.
I am thankful to my parents, my nuclear family, my mother in law and my beloved
husband, Ami Suhelmi for support me spiritually and cheer me up when I gave up in
writing my thesis.
Also, I thank my relatives, my peers, my big Indonesian family in Adelaide and
anybody who has supported and encouraged me throughout my study.
Last but not least, I also wish to thank my fellow colleagues, Aksa Nugraha and
Amelia Sitohang.
Bogor, August 2016
Rizki Pramitasari
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TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLE
LIST OF APPENDIXS
1 INTRODUCTION
Background
Problem Formulation
Research Objective
Research Benefit
Scope of Research
2 LITERATURE REVIEW
Indonesia and ASEAN+6
Trade Integration
Intra-Industry Trade
Business Cycle Synchronisation
Measuring Trade Integration and Determinants of Business Cycle
Link Between Trade Integration and Business Cycle
Research Hypothesis
3 DATA AND METHODOLOGY
Data
Operational Definition
Dependent Variable
Independent Variable
Setup Model Specification
Estimation and Methodology
Fixed Effects
Random Effect
Hausman Test
Wooldridge Test
4 RESULTS AND DISCUSSIONS
Intra Industry Trade between Indonesia and ASEAN+6
Trade Intensity Index between Indonesia and ASEAN+6
Panel Regression Model Test
Policy Implications for Indonesia
5 CONCLUSION
REFERENCES
BIOGRAPHY
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LIST OF FIGURES
1 Intra-Regional Trade of ASEAN, ASEAN+3 and ASEAN+6
2 Intra-Industry Trade between Indonesia and ASEAN+6
3 Forms of Economic Integration (Griffin &Pustay 2010)
4 The Business Cycle Phases (Schmidt 2004)
5 Business Cycle Synchronisation Channel (Mizstal 2013)
6 Intra Industry Trade between Indonesia and ASEAN+6
7 Trade Intensity Index between Indonesia and ASEAN+6 (export)
8 Trade Intensity Index between Indonesia and ASEAN+6 (import)
9 Trade Intensity Index between Indonesia and ASEAN+6 (export - import)
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LIST OF TABLES
1 Data Types
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LIST OF APPENDIXS
1 Result of Wooldridge Test
2 Result of Hausman Test
3 Panel Fixed Effect Regression Results
4 Panel Fixed Effect with Time Dummies Regression Results
5 Panel Random Effect Regression Results
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1 INTRODUCTION
Background
For over half a century, expansion of international trade activity has made a
substantial contribution to Indonesia’s economic growth. The influence from
important trading partners such as ASEAN+6 has become an essential factor for
Indonesia’s pattern of growth, and could lead to stronger economic integration.
Trade integration leads to wider market access and more effective trade and,
ultimately, has a positive impact on macroeconomic variables, resulting in
economic growth.
According to Misztal (2013), increased trade with other countries can cause
their business cycles to diverge or converge. Business cycle implies the long-term
cyclical part of output growth, which moves constantly because of globalisation
(Haan et al. 2008). Studies on business cycles or economic cycles have been taken
into account in an increasingly integrated global economy. As a consequence, one
internal or external variable shock will lead to worldwide economic fluctuations.
In the long term, the degree of this dynamic co-movement will form an economic
cycle known as the business cycle (Jansen and Stockman 2014).
There are several channels through which the harmonisation of business
cycles between two or more countries can be increased. One of these is through
trade integration. Many researchers believe that there is a strong correlation
between trade integration and the business cycle. When integrated nations have
highly dominating intra-industry trade, a cross-country spill over effect may
reduce the potential for asymmetric shock and create business cycles that are more
synchronised. However, if inter-industry trade dominates the trade pattern, less
harmonisation of the cycles would be expected.
Source: World Development Indicator, 2014
Figure 1. Intra-Regional Trade of ASEAN, ASEAN+3 and ASEAN+6,
by Country Group, 1990–2014
9
In today’s globalised world, regional integration partnerships have
tremendous benefits, accelerating large markets and trade scale cooperation, and
developing vital aspects for economic growth, especially in trade in services and
merchandise and investment (Sen et al. 2013). EEU is the first role model which
successfully improve its members output. This regional partnership has inspired
other regions to embrace established integration partnership.
Problem Formulation
Indonesia is one of the largest countries in ASEAN and internationally is
considered to have high economic potential. This country has a strong
commitment to enhancing trade cooperation through a regional partnership. The
aim of this new formation, which is called ASEAN+6, is to accelerate economic
growth, through larger markets and scale of trade, and cooperation in energy,
foods and other vital aspects of economic development. This regional trade
network is considered the most suitable union for improving regional economic
performance, especially in trade and investment (Sen et al. 2013). As shown in
Figure.1, the share of intra-regional trade in ASEAN, ASEAN+3 and ASEAN+6
has shown a moderate rise in recent years, from about 16.97% in 1990 to around
24.01% in 2014 for ASEAN and from 33.04% in 1990 to 42.25% in 2014 for
ASEAN+6. The intra-regional trade share increased throughout the two decades.
In the future it is expected to rise and reach the integrated economic goal of
ASEAN+6. Because of Indonesia’s increased trade volume with ASEAN+6, it is
important to examine whether this has led to more integration in trade and
affected Indonesia’s business cycle coherence with ASEAN+6 member countries.
Research Objective
The aim of this study is to explore the impact of trade integration on the
business cycle synchronisation between Indonesia and ASEAN+6.
Research Benefit
The results of this research will be beneficial to the Indonesian and
ASEAN+6 partnership in deciding the next step in the integration, in particular, to
policy makers and the government of the Republic of Indonesia in boosting the
trade sector, and to societies in charge within the trade sector and practitioners
who run businesses within the ASEAN+6 boundaries.
Scope of Research
This study used samples from the five major ASEAN countries, namely,
Indonesia, Malaysia, Singapore, Philippines and Thailand (ASEAN-5), plus the
six members, namely, Japan, China, South Korea, India, Australia and New
10
Zealand. The variables were intra-industry trade, trade intensity index, demand
spill over, and fiscal and monetary coordination policies from 1990 to 2013.
Variables were analysed using panel data with econometrics software E-Views 8
and Stata 13 to investigate the influence of these variables on the business cycle
synchronisation between Indonesia and ASEAN+6.
This study is structured in six sections, starting with the introduction.
Section 2 provides the background of Indonesia and ASEAN+6 and an overview
of trade integration, intra-industry trade and synchronisation of business cycles.
Next, Section 3 reviews the theories on trade integration and business cycle
synchronisation. Section 4then presents the main specifications and data used in
the panel data, and Section 5 discusses the results of the analysis. Finally, Section
6 provides the final conclusions, including how the results can be used as policy
options to cope with the existing issues.
2 LITERATURE REVIEW
To begin with, it is important to outline Indonesia and ASEAN+6, trade
integration and business cycles, to increase understanding when assessing the
effects of trade integration on business cycle synchronisation between Indonesia
and ASEAN+6.
Indonesia and ASEAN+6
Indonesia is ranked as the ninth largest economy in the world (World Bank
2015). By the end of 2014, Indonesia’s gross domestic product (GDP) had
reached US$ 2.55 trillion. This position surpassed the GDP of the United
Kingdom, which was only US$ 2.4 trillion in 2014. Along with this improvement,
Indonesia strongly supports the establishment of economic integration among the
countries of Southeast Asia. ASEAN economic integration is a form of economic
cooperation among countries in Southeast Asia that was created by establishing
the ASEAN Free Trade Area. Currently, this cooperation has been expanded by
members being added to form ASEAN+6, which is expected to have a positive
impact and boost economic growth (Hamanaka 2014).
ASEAN+6 was given concrete shape by the East Asia Summit (EAS)
leaders, comprising 16 participating countries, namely, the 10 ASEAN countries,
Australia, China, India, Japan, the Republic of Korea and New Zealand. EAS
participants have agreed to maintain EAS as an open, inclusive, transparent and
outward-looking forum that enables participants to hold strategic discussions on
various actual themes in the region. Indonesia provides a major contribution and
is committed to strengthening regional trade in ASEAN+6. However, because of
the dramatic rise in trade between Indonesia and the other ASEAN+6 member
countries as shown in Figure 2, from about 48 Million in 1990 to roughly 347
Million in 2013. This increase in number does not merely lead to harmonising the
business cycle in these nations.
11
Source: World Integrated Trade Solution, 2015
Figure 2. Intra-Industry Trade between Indonesia and ASEAN+6
Trade Integration
Moneta (2006) stated that economic integration has the same principles and
mechanisms as free trade, which refers to the elimination of national trade
boundaries. There are five different stages of economic integration, as
documented in Figure 3. Marrewijk (2007) asserted that the highest degree of
economic integration is political union, followed by economic union, common
market, custom union and free trade area. A deeper integration scheme such as the
European Union (EU) could be classified as a common market (Schwarze, 2006),
whereas a shallow integration, for instance, ASEAN+6, is integrated as a free
trade area, which can change in structure to become intense trade ties and closer
economic links. Factors in forming regional trade integration are:
1. Establishing security and policies to ensure trade diplomacy for neighbouring
countries. For example, the formation of the European Economic Community
was conducted through a trade cooperation policy in the region (Pentecote et
al. 2015; Rose and Stanley 2005). Trailing far behind is ASEAN+6 in a free
trade zone, established to reform mixed policies with a comprehensive pact
involving trade facilitation.
2. Managing the global value chain and trade friction. A feature of the ASEAN+6
trade scheme is its intra-regional production network approach. Indonesia, the
largest country among the ASEAN countries, has increased the efficiency and
effectiveness of trade production. China, Japan and Korea are among the
strongest economic powers in East Asia and are considered to be strengthening
the ASEAN+6 partnership. Similarly, India plays a prominent role in
enhancing this integration because it accounts for a large trade share within the
ASEAN countries. The remaining countries, Australia and New Zealand, have
liberalised their trade with ASEAN and its neighbours.
3. Increasing capacity building for development and cooperation in the region in
the form of eliminating discriminatory trade barriers to boost efficiency,
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productivity and competitiveness as well as reducing costs and trade and
investment risks. The formation of the ASEAN+6 Free Trade Area was
intended to reduce, even remove non-tariff barrier.
4.
Source: Griffin and Pustay, 2010
Figure 3. Forms of Economic Integration
More intensive trade relations between countries accelerates exports and
imports. As a result, the demand for goods from abroad also increases, leading to
a rise in the income of a country.
Intra-Industry Trade
By influencing business cycles, intra-industry trade is an essential aspect of
trade integration. Intra-industry trade is the trade in the same industry, while interindustry trade are global tradingin different product groups (Pittiglio 2012). Interindustry trade is based on traditional trade theory, such as the Heckscher–Ohlin
(H–O) model (Zhang 2006). However, in today’s growing world, intra industry
trade is more essential to expect how trade integration in influencing business
cycle coherence.
There are various classifications and definitions of intra-industry trade.
Grubel and Lloyd pioneered the research in intra-industry trade in 1975 and
certain empirical tests employ the Grubel–Lloyd (GL) index (Van and Vijk 2002).
According to Lancaster (1980), traditional trade reflects a natural and acquired
comparative advantage, and inter-industry trade notices endowment factors
following the proportional factor theory, which is based on the international trade
theory of Heckscher and Ohlin (Zhang 2006). Salvatore (1995) asserted that intraindustry trade reflects economies of scale. The emergence of intra-industry trade
is based on the advantageous consideration of economies of scale in the
production process. Intra-industry trade benefit consumers because it gives them
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access to a more diverse choice of products and lower prices as a result of
productivity improvement through economies of scale.
Business Cycle Synchronisation
Studies about business cycles or economic cycles have been taken into
account amongst researchers in an increasingly integrated global economy.
Business cycle implies the long-term cyclical part of output growth (Haan et al.
2008), where the real GDP of each country is used as the indicator. This
measurement relates to a global economy. As a consequence, one internal or
external variable shock will lead to worldwide economic fluctuations. In the long
term, this dynamic co-movement will form an economic cycle known as the
business cycle (Jansen and Stockman 2014). The business cycle can also be
defined as the deviation of output against expansion and contraction trends (Botha
2004; Misztal 2013).
Source: Schmidt, 2004
Figure 4. The Business Cycle Phases
As shown in Figure 4, the red line is the trend of potential GDP, calculated
from the blue line, which is the actual real GDP. Each cycle has two types of
turning points, the peak and the trough (Camacho et al. 2008). The peak point
shows the positive output gap, and the negative output gap is shown from the
trough or depression. Both turning points indicate the direction of the cyclical
movement, which becomes an indicator of change in contraction or expansion
from period to period. Schmidt (2004) stated that there are four stages in an
economic cycle, namely, the depression stage, the expansion phase, the phase of
contraction and the recovery phase. The first stage is the depression period, that is,
a period of rapid decline in aggregate demand, followed by a low level of output
and high unemployment rates, which gradually reaches the lowest point.
The expansion phase is the initial period of prosperity when the economy is
undergoing expansion that exceeds the height of the previous stage. Demand and
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production rise, leading to a rise in inflation and interest rates. Business expansion
thereby reaches a peak followed by a phase of contraction, when production
factors such as sales, prices and employment begin to decline. The last phase is
the phase of recovery, when economic activity advances to a new period of
expansion and a rise in the business cycle. Recovery is a transitional phase
beginning from the lowest economic point and then returning to the original
starting point. In general, the strongest growth occurs in the recovery phase, but
the duration is shorter than a recession (Botha 2004).
Source: Mizstal, 2013
Figure 5. Channel through Which the Intensity of International Trade Exerts
Influence on Business Cycle Synchronisation
Business cycle synchronisation is important for ASEAN+6because of its
impact. According to the theory of the business cycle, when there is an economic
boom, credit moves uncontrollably, developing into moral hazards so that
prosperity turns into a crisis. In this condition, business cycle is considered an
imbalance in the economic rhythm. The Asian crisis in 1997 motivated
researchers to assess the business cycle and its leverage comprehensively.
Following the Asian crisis, Indonesian trade revived, mainly with other major
ASEAN and ASEAN+6 countries. Massmann and Mitchell (2004) stated that comovement of a business cycle in the same direction between two or more
countries indicates that the business cycle is synchronised. This synchronisation
reduces the cost of the economy.
As a result, the economy becomes more integrated, and then it induces the
fluctuation of business cycle. The theory of traditional trade predicts that trade
openness can lead to specialisation in production (Song 2011). Therefore,
integrated economy will result in a decline in the correlation of business cycles.
However, if trade is dominated by intra-industry trade, the business cycle will
become more symmetric (Dees and Zorell 2012).
In theory, there are three causes of the business cycle co-movement. First,
country-specific shocks are quickly spread to other nations. Secondly, external
shocks have the same effect for all nations. Third, specific shocks have in the
15
same effect on economic sector in different countries. Differences between
countries’ business cycles can occur for many reasons. Despite experiencing the
same shocks, each country may have a different effect or response, such as in the
reaction to national output. As the economies of Indonesia and ASEAN+6 are
forecasted to become more integrated, this investigation is needed to pursue
outstanding growth among the members.
Measuring Trade Integration and Determinants of Business Cycle
The baseline model of trade integration is approached by using trade
intensity and intra-industry trade (Misztal 2013). Trade intensity is the intensity of
countries in generating bilateral trade. This approach can be measured using
bilateral export data, bilateral import data or a mix of these. For bilateral exports,
trade intensity can be calculated as the bilateral export divided by the total exports
of both countries. Likewise, trade intensity for imports can be calculated as the
bilateral import divided by the total imports of both countries. Similar calculations
for export and import are done by dividing the bilateral export and import by the
total exports and imports of both countries.
Another parameter to measure trade integration is the intra-industry trade
index. It was observed by Reinert (2012) that approximately one-third of world
trade takes place as intra-industry trade, especially in manufactured goods among
the developed or high-income countries of the world, where it probably accounts
for up to 70% of trade. Globally, intra-industry trade is also becoming more
important over time, particularly in Asia. Intra-industry trade arises if a country
simultaneously imports and exports similar types of goods or services (Shelburne
2002). To measure intra-industry trade, the most commonly used method is the
GL index (Greenaway and Milner 1986), based on the standard international trade
classification (SITC). The SITC is a classification of goods created by the United
Nations (UN) used to classify the exports and imports of a country, which can be
compared across different years.
To measure a business cycle, the annual GDP is statistically smoothed using
econometric software, to obtain the actual GDP and potential GDP. The Hodrick–
Prescott (H–P) filter, first differencing and quadratic de-trending are amongt he
statistical smoothing techniques that are popular and widely constructed in studies
of potential output calculation (Saiki and Kim 2014). Following Frankel and Rose
(1998) and Cortinhas (2007), the H–P filter can shrink the effects of cyclical GDP
by removing trend components to estimate the non-trend (cyclical) component.
The non-trend (cyclical) component is considered the difference between the
original series and its trend. Instead of a long-term component, smoothing time
series can show output gap deviations by decomposing the actual GDP and
potential GDP. Hence, the H–P filter method is commonly used to identify long
time series economic trends with no structural breaks.
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Link Between Trade Integration and Business Cycle
The theories of trade integration and business cycles have led to an increase
in the attention paid to these empirical questions. Krugman (1991), who follows
the Heckscher–Ohlin trade theory, asserted that growing trade activity has led to
higher specialisation of production. Krugman added that this trade pattern has
resulted in different direction co-movement of business cycles. Hence, trade
integration has a diverging effect on business cycles.
In contrast, a substantial number of empirical studies have documented a
significant and clear positive relationship between trade integration and business
cycle synchronisation. Research by Cortinhas (2007), who adopted the modified
variation of the Frankel and Rose (1998) model, led to an empirical result that
countered that of Krugman (1991). Corthinhas found that intra-industry trade is
the main factor leading to more harmonised business cycles in the economies of
ASEAN without Indonesia. He examined the effect of intra-industry trade on
business cycle synchronisation in five ASEAN countries over the period 1962–
1996, using panel two-stage least squares (TSLS) estimations. The developed
independent variables for intra-industry trade were the log of distance and a
dummy for land borders. According to his empirical findings, although it is the
largest economy, Indonesia appeared to be less integrated than other smaller
member countries. Hence, Cortinhas ignored Indonesia in his alternative
combinations, unless the trade would lead to reducing the correlation of business
cycles.
Similarly, Shin and Wang (2003) emphasised that an increase in whole trade
should be accompanied mainly by intra-industry trade; it can then induce more
positively converged business cycles. The data used in their paper were the annual
GDP from 12 Asian economies over two decades, from 1976 to 1997, and trade
volume data. They set up four coefficients in the regression: bilateral trade
intensity, employing Frankel and Rose’s (1998) model; intra-industry trade
intensity, adopting Grubel and Lloyd’s (1975) equation; and two policy
coordination channels. These policy shocks were split into the degrees of
coordination of fiscal and monetary policy. The fiscal coordination channel was
investigated by calculating the budget deficit ratio to GDP, and the degree of
monetary coordination was constructed by examining the correlation of the M2
growth rates. Consistent with recent investigations, in this empirically analysed
paper, these authors concluded that a rise in intra-industry trade between Korea
and other Asian countries has strengthened business cycle coherence.
In line with this, in a separate paper, Shin and Wang (2005) suggested that
the deeper the trade integration the closer the business cycle in East Asia. They
extended four possible variables, namely, demand spill over, inter-industry trade,
intra-industry trade and policy coordination of 12 Asian nations, during 1976 and
1997. Estimated coefficients were tested using panel ordinary least square (OLS)
fixed effect, and real GDP correlation was estimated using first differences in
logarithms and the H–P filter. Shin and Wang emphasised that in tackling
unobservable components that are country specific in time series data of trade,
OLS fixed effect regression is more suitable than panel regression with pooled
data. Their result confirmed that only the second channel, which lowered the
coherence of business cycles. Also, trade formation is predominantly integrated
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by intra sector in East Asia. This finding relates to the authors’ previous work, in
2003, that found that intra-industry trade is the primary channel for fostering
business cycle coherence between Korean and Asian countries.
Following this study by Shin and Wang (2005), Rana (2007) reviewed the
importance of intra-industry trade and, to a lesser extent, monetary coordination in
synchronising business cycles in East Asia. The methods used in this paper were
value at risk (VAR) and pooling regression. Using the annual GDP data of 11
ASEAN+3 nations spanning a decade, between 1989 and 2003, and the H–P filter,
the author found similar results to those of Shin and Wang (2005). He also used a
different proxy by correlating short-term real interest rates to estimate financial
channels.
For the sake of comparison, Park (2013) revealed that monetary aggregate
was a key aspect in explaining business cycles in East Asia, notably in Korea,
Malaysia and the Philippines. Data used in this study were quarterly real GDP
data, real private consumption expenditure, real gross capital formation, real stock
of M2, real government expenditure, productivity, oil price, and total export and
import in eight nations of East Asia. Unlike previous literature on the bilateral
trading nexus, this study modified the dynamic factor model of Crucini, Kose and
Otrok (2011), which measures time-varying dynamic conditional relationships.
Departing from the existing study, Duval et al. (2014) proposed an
integrated trade and business cycle nexus in Asia by using 34 advanced countries
and 29 emerging economies over two decades. By retaining the dynamic factor
model with a gravity and panel approach, these authors found that worldwide
factors can explain business cycle evolution. Additionally, by examining trade
intensity, trade specialisation and intra-industry trade as a proxy for trade
integration, and banking and portfolio integration as a proxy for financial
integration, they found that statistically examined trade integration has driven
business cycles in Asia. Specifically, its impact in crisis periods outweighs that in
tranquil times. This result was confirmed in a later empirical approach by Dai
(2014). The method used in his paper was panel regression. Dai focused on how
newly industrialising economies, ASEAN-4, and Asian business cycles have comoved with the business cycles of China, Japan and the United States. He
attempts that trade and financial linkage indicate the importance of Japan, China
and the United States in Asian business cycles.
In line with this finding, Inklaar et al. (2008) stated that trade intensity,
financial similarities and specialisation (intra-industry trade) have robust effects
on business cycle coherence in certain Organisation for Economic Co-operation
and Development (OECD) countries. These effects were not as great as those
reported by Frankel and Rose (1998). Instead of using instrument variables, they
constructed a multivariate model. This approach enabled them to highlight
specialisations and degrees of economic policy similarities. The measures of
economic activity used in this study were the monthly Index of Industrial
Production and the quarterly GDP determined by the H–P filter.
Kumakura (2006) noted that the electronic industry as a specific
manufacture positively enhances business cycle coherence. He followed Frankel
and Rose’s model in 13 Asia–Pacific nations and computed the measures using
first differenced annual real GDP data, bilateral trade data and specific industries.
This business cycle is a key determinant in entering a unified currency area.
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Hence, growing trade with no industry-specific shock influencing the business
cycle does not necessarily transform an optimum currency area in Asia–Pacific
nations.
According to Saiki and Kim (2014), business cycle co-movement affected
by intra-industry trade and vertical intra-industry trade increased in both Europe
and East Asia. However, increased intra-industry trade and vertical intra-industry
trade in East Asia were faster than those in the Eurozone because of the role of the
supply chain. The data used covered four decades, from 1970 to 2011. The pattern
of the business cycle was explained by quarterly real GDP filtered using year over
year growth, the Baxter–King filter and the H–P filter, and a detailed investigation
of intra-industry trade and vertical intra-industry trade were calculated using the
GL index. A separate empirical study by Rana, Cheng and Chia (2011) had a
similar empirical finding. The fixed effect panel method was used to estimate
trade and business cycles, whereas gravity equation was used to calculate the trade
pattern of countries.
However, a number of empirical studies suggest that an important channel
contributing to business cycle co-movement is trade integration. By estimating a
simultaneous equation model based on the six variables panel approach within the
27 EU countries from 1995 to 2012, Antonakakis and Tondl (2014) verified that
trade and foreign direct investment have fostered business cycle co-movement,
whereas specialisation has an insignificant impact on business cycles. In addition,
these writers found that poor fiscal coordination and exchange rate fluctuations
have impeded economic cycle synchronisation in the EU. This leads to further
policy implications.
In essence, business cycles have been playing a crucial role in the globalised
economy. In this regard, intra-industry trade is the most obvious parameter for
approaching trade integration. It is closely related with business cycle comovement. In contrast, influences from other channels, namely, financial and
policy channels, are not as strong as trade channels. A review of empirical
literature about trade integration and business cycles from different countries
indicates that the economic characteristics of member countries have an influence
on the strength and nature of the impact of trade integration on business cycle
harmonisation.
Research Hypothesis
Based on asymmetric information, an increase of trade will cause an
increase of business cycles synchronisation between Indonesia and ASEAN+6.
3 DATA AND METHODOLOGY
This section explains the data used and the econometric methods used to test
trade integration and the business cycle nexus. By employing the modified
variation of Frankel and Rose’s (1998) model, this empirical study assessed
whether intra-industry trade is the main factor influencing business cycle
harmonisation among the economies of Indonesia and ASEAN+6.
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Data
This investigation used secondary annual, cross-sectional annual time series
data for 11 countries for the period 1990 to 2013. The data were obtained from
International Financial Statistics and the International Monetary Fund (IFS-IMF),
and World Integrated Trade Solution and the World Bank (WITS-World Bank).
The data set analysed in this study includes real GDP, total nominal exports, total
nominal imports, bilateral exports between countries, and bilateral imports
between Indonesia and ASEAN+6 countries. ASEAN-5 (Indonesia, Philippines,
Singapore, Malaysia and Thailand) were analysed; the other ASEAN countries
were not included in this study because of their low level of bilateral trade with
Indonesia, small economies and limitations of the source. In addition, the study
included the other six partner countries, namely, Japan, China, India, South
Korea, Australia and New Zealand. These countries were added to determine the
degree of trade integration of the six countries, in line with the background of
economic cooperation expansion of ASEAN into ASEAN+6.
Two models were constructed in this investigation. The first used three
subsample periods of equal length, 1990–1997, 1998–2005 and 2006–2013, and
the second model used three similar sub sample periods but excluded the year
1998/1999. These years are considered outliers, leading to potentially distorted
results. Each model was divided into two sub models to compare the significance
of the trade variables, that is, one model with policy variables and one model
without policy variables.
Operational Definition
This study has obtained data from International Financial Statistics (IFS)
and World Integrated Trade Solution (WITS)–World Bank. All data types used in
this study and the definitions are mentioned in Table 1.
Table 1.Data Types
No. Data
1
Real GDP
2
Total Export
3
Total Import
4
Bilateral Export
5
Bilateral Import
6
M2
7
Budget Deficit
Definition
national output by using constant price
to remove inflation factor
Amount of goods and services sold to
other countries
Amount of goods and services bought
from other countries
Amount of goods and services sold to
partner country
Amount of goods and services bought
from partner country
Amount of money supply including
savings and deposits
The difference between government
spending (G) and government revenue
from taxes (T) within a certain period
Source
IFS-IMF
WITSWorld Bank
WITSWorld Bank
WITSWorld Bank
WITSWorld Bank
IFS-IMF
IFS-IMF
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Dependent Variable
The dependent variable in this regression is the bilateral correlation of
business cycles between Indonesia and other ASEAN+6 countries. Business cycle
is measured using the H–P filter. HP filter is flexible and detrending method
commonly used in economic research to remove the cyclical component of a time
series from raw real GDP data. In HP filter, a series of data can be separated into
two components, trend and cycle. It is used to obtain a smoothed-curve
representation of a time series, which is more sensitive to long-term than to shortterm fluctuations. Correlation of this output cycle can be calculated in each sub
period, taken from eight years.
Independent Variable
According to Grubel and Lloyd (1975), intra-industry trade is calculated
using the following equation:
IIT =1–∑i|exkjt–∑imkjt|/(exkjt + ∑imkjt)
(1)
Where exjt and imjt are the values of country j’s exports to Indonesia and the
values of country j’s import to Indonesia. The index ranges from zero to one. Zero
point means no intra-industry trade, while one implies its maximum level of trade
of intra industry. Intra-industry trade goes up when nations concurrently
merchandise similar types of trading services or goods. The same sector
classification is identified using the UN standard, namely, SITC.
Following Rose and Frankel (1998), three different proxies were used to test
how the intensity of bilateral trade between Indonesia and other ASEAN+6
countries affects the business cycle. The first is export data, the second is import
data and the last is a mix of these. This is also called the trade intensity index. The
trade intensity index is counted on an actual bilateral trade nexus between any
given two countries. The following equation uses the three options to measure TII
(trade intensity index):
TII EXjt = (exjt)/(EXind t + EXjt)
TII IMjt = (imjt)/(IMind t + IMjt)
TII EXind-jt = (extj + imjt)/(EXit + IMind t + EXjt+ IMjt)
(2)
(3)
(4)
EXit and IMit are country i’s total exports and import. Country I is
Indonesia’s trading partner country in ASEAN+6. Similarly, EXjt and IMjt are
total exports and imports of Indonesia’s trading partner country. An index value
that is greater indicates that a growing intensity of bilateral commerce occurs
between Indonesia and its trading partner country. However, this intensity index
still does not reveal the demand spill over effects. Thus, by adding a demand spill
over variable, as was done by Shin and Wang (2003), it can be determined
whether the business cycle is affected purely by the trade intensity index or
demand spill over.
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Additionally, to compare the significance levels of trade integration in
influencing business cycles, two policy variables were added to this model. These
variables are fiscal and monetary policies, included to differentiate how
significant integration of trade is positively affecting economic cycles. Fiscal
linkage was approached using demand spill over and budget deficit. The other
policy variable, monetary policy, is the correlation of the M2 growth rates and real
GDP of the trading partner country.
Setup Model Specification
To analyse the relationship between the correlation output and trade,
Frankel and Rose (1998) formulated a panel regression equation as follows
(Misztal 2013):
Corrind-j = c + αTIIind-jt + βIITind-jt + εind-j
(5)
where Corrind-j is the correlation of business cycles between country ind
(Indonesia) and j (its trading partner country), TIIind-jt is the intensity index of
commerce between bilateral country ind (Indonesia) and j (its trading partner
nation) throughout the time period given and IITind-jt is the intra-industry trade
between bilateral country ind (Indonesia) and j (its trading partner country)
throughout 1990–2013. This research adapted the model developed above. Trade
integration between Indonesia and ASEAN+6 was expected to improve the
synchronisation of business cycles of these countries. Since trade integration
consists of two proxies, namely, IIT (intra-industry trade) and TII (trade intensity
index), adapted from the Shin and Wang model (2003), fiscal and monetary policy
variables were used to represent the level of significance and accuracy in the
previous model:
Corr (ind,j)t = α0 + α1 IIT (ind,j)t + α2 TII (ind,j)t + α3D_Spill(ind,j)t +
α4BD_fiscal(ind,j)t + α5mon(ind,j)t + εkt
(5)
where Corr (ind,j)t is the business cycle nexus between Indonesia (nation ind) and
other ASEAN+6 countries (country j) in period t. IIT (ind,j)t is the intra-industry
trade between Indonesia (country ind) and other ASEAN+6 countries (country j) in
period t, and TII is the trade intensity index between Indonesia (country ind) and
other ASEAN+6 countries (country j) in period t. D_Spill(ind,j)t is the demand
spill over between Indonesia (country ind) and other ASEAN+6 countries (country
j), which TII is multiplied with ratio of the real GDP of other ASEAN+6
countries. The remaining variables are fiscal and monetary variables.
BD_fiscal(