BULLETIN OF MONETARY ECONOMICS AND …. Dr. Iwan Jaya Azis Prof. Iftekhar Hasan Dr. M. Syamsuddin ... Bagus Arya Wirapati, Niken Astria Sakina Kusumawardhani Making East Asian Regionalism

1ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007

BULLETIN OF MONETARY ECONOMICS AND BANKING

Directorate of Economic Research and Monetary PolicyBank Indonesia

PatronPatronPatronPatronPatronBoard of Governor Bank Indonesia

Editorial BoardEditorial BoardEditorial BoardEditorial BoardEditorial BoardProf. Dr. Anwar Nasution

Prof. Dr. Miranda S. GoeltomProf. Dr. Insukindro

Prof. Dr. Iwan Jaya AzisProf. Iftekhar HasanDr. M. Syamsuddin

Dr. Perry WarjiyoDr. Halim Alamsyah

Dr. Iskandar SimorangkirDr. Solikin M. JuhroDr. Haris Munandar

Dr. Andi M. Alfian Parewangi

Editorial ChairmanEditorial ChairmanEditorial ChairmanEditorial ChairmanEditorial ChairmanDr. Perry Warjiyo

Dr. Iskandar Simorangkir

Executive DirectorExecutive DirectorExecutive DirectorExecutive DirectorExecutive DirectorDr. Andi M. Alfian Parewangi

SecretariatSecretariatSecretariatSecretariatSecretariatToto Zurianto, MBA

MS. Artiningsih, MBA

The Bulletin of Monetary Economics and Banking (BEMP) is a quarterly accreditedjournal published by Directorate of Economic Research and Monetary Policy-BankIndonesia. The views expressed in this publication are those of the author(s) anddo not necessarily reflect those of Bank Indonesia.

We invite academician and practitioners to write on this journal. Please submityour paper and send it via mail to: [email protected]. See the writing guideon the back of this book.

This journal is published quarterly; January √ April √ August √ October. The digitalversions including all back issues are available online; please visit our stable link:√http://www.bi.go.id/web/id/Publikasi/Jurnal+Ekonomi/. If you are interested to sub-scribe for printed version, please contact our distribution department: Publicationand Administration Section √ Directorate of Economy and Monetary Statistics,Bank Indonesia, Building Sjafruddin Prawiranegara, 2nd Floor - Jl. M. H. ThamrinNo.2 Central Jakarta, Indonesia, Ph. +62-21-3818202, Fax. +62-21-3802283,Email: [email protected].

BULLETIN OF MONETARY ECONOMICSAND BANKING

Volume 13, Number 1, July 2010

Quarterly Analysis: The Progress of Monetary, Banking and Payment System

Quarter II - 2010

Quarterly Report Team, Bank Indonesia

Do Regional Trade Areas Improve Export Competitiveness? - A Case of Indonesia

Amalia Adininggar Widyasanti

The Impact of ACFTA Implementation on International Trade of Indonesia

Ibrahim, Meily Ika Permata, Wahyu Ari Wibowo

Is ACFTA Proper Strategy of Sustainable Poverty Alleviation?: Proof From The Depletion

of Saving Rate

Bagus Arya Wirapati, Niken Astria Sakina Kusumawardhani

Making East Asian Regionalism Works

Fithra Faisal Hastiadi

5

75

103

1

23

1QUARTERLY ANALYSIS: The Progress of Monetary, Banking and Payment System, Quarter II, 2010

The Indonesian economy in second quarter of 2010 demonstrated a continued

strengthening. This optimism was supported by investment and export performance that grows

higher, in line with the global economic recovery. Economic conditions continued to show an

atmosphere that supports the optimistic better economic outlook than previously thought. The

Indonesian economy in 2010 was estimated to grow towards the upper limit of the range of

5.5% -6.0% and reached 6.0% -6.5% in 2011. In terms of prices, inflationary pressure

throughout the second quarter of 2010 showed an increase caused by the volatile foods, such

as various spices and rice. Meanwhile, the administered prices group and minimal core inflation

contributed to the price development during the second quarter of 2010. Thus, overall the

year, CPI inflation in 2010 will still be in the range of the inflation target of 5% ± 1%.

The global economic recovery still continues, although shadowed by the pressure on

global financial markets and concerns about sustainability of Europe»s economic recovery. The

rate of global economic recovery in the second quarter of 2010 was expected to moderate

compared with the first quarter of 2010. Nevertheless, these developments that remain positive

have raised optimism on global recovery process. The condition was supported by the improving

conditions in developed countries, especially the United States (U.S.) and Japan, as well as a

number of countries in Asia.

In Asia, economic growth showed an increase, in except China which slowed slightly,

related to policies pursued by the Chinese government to overcome the symptoms of

overheating. European crisis has triggered pressure on global financial markets during the second

quarter of 2010, and was reflected by the significant drop in global stock markets and the

soaring Credit Default Swap (CDS) and the yield spread of PIIGS (Portugal, Italy, Ireland, Greece,

and Spain). In Asia, the flight to quality was indicated from the position of net selling by foreign

investors in the stock market, the weakening regional currencies, as well as the rising of sovereign

CDS. So far the impact crisis in Europe has only affected the global financial markets and yet to

significantly influence the overall global economic recovery. Various measures have been taken

QUARTERLY ANALYSIS:The Progress of Monetary, Banking and Payment System

Second Quarter - 2010

Author Team of Quarterly Report, Bank Indonesia

2 Bulletin of Monetary, Economics and Banking, July 2010

by countries which experienced the crisis in Europe such as the austerity program, the help of

European Union (EU) and the International Monetary Fund (IMF) and so far they are capable of

dampening the global financial market turmoil that occurred during the second quarter of

2010

Domestic economic performance the second quarter of 2010 was expected to be better

than previous projections. In the second quarter of 2010, the domestic economy was expected

to grow 6.0%, higher than the previous projection of 5.7%. This optimism was supported by

the investment performance that rises higher. Investment growth was expected to reach 10%

(yoy) in the second quarter of 2010, as a response from domestic and external demand was

getting stronger. Better investment climate was also supported by Indonesia»s sovereign credit

rating which increased along with the improvement of economic fundamentals.

By sector, economic performance in 2010 was mainly supported by trade, hotels, and

restaurants and transport and communications sector. Acceleration for higher domestic economy

needs more support, especially related to the acceleration of the implementation of infrastructure

programs.

Improved economic growth, also reflected in regional economic growth that continued

to grow strong. The performance of the local economy was sustained by the economy activities,

especially in Jakarta, West Java, East Java and Sulawesi, Maluku and Papua. Performance of the

improved regional economic development has been driven mainly by improved performance in

consumption, investment and exports. Growth in household consumption remained strong in

the area, indicated by the increasing growth of consumer credit, retail sales growth remained

high, and stable exchange rate of farmers in various regions. In addition, the regional elections,

which many of them took place in the second quarter of 2010, also played a role in increasing

local consumption. From the investment side, the increase occurred mainly in construction

investment.

Investment activities in building showed an increase in Sumatera, Jakarta and Java, Bali

and Nusa Tenggara. Construction investment activities in these areas, were mostly for the

commercial and residential property sector. In terms of exports, a considerable growth remained

in Kalimantan, Sulawesi, Maluku and Papua and Sumatra to Jakarta for mining commodities

and products, as well as in Java, Bali and Nusa Tenggara for manufacturing product.

In terms of prices, inflationary pressure throughout the second quarter of 2010 showed

an increase that came from the non-fundamental factor. The rising prices of spices and rice

commodities along the second quarter of 2010 have put a significant pressure on CPI. The

inflation of CPI in June 2010 was recorded at 0.97 (mtm), higher than previous months like in

3QUARTERLY ANALYSIS: The Progress of Monetary, Banking and Payment System, Quarter II, 2010

May, and 0.15% -0.14% in April 2010. With these developments, during the second quarter

of 2010, CPI inflation stood at 1.41 (qtq) or reached 5.05% (yoy), increased significantly

compared with the previous quarter which reached 0.99% (qtq) or 3.43% (yoy).

The high inflation of food commodities was due to the supply constraints triggered by

the disruption of production and distribution due to heavy rainfall in some areas. Meanwhile,

the price development of the administered prices group had relatively small impact on CPI

inflation. In terms of fundamentals, core inflation pressures were still relatively low, which was

sustained by the controlled expectations of inflation, the minimum external pressure and the

adequate supply response toward the rising demand. Despite the increase of inflationary pressures

in the second quarter of 2010, in general, the CPI inflation is expected to remain in the range

of the inflation target of 5% ± 1%.

The conducive global economic conditions and the strong domestic economic

fundamentals have supported Indonesia»s balance of payments (neraca pembayaran Indonesia/

NPI) in second quarter of 2010 to remain solid. The current account recorded a surplus estimated,

mainly due to the global economic recovery that continued and the rising trend of global

commodity prices.

In terms of balance sheet capital and financial (transaksi modal dan financial/TMF) of the

second quarter 2010, it is also expected to record a surplus. TMF surpluses were supported by

the re-entry of foreign capital flows in line with the global financial market turmoil which has

subsided and the repair of credit rating outlook for Indonesia. With these developments, the

national reserves at the end of June 2010 reached 76.3 billion U.S. dollars, equivalent to 5.9

months of imports and foreign debt repayments by the Government.

Along with a solid performance of balance of payments and the sustained low risks,

exchange rate is in the strong trends. When compared with the first quarter of 2010, on

average, the rupiah was appreciated by 1.58% (qtq), reaching Rp.9.110 per U.S. dollar. The

strengthening of rupiah in the second quarter was followed by the volatility-which fell from

0.57% in the first quarter of 2010 to 0.47% in second quarter of 2010.

At the end of second quarter of 2010 rupiah closed at Rp9.060 per U.S. dollar, or gained

0.33% (ptp) compared with the first quarter of 2010. Package policies issued by Bank Indonesia

on June 15, 2010 was generally positively responded by market participants both domestically

and internationally so that the pressure on the rupiah exchange rate were relieved, and further

strengthened the monetary management and the deepening of financial markets.

The overall financial market performance in the second quarter of 2010 improved, despite

temporarily depressed in May 2010. SUN market and capital markets condition gradually


improved, after a depressed negative sentiment caused by the Europe»s debt crisis in May

2010. The improvement in capital markets and securities in the second quarter of 2010 was

supported by the re-entry of foreign investors and the relieved bubble pressure on the stock

market.

On the interbank money market, the liquidity conditions during the second quarter of

2010 were sufficiently good. The extension of corridor of PUAB O/N per June 17, 2010 had an

impact on the decline in PUAB O/N rates. Parallel with the improving global conditions and

domestic fundamentals, monetary policy transmission continued to improve. This was reflected

in the decline of deposit and credit rates, and credit growth which increased was estimated to

reach 18.6% by June 2010.

From the micro-banking side, national banking conditions remained stable. This was

reflected from the maintained capital adequacy ratio (CAR) as of May 2010 amounted to

17.8%. Meanwhile, the ratio of gross non-performing loans (NPLs) remained at 3.6% with a

net ratio of 1%. In addition, bank liquidity, including liquidity in the interbank money market

sustainably improved and third-party funds (TPF) remained increasing.

It is considered that the BI Rate at 6.5% rate was still consistent with the inflation target

for 2010 at 5% ± 1% and the current direction of monetary policy were also considered

conducive for the economic recovery amid the high global risk resulted from the debt crisis in

a number of European countries. The Bank Indonesia Board of Governors Meeting on July 5,

2010 decided to maintain the BI Rate at 6.5%, with the corridor of PUAB O/N rate as the

operational target for monetary policy ± 100 bps.

5Do Regional Trade Areas Improve Export Competitiveness? - A Case of Indonesia

Amalia Adininggar Widyasanti 1

Indonesia has involved in quite many regional trade agreements, since more than a decade ago.

Theoritically, free trade agreements (FTAs) are very beneficial to the countries, as resources are more

efficiently allocated due to production specialization. However, presence of asymmetric information, market

inefficiency, and economic distortion in the real world have led to a deviation of FTAs benefits from its

theoritical framework. This paper studies whether Indonesian export competitiveness is improving after

Indonesia involves in ASEAN Free Trade Agreement (AFTA) and ASEAN-China Free Trade Agreement

(ACFTA). Export competitiveness are measured by some trade indicators, such as: trade intensity index,

market share, export product dynamics, and RCA, for some Indonesian main export products. The indices

are compared across ASEAN countries and China to reveal: (i) which products are gaining or losing

competitiveness in ASEAN and China markets; and (ii) which countries are becoming Indonesian main

competitors in ASEAN and China markets. Additionally, this paper ends up with some policy

recommendations that Indonesia should undertake to improve competitiveness of its products in ASEAN

and China markets.

JEL ClassificationJEL ClassificationJEL ClassificationJEL ClassificationJEL Classification: R11, F16

Keywords: FTA, export competitiveness, Indonesia.

1 Amalia Adininggar Widyasanti is currently a Deputy Director for Trade at The Ministry of National Development Planning. Sheobtained her PhD degree in Economics from The University of Melbourne, Australia. Views and opinions expressed in this paper areher own and not representing statements of The Ministry

Abstract

DO REGIONAL TRADE AREAS IMPROVE EXPORT COMPETITIVENESS? -A CASE OF INDONESIA


I. INTRODUCTION

According to the theory of international trade, Free Trade Areas (FTAs) are accepted as

their mutual benefits from such trade are very beneficial to the countries due to the concept of

comparative advantage. A country will specialize in producing the products that it has a

comparative advantage. By this specialization, the world can expand total world output with

the same quantity of resources, as the economic efficiency is increased. Therefore, theoritically,

an FTA can assure that all countries involved in the agreement will gain from the trade creation

and trade diversion.

The recent trends of free trade agreements (FTAs) shows that countries in the world have

involved in many trade agreements, either bilateral or regional trade agreements. Data in Figure

1 suggests that the increase of world FTAs has been significant since 2002.

The data also shows that up until now the number of FTAs in the world is 221 agreements,

rising as much as 152 agreements from the year 2002, which was only 69 agreements. Numbers

of regional and bilateral agreements are increasing as those agreements are believed to be the

second-best option of FTA after the multilateral agreement. Because the implementation of

multilateral agreement is not optimally implemented, countries prefer to undertake quite many

regional and bilateral agreements in order to expand their trade and to strengthen their economic

relations with other countries.

Figure II.2 shows the classifications of FTA into bilateral and plurilateral agreements. Bilateral

agreement refers to a preferential trading arrangement where it involves only two parties.

Figure II.1:Development of FTAs in the World (1991-2010)

Numbers of FTAs

250

200

150

100

50

0

221210

195

134

94

8

69

Year«93«92«91 «04«95«94 «03«02«01«00«99«98«97«96 «05 «06 «08 «09«07 «10

Source: Asia Regional Integration Center Database, ADB (modified)


Correspondingly, plurilateral agreement is a preferential trading arrangement that involves

more than two parties. Based on the figure, it can be seen that bilateral arrangement dominates

plurilateral arrangement, where it accounts for 77 % out of total 221 agreements in 2009.

Therefore, only 23% of the agreements are plurilateral.

Indonesia has also involved in quite many trade arrangements. Up until now, it has

implemented 7 agreements that are already in effect, and 8 arrangements that are still in the

process of negotiation or study. Table 1 shows the FTAs that Indonesia has involved.

This paper will focus on analyzing the competitiveness of Indonesian export products

after the implementation of ASEAN Free Trade Area (AFTA) and ASEAN-China Free Trade Area

(ACFTA). The reason why these FTAs are selected is because: (i) ASEAN and China are Indonesia»s

main export markets; and ASEAN countries are also Indonesian main competitors in these

markets.

Source : Asia Regional Integration Database, ADB (modified)

51(23%)

170(77%)

BILATERAL

PLURILATERAL

Figure II.2:Classification of Trade Arrangements


II. INDONESIA AMONG AFTA AND ACFTA

The ASEAN Heads of State and Governments decided to establish an ASEAN Free Trade

Area or AFTA in January 1992. The objective of AFTA is to eliminate tariff barriers among the

Southeast Asian countries with a view to integrating the ASEAN economies into a single

production base and creating a regional market; which will be done through elimination of

intra-regional tariffs and non-tariff barriers. The ASEAN Free Trade Area or the AFTA is perceived

to be the soul of ASEAN economic integration. The implementation of AFTA is started in January

1993. The schedule of tariff reduction for AFTA are scheduled under CEPT (Common Effective

Preferential Tariff)-Scheme, and the schedule of tariff reduction for ASEAN-6 more advanced

than CMLV countries (Cambodia, Myanmar, Lao, and Vietnam). Under the CEPT-scheme,

products are categorized into 5 (five) groups, i.e. Inclusion List (IL), Sensitive List (SL), Highly

Sensitive List (HSL), Temporary Exclusion List (TEL), and General Exception List (GEL).

For Indonesia, total numbers of tariff lines that are put under CEPT Scheme is 11.153

lines; where 98.9% or 11.028 tariff lines are included in Inclusion List. The rest of it is belong

to General Exclusion List and Sensitive List. Structure of Indonesian tariff under the CEPT scheme

can be seen in the Figure below.

Table II.1Lists of FTAs that Indonesia has involved

No Name of Arrangement Status

Source: Asia Regional Integration Database, ADB (modified)

ASEAN Free Trade Area

ASEAN-Australia and New Zealand Free Trade Agreement

ASEAN-India Regional Trade and Investment Area

ASEAN-Japan Comprehensive Economic Partnership

ASEAN-Korea Comprehensive Economic Cooperation Agreement

Japan-Indonesia Economic Partnership Agreement

ASEAN - China Comprehensive Economic Cooperation Agreement

ASEAN-EU Free Trade Agreement

Comprehensive Economic Partnership for East Asia (CEPEA/ASEAN+6)

East Asia Free Trade Area (ASEAN+3)

India-Indonesia Comprehensive Economic Cooperation Arrangement

Indonesia-Australia Free Trade Agreement

Indonesia-European Free Trade Agreement

Pakistan-Indonesia Free Trade Agreement

United States-Indonesia Free Trade Agreement

In effect

In effect

In effect

In effect

In effect

In effect

In effect

Under negotiation

Proposed/Under consultation and study





Under Negotiation


1

2

3

4

5

6

7

8

9

10

11

12

13

14

15


ASEAN Member Countries have made significant progress in lowering the intra-regional

tariffs through the Common Effective Preferential Tariff (CEPT) Scheme for AFTA. More than 99

percent of the products in the CEPT Inclusion List (IL) of ASEAN-6, comprising Brunei Darussalam,

Indonesia, Malaysia, the Philippines, Singapore and Thailand, have been brought down to the

0-5 percent tariff range. Figure II.4 shows us that imports of ASEAN-6 countries from that

regional is increasing along with the decrease of import tariff in ASEAN-6.

In November 2004, at the 10th ASEAN Summit in Vientiane, Lao PDR, the Economic

Ministers of ASEAN and China signed the Agreement on Trade in Goods (TIG) of the Framework

Agreement on Comprehensive Economic Cooperation between ASEAN and China. This is well-

Figure II.3: Structure of Indonesian Tariff Lines under CEPT Scheme

Figure II.4: Development ofImports and Tariff in ASEAN-6

Source: Ministry of Finance

11.0280

25

100

IL TEL GEL SL/HSL

Imports ($ Million) Tariff, %(Weghted Average)

Source: UNCTAD-Train Database (modified)

Imports (Million USD)

Tarif (Weghted Average)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

0

2

4

6

8

10

12

14

16

18

«93 «94 «95 «96 «97 «98 «99 «00 «01 «02 «03 «04 «05 «06 «07


known as the ASEAN-China Free Trade Agreement (ACFTA), where its implementation has

come into force since July 1, 2005. In this agreement, the tariff lines under the modality of tariff

reduction was classified into 3 groups, i.e early harvest program, normal track, and sensitive

track. Tariff lines placed in the Normal Track the Normal Track the Normal Track the Normal Track the Normal Track have been gradually reduced and eliminated

according to the following Schedules (ASEAN-6 and China):

Table II.2Modality of Normal-Track Tariff Reduction for ASEAN-6

* The first date of implementation shall be 1 Jully 2005

X > 20% 20 12 5 0

15% < x < 20% 15 8 5 0

10% < x < 15% 10 8 5 0

5% < x < 10% 5 5 0 0

x < 5% Standsill 0 0

X = Applied MFNTariff Rate

ACFTA Preferential Tariff Rate(Not Later than 1 January)

2005* 2007* 2009 2010

However, the tariff reduction under Sensitive Tracks is starting to be implemented in

2012 for the Sensitive Lists, and it shall be gradually reduced to 0-5% not later than 1 January

2018. Furthermore, the tariff of products under high sensitive list should not exceed 50%

started in 2015.

Figure II.5:Import Tariff and Trade Balance of ASEAN-6 and China (2000-2007)

Source: UNComtrade and UNCTAD-Trade Database

Tariff (Weighted Average), %

Tariff Applied to ASEAN-6 in China Market

Tariff Applied to China in ASEAN-6 Market

0

2

4

6

8

10

12

14

16

2000 2001 2002 2003 2004 2005 2006 2007

2008

2007

2006

2005

2004

2003

2002

2001

2000

-15000 -10000 -5000 0 5000

Trade Balance of Indonesia With China

Trade Balance of ASEAN-6 With China

Trade Balance ($ Million)


The Figure above shows us that the weighted-average tariff has been decreasing both in

ASEAN-6 and China market. Whereas, deficit in trade balance of ASEAN-6 with China tends to

increase, meaning that ASEAN-6 imports is increasing more rapidly than its exports to China.

On the other hand, the total trade balance of Indonesia with China tends to be surplus. But,

this is not the case for non oil-and-gas trade balance of Indonesia with China, where its trade

balance has started to be deficit since 2005. Therefore, Indonesian trade surplus with China

was due to a big surplus in Indonesian trade of oil and gas to China.

III. COMPETITIVENESS INDICATORS

Some literatures (Ng, 2002; Mikic, 2005; ITC Market Analysis Section, 2000; World Bank

Institute, 2010) have provided indicators and indices that are commonly used for international

trade analysis. However, this paper chooses some of the competitiveness indicators that are

considerably practical to analyze whether Indonesian export products are loosing or gaining

their competitiveness after AFTA and ACFTA come into effect. The indicators are export intensity

index, market share, and dynamic RCA.

Export intensity index is a measure of whether or not a country exports more to a given

destination than the world does on average. The expression is defined by the following equation:

Figure II.6: Non-Oil and Gas Trade Balanceof Indonesia with China (2004-2010)

Source: Ministry of Trade (modified)

2008

2007

2006

2005

2004

2003

2002

-8,000,000 -6,000,000 -4,000,000 -2,000,000 0 2,000,000

Trade Balance Indonesia with China

Trend of Trade Balance

ww

wj

iw

ij

ijX

x

X

xEII =

(II.1)


where xij is the dollar value of exports of country/region i to country/region j, X

im is the dollar

value of the exports of country/region i to the world, xmj

is the dollar value of the world exports

to country/region j, and Xmm

is the dollar value of market exports. An index of more than one

indicates that trade flow between countries/regions is larger than expected given their importance

in the trade.

Market share is measured by the following equation:

Where:

MSij = Market Share of country i in market j.

X ij = Exports of country i to market j.

M j

= Imports of market j.

Dynamic RCA is a modification of static RCA, and it is not yet common in use as the

static RCA has been. Dynamic RCA has been used by Edwards and Schoer (2001) to analyze

the structure and competitiveness of South African Trade.

The advantages of using the dynamic RCA are: (i) it describes RCA over some period of

time; and (ii) it provides product positioning in the export destination countries, as it offers

some criteria to cluster products according to their positions in the market. Therefore, dynamic

RCA is more useful than traditional RCA, particularly if the study is going to identify which

products are gaining or loosing the markets and to provide policy recommendation based on

market positions of the export products. In addition, dynamic RCA is more informative than

static RCA in explaining how competitive is the export products.

In this paper, the formula of dynamic RCA, which were referring to Edwards and Schoer

(2001) is calculated using the formula below and was slightly modified to fit in with the ASEAN

or China Market as follows:

%100×=j

ij

ijM

XMS

(II.2)

∑

∑

∑

∑

∆

−

∆

=∆

=

j

jm

jm

j

jm

jm

j

ji

ji

j

ji

ji

j

j

j

X

X

X

X

X

X

X

X

RCA

RCADRCA

,

,

,

,

,

,

,

,

(II.3)


Where:

DRCA j = dynamic RCA indicator

X i,j

= exports of commodity j of country i to destination market (ASEAN or China)

X m,j

= exports of commodity j of ASEAN countries to destination market (ASEAN or China)

The first term of the right hand side refers to the export share of commodity j in the

reporting country»s total export to the destination market. The second term refers to the export

share of ASEAN countries of commodity j to the total ASEAN exports directed to the destination

market.

Edwards and Schoer (2001) provided a positioning matrix that is very useful to analyze

the competitiveness of the products under evaluation. The matrix is given in Table II.3.

Table II.3Positioning Matrix of Export Competitiveness

Adapted from Edwards and Shoer (2001)

Share of j ina country»s export

Share of j ina market»s export Position

Increasing RCAIncreasing RCAIncreasing RCAIncreasing RCAIncreasing RCA

Decreasing RCADecreasing RCADecreasing RCADecreasing RCADecreasing RCA

>

<

<

<

>

>

Rising stars

Falling stars

Lagging retreat

Lost opportunity

Leading retreat

Lagging opportunity

IV. DATA AND METHODOLOGY

The Data used in this paper is mainly obtained from UNCOMTRADE Database, which

was retrieved using World Integrated Trade Solution (WITS) application. The export data is

taken from 1996-2008 for ASEAN countries and China. Data for ASEAN countries consists of

Indonesia, Malaysia, Singapore, Thailand, Philipines, and Brunei, because the rest of ASEAN

countries do not have a complete data set available in WITS. The product analysis refers to

classification of 2-digit HS 1996.

Calculation of Export Intensity Index is specifically obtained from Asia Regional Integration

Center Database in Integration Indicator Database, which is available to be downloaded from

http://aric.adb.org/indicator.php.


A further classification of HS-1996 is also used in this paper for simplifying the analysis.

The classification in Table II.4 is referred HS-1996 Classification by sections with some

modifications in it to shorten the category.

V. RESULTS AND ANALYSIS

V.1. AFTA

Before the implementation of AFTA, which is in 1992, the contribution of Indonesia in

ASEAN-6 exports to ASEAN-6 is about 12.7%. Then this share is decreasing in 1995, but

started to gradually increase up until now. Most of the export commodities/products of Indonesia

are either increasing or stable in market share. This means that Indonesian products are quite

competitive in ASEAN market.

However, there are some products that are loosing the market. Those are chemicals,

textiles, skin and leather products, and machinery/electricals. The main competitors of these

products are Malaysia for Chemicals, Singapore for Machinery/Electricals, Thailand for textiles,

and Vietnam for textiles and Skin/leather products.

Table II.4Product Classification under HS-1996 Codes

No Product Classification HS Codes

1 Live animals and animal products 01-052 Vegetable Products 06-143 Animal or Vegetable Fats and Oil 154 Foodstuffs 16-245 Mineral Products 25-276 Chemicals 28-387 Plastics and Rubbers 39-408 ΩSkin and leather 41-439 Wood & Wood Products 44-49

10 Textiles 50-6311 Footwear 64-6712 Stone and Glass 69-7113 Metals 72-8314 Machinery / Electrical 84-8515 Transportation 86-8916 Miscellaneous 90-97

Source: UNComtrade, http://comtrade.un.org/kb/article.aspx?id=10253 (modified by author)


In Table II.6, it can be seen that export intensity index of ASEAN countries is increasing

particularly in 2000s, with a slight decrease in 1995. Indonesia»s export intensity index has been

increasing, which means that AFTA has helped Indonesia to export more to other ASEAN

countries, causing export intensity of Indonesia to continously increase. Other ASEAN countries,

such as Malaysia, Singapore, and Thailand has also been experiencing an increase of export

intensity index. Therefore, AFTA has improved trade flows among countries in this region.

Table II.5Market Share of Indonesian Export Products in ASEAN

Market Share

Total 12.7% 8.8% 10.1% 10.1% 10.8% 11.6%Animal/Veg Oils 30.5% 24.2% 48.9% 51.1% 53.7% 57.5%Foodstuffs 15.0% 13.2% 16.8% 18.4% 18.2% 20.2%Footwears 15.9% 11.5% 28.0% 28.3% 24.6% 21.5%Metals 2.8% 3.3% 24.3% 23.6% 23.4% 25.3%Transportation 7.0% 14.4% 12.4% 15.6% 14.0% 17.1%Plastics&Rubber 6.5% 8.1% 9.2% 9.6% 10.3% 10.8%Wood Products 20.1% 19.1% 23.6% 24.3% 23.1% 22.7%Vegetable 17.3% 15.0% 13.4% 12.1% 10.4% 8.1%Mineral Products 19.5% 13.4% 9.8% 10.5% 12.1% 12.1%Miscellanous 6.9% 21.0% 6.7% 9.0% 9.8% 8.4%Animal Products 18.4% 18.7% 18.3% 17.5% 19.0% 22.3%Stone/Glass 20.1% 20.1% 16.4% 22.6% 20.9% 19.1%Chemicals 13.2% 9.7% 9.5% 9.3% 13.8% 9.9%Machinery/Electricals 14.2% 8.8% 6.8% 5.7% 5.5% 6.0%Skin/Leather products 20.2% 9.2% 10.7% 16.0% 13.3% 8.7%Textiles 55.7% 21.3% 25.2% 22.7% 22.1% 19.9%

Source: UNCOMTRADE Database (computed by author)

Product1992 1995 2005 2006 2007 2008

IncreasingMarketShare

Stable MarketShare

DecreasingMarketShare

Table II.6Export Intensity Index of ASEAN Countries in ASEAN Market

ASEAN 4.05 3.67 4.66 4.63 4.67 4.56

Indonesia 2.71 2.14 3.40 3.40 3.62 3.54

Malaysia 6.02 4.14 4.80 4.84 4.76 4.61

Singapore 4.53 4.55 5.77 5.72 5.87 5.71

Thailand 2.73 2.97 4.05 3.87 3.95 4.04

Viet Nam 3.98 2.97 3.26 3.09 3.09 2.91

Reporter 1992 1995 2005 2006 2007 2008


Another interesting result in this paper is presented in Figure II.7 concerning positioning

of Indonesian Export Competitiveness in ASEAN Market by product groups. It seem to be

satisfying that there is only 1 product group (out of 16 product groups) which has been loosing

its competitiveness in ASEAN market, as it has lost its opportunity. The product is vegetable.

There are quite many products include in the rising star groups, which are very promising to the

future of Indonesia»s trade with ASEAN. However, Indonesia has to put its attention to some

products which have lagging-opportunities. Those are metals and mineral products. According

to the results, growth of Indonesian market share for these products in ASEAN is still lower

than growth of ASEAN demand for these products. Meaning that Indonesia still have more

opportunities to increase its share of these products in ASEAN Market. In general, Indonesia

has entered to the right market in ASEAN.

Figure II.7.Positioning of Indonesian Export Competitiveness in ASEAN Market (2003-2008)

Growth of Share of jin a country»s export

Note := Increasing RCA

= Decreasing RCA

Growth of Share of jin a market»s export

Rising Star

Falling StarLagging Retreat

Lost Opportunity

LeadingRetreat

Lagging Opportunity

14

1

109

6 11

2 13

5

34

715

8

12

16

1 = Live animals and animal products, 2 = Vegetable Products, 3 = Animal or Vegetable Fats and Oils, 4 = Foodstuffs,5 = Mineral products, 6 = Chemicals, 7 = Plastics and Rubbers, 8 = Skin and Leather, 9 = Wood and Wood Products,10 = Textiles, 11 = Footwear, 12 = Stone and Glass, 13 = Metals, 14 = Machinery/Electricals, 15 = Transportation,16 = Miscellaneous


V.2. ASEAN-China FTA

After the implementation of ASEAN-China FTA, the structure of Indonesian exports to

China has slightly changed. Before ACFTA, wood and articles of wood (HS-44) was among the

top 10 Indonesian export commodities to China, where its share in the total exports to China is

7.2%. However, after ACFTA, this commodity was replaced by Ores, slag, and ash (HS-26). In

addition, the share of mineral fuels, oils and product (HS-27) and animal/vegetable fats and oil

(HS-15) are increasing from 26.1% and 12.8% in 2004 to 39.2% and 18.2% in 2008. The

main reason for this is because in recent years China imported more industrial raw materials

due to the increase of its industrial activities and production. This reason is also supported by

the fact that China has increased its imports of Ores, slag, and ash and articles of iron or steel.

As Indonesia is one of the main world suppliers for mining products due to its natural resources,

therefore exports of Indonesia to China for this product is also increasing.

Table II.7Top 10 Commodities of Indonesian Exports to China (2004 and 2008)

27 Mineral fuels, oils & product of th 26.1% 27 Mineral fuels, oils & product of th 39.2%

15 Animal/veg fats & oils & their clea 12.8% 15 Animal/veg fats & oils & their clea 18.2%

29 Organic chemicals. 12.3% 40 Rubber and articles thereof. 7.7%

44 Wood and articles of wood; wood ch 7.2% 46 Manufactures of straw 6.4%

46 Manufactures of straw 5.7% 26 Ores, slag and ash. 5.6%

40 Rubber and articles thereof. 5.5% 29 Organic chemicals. 2.9%

47 Pulp of wood/of other fibrous cellu 4.3% 73 Articles of iron or steel. 2.7%

84 Nuclear reactors, boilers, mchy & m 4.2% 84 Nuclear reactors, boilers, mchy & m 2.4%

83 Miscellaneous articles of base metal 2.7% 83 Miscellaneous articles of base metal 2.2%

73 Articles of iron or steel. 2.6% 47 Pulp of wood/of other fibrous cellu 1.7%

Share to Total Exports to China Share to Total Exports to China Share to Total Exports to China Share to Total Exports to China Share to Total Exports to China 83.4% Share to Total Exports to ChinaShare to Total Exports to ChinaShare to Total Exports to ChinaShare to Total Exports to ChinaShare to Total Exports to China 89.01%

2004 Share 2008 Share

Source: UNCOMTRADE (computed by Author)

Table II.8 shows Indonesian market share in China market by product category. The

denominator of the share is total exports of ASEAN-6 + Vietnam to China Market. From the

data it can be seen that share of Indonesian export in China market tends to be stable with a

little increase in 2008. Some Indonesian products are gaining market in China after the

implementation of ACFTA in 2005. Those products are Animal/Vegetable Oils and fats, Foodstuffs,

Footwears, Metals, Mineral Products, Plastics and Rubber, as well as Skin/Leather products. It

can be seen that products that generallly gain increasing market share in China are natural-

resource based; classifying as agricultural and mining products; except footwear. Manufacturing


products, such as: woods, textiles, chemicals, and machinery/electrical products, are experiencing

a decrease in market share. This is because these products cannot compete with local China

products or with other ASEAN countries» products.

Table II.8Market Share of Indonesian Exports in China (2005-2008)

Market Share

Total 22.2% 20.4% 20.8% 19.8% 19.6% 22.4%Animal/Veg Oils 24.7% 29.6% 36.4% 39.6% 34.1% 34.7%Foodstuffs 5.4% 5.2% 7.7% 5.5% 7.1% 6.9%Footwears 20.5% 21.7% 24.3% 21.0% 29.4% 31.4%Metals 16.9% 14.3% 21.4% 22.4% 14.7% 16.4%Mineral Products 22.9% 21.1% 38.5% 39.7% 40.9% 38.1%Plastics&Rubber 6.3% 8.1% 8.1% 9.9% 10.0% 10.1%Skin/Leather products 4.1% 6.5% 14.3% 20.7% 17.3% 17.9%Miscellanous 1.5% 2.2% 2.3% 2.8% 3.3% 3.0%Vegetable 4.4% 4.9% 4.5% 3.5% 4.5% 6.7%Transportation 4.3% 6.6% 8.2% 8.8% 7.2% 2.5%Animal Products 25.5% 26.8% 21.5% 17.5% 9.0% 18.1%Chemicals 18.2% 19.3% 19.1% 16.5% 16.1% 14.3%Machinery/Electricals 1.6% 1.7% 1.2% 1.2% 1.3% 1.5%Stone/Glass 15.2% 20.7% 14.8% 10.7% 8.3% 4.9%Wood Products 59.1% 53.2% 49.6% 50.4% 46.4% 53.0%Textiles 31.7% 25.2% 20.9% 22.6% 22.7% 22.6%

Product2003 2004 2005 2006 2007 2008

IncreasingMarketShare

Stable Market Share

DecreasingMarketShare

The Indonesian main competitor of Chemicals, Machinery/Electricals, Wood products,

and Textiles in China market is Thailand; as its shares is increasing after ACFTA was implemented.

In addition, Vietnam is also a good supplier of wood products and textiles to China market, its

share is increasing under ACFTA framework. But, products of machinery/electricals and chemicals

of Vietnam do not seem to be competitive in China market.

Export Intensity Index of ASEAN countries in China Market tends to increase (Table II.9),

as China»s export intensity index in ASEAN has also increased. Export Intensity Index for all

countries in any year is always greater than 1, showing that trade flows between ASEAN countries

to China, or the other way around, is larger than expected given their importance in such

regional trade. This means that the implementation of ACFTA does increase trade intensity

among the participating countries and in general improve trade flows among the countries in

the region.


The results of dynamic RCA calculation are summarized in the chart below (Figure II.8) to

portray the competitiveness positioning of Indonesian export products in China market. The

products were classified into the category mentioned in Table II.4.

From the result, it can be seen that there are only 3 product groups which are in ≈rising

star∆. Those products are mineral products, plastics and rubbers, and footwear. The products

which are in ≈lagging opportunity∆ are Animals/vegetable oils and fats and foodstuffs. Lagging

opportunity means that demands on these products in China are quite high, but the rate of

export growth of these products are still lower than that of the demands. Most of Indonesian

export products in China market is categorized as leading retreat and lagging retreat. On the

other hand, Indonesia in the future should not focus on exporting skins and leather as demands

in China Market for this product is decreasing.

Table II.9Export Intensity Index of ASEAN countries and China

ASEAN China 1.31 1.35 1.42 1.46 1.49 1.45

Indonesia China 1.24 1.20 1.38 1.39 1.38 1.37

Malaysia China 1.30 1.24 1.17 1.22 1.42 1.54

Singapore China 1.26 1.44 1.52 1.64 1.57 1.48

Thailand China 1.42 1.37 1.46 1.52 1.58 1.48

Viet Nam China 1.87 2.04 1.76 1.37 1.22 1.16

China ASEAN 1.31 1.34 1.34 1.37 1.43 1.43

Reporter Partner 2003 2004 2005 2006 2007 2008

Reporter Partner 2003 2004 2005 2006 2007 2008


VI. CONCLUSIONS

This paper provides some analysis about competitiveness of Indonesian export products

in ASEAN and China, after the implementation of ASEAN FTA and ASEAN-China FTA. The

competitiveness indicators used in this paper is market share, export intensity index, and dynamic

RCA. The results show that Indonesia is doing well in ASEAN Market by gaining some market

for some products. However, some policy strategy is needed for the products, particularly for

vegetable products which has lost the opportunity in ASEAN markets. Some policies needed

would be product diversification, improvement in quality control, and health-related concerns.

In China market, Indonesia gained the market only for the products of plastics and rubber,

mineral products, and footwear. The products which are in ≈lagging opportunity∆ are Animals/

vegetable oils and fats and foodstuffs; meaning that Indonesia can still do some improvement

to optimize the opportunity, as the rate of export growth of these products are currently still

lower than that of the demands. Most of Indonesian export products in China market is

categorized as leading retreat and lagging retreat. In the case of ACFTA, Indonesia can still do

more to improve its export performance in China market.

Figure II.8.Positioning of Indonesian Product Competitiveness in China Market Using Dynamic RCA

1 = Live animals and animal products, 2 = Vegetable Products, 3 = Animal or Vegetable Fats and Oils, 4 = Foodstuffs,5 = Mineral products, 6 = Chemicals, 7 = Plastics and Rubbers, 8 = Skin and Leather, 9 = Wood and Wood Products,10 = Textiles, 11 = Footwear, 12 = Stone and Glass, 13 = Metals, 14 = Machinery / Electricals, 15 = Transportation,16 = Miscellaneous

Growth of Share of jin a country»s export

Note := Increasing RCA

= Decreasing RCA

Growth of Share of jin a market»s export

Rising Star

Falling StarLagging Retreat

Lost Opportunity

LeadingRetreat

Lagging Opportunity

1016

13

1

2

8614

9

12

154

3

7

5 11


Edwards and Schoer (2001). The Structure and Competitiveness of South African Trade, Trade

and Industrial Policy Strategy √ Annual Forum, Muldersdrift.

Ng (2002). Trade Indicators and Indices, in Development, Trade, and WTO: A Handbook, edited

by Hoekman, Mattoo, and English, The World Bank, Washington DC.

Mikic (2005). Commonly Used Trade Indicators: A Note, presented at ARTNeT Capacity Building

Workshop on Trade Research, UNESCAP.

ITC Market Analysis Section (2000). The Trade Performance Index √ Background Paper, UNCTAD/

WTO.

Utkulu and Seymen (2004). Revealed Comparative Advantage and Competitiveness: Evidence

for Turkey vis-à-vis the EU/1, paper presented at the European Trade Study Group 6th Annual

Conference, Nottingham.

World Bank Institute (2010). World Trade Indicators 2009/2010 √ User Guide to Trade Data,

The World Bank.

REFFERENCES


This page is intentionally left blank

23The Impact of ACFTA Implementation on International Trade of Indonesia

THE IMPACT OF ACFTA IMPLEMENTATIONON INTERNATIONAL TRADE OF INDONESIA

IbrahimMeily Ika Permata

Wahyu Ari Wibowo1

This study analyze the impact of the implementation of trade agreements within the framework of

ACFTA on Indonesia»s export by using the GTAP model; a Multi Regional Computable General Equilibrium

Model. Results shows that ACFTA provide opportunities for increased export from Indonesia; Indonesia

obtained a net trade creation of international trade amounted to 2% and total exports growth increased

by 1.8. However, the export performance of Indonesia in the period showed a decrease of competitiveness,

as shown by the decline in share of Indonesian export commodities which are highly competitive and high

intra-industry linkage. This paper also find that because the commodity structure of China and the non

compeeting behavior of ASEAN countries including Indonesia (tends to complement), China is relatively

easier to penetrate export to the Asean market. The entering products from China should provide

opportunities for domestic producers to increase production capacity in ASEAN, due to wider choice of

relatively cheap capital goods imports.

JEL ClassificationJEL ClassificationJEL ClassificationJEL ClassificationJEL Classification: C67, F14, R12

Keywords: ACFTA, trade, export, GTAP, Revealed Comparative Advantage, CGE.

1 The authors are researchers at the BRE-DKM Bank Indonesia and are responsible for the results this research and all opinions. Thank-you note is addressed to the Chairman DKM, Perry Warjiyo and Iskandar Simorangkir, and all other researchers who have supportedthis research

Abstract


1. INTRODUCTION

The development of international trade leads to more liberal forms of trade that are

accompanied by various forms of bilateral, regional and multilateral cooperation. One of the

main objectives of international trade agreements is to reduce or eliminate trade barriers.

Liberalization of world trade, with the pattern of international cooperation, provide a positive

implication on the growth of world economy. The value of world trade grew more than twice

the growth in gross domestic product (GDP) of real world (Krueger, 1999).

In the mid-1980s, preferential trading arrangements (PTA), developed as a complement

to international cooperation. In contrast to international cooperation, PTA involves two or more

countries. Based on the theory of PTA, as described by Kemp (1964) and Vanek (1965), the

impact of two or more countries that make up a custom unions (common external tariff) is the

growing prosperity of the countries that joined the union and it does not cause any decline on

the welfare of countries outside the union. This is proved in a study by Ohyama (1972) and

Kemp and Wan (1976). Rather than setting a common external tariff, a more developed pattern

of PTA is the elimination of intra-trade barriers or more familiar known as the free trade agreement

(FTA). Some FTAs that have been running are the North American Free Trade Area (NAFTA), the

European Economic Area (EEA), the African Free Trade Zone (AFTZ) and the South Asia Free

Trade Agreement (SAFTA).

Likewise with Indonesia which has agreed on trade cooperation both bilaterally, regionally

and internationally. Although Indonesia»s involvement in these various trade cooperation causes

a challenge to domestic products, the goal of these agreements are provide positive impact to

the economy of the countries involved and to economy of Indonesia in particular.

Related to the regional area, Indonesia joined the ASEAN Free Trade Area (AFTA) which

was signed on January 28, 1992. In its development, the cooperation extended to involve

other countries including China, known as the ACFTA. In particular, the involvement of Indonesia

in ACFTA need for further observed. This is related to many factors such as the readiness of

domestic products to encounter the rush of imported goods from China and the ASEAN market

that is potentially reduced for domestic products. Many literatures and existing studies have

widely reviewed the impact of ACFTA by various dimensions and analysis tools. This research is

expected to become one of the complementary study on the impact of ACFTA with new added

value. Thus, information associated with the study of ACFTA market trading will be more

complete.

The objective of this paper are (i) To contribute to the study of external sector, particularly

the international trade of Indonesia, (ii) To provide an understanding of Indonesia»s trade structure,


especially within the scope of the ASEAN-China region, (iii) To measure the impact of the

implementation of ACFTA towards, on generally, the member countries in terms of agreements

on international trade, and for Indonesia in particular, and (iv) To map the opportunities and

challenges presented by the characteristics of Indonesian exports. Many opportunities are

associated with the opening of Chinese markets for export commodities of Indonesia. Yet

challenges also emerge with China competing in the ASEAN market.

The impact of ACFTA trade on the Indonesian economy covers many aspects that can be

further development of analysis such as GDP, employment, investment, inflation and international

trade. To provide added value on the existing ACFTA topics, this study will focus on ACFTA

impact on Indonesia»s exports. Analysis of various indicators of performance and characteristics

of Indonesian exports are specifically addressed to ACFTA market coverage.

In terms of analysis tool, we will only review the results of GTAP model that are related to

the trading impact of Indonesia»s export, especially with trading partner countries of ACFTA

region. Based on the results of the GTAP model, further analysis will be carried out either by

using analytical tools for international trade indicators such as the RCA, IIT, IES, IEO.

The second part of this paper will describe the empirical ground and literature review on

trade and economic balance, the third part covers the methodology, the fourth section discusses

the results and interim analysis, while conclusions and implications will wrap the paper.

II. EMPIRICAL GROUND AND LITERATURE REVIEW

II.1. The Basic Model of International Trade

The economy of a country is an aggregation of the behavior of each individual. The

balance of goods in one country can be explained based on the interaction of profit maximization

behavior of producers and utility maximization of consumers. In a closed economy (autarky), in

equilibrium (point A), the composition of goods and prices of goods is the result of the interaction

mechanism of aggregate demand and aggregate supply in the country (Figure III.1).

Aggregate supply is strongly influenced by the available factors of production (endowment)

and the level of productivity, represented by the production and technology function. In the

other hand the aggregate demand curve is strongly influenced by the level of consumer utility

(U) and available consumption baskets. The level of production, consumption and the level of

consumer utility depend on the endowment and type of products available in the economy.

Manufacturers only have the option to produce a collection of specific types of product and try

to maximize profits based on the available endowment and production function. On the other


hand, consumers can only maximize utility by consuming a combination of product types

manufactured domestically and indirectly, the level of utility will be very limited.

Endowment differences between countries, as well as different levels of production and

technology and the types of products cause large variations in the type of product produced

between countries. And differences in tastes and individual utility level among countries will

imply on a high demand of variation of consumption basket desired by consumers within the

region. In a broader scope and in line with the era of globalization, the economy is no longer

limited to the scope of a country but it has evolved and crossed the border. The corporates»

profit maximization and consumers» utility maximization are no longer limited in national scope

but as well as inter-nations scope.

In the open economy equilibrium model, there are opportunities to maximize the profits

by expanding into foreign markets and by producing goods exceeding the domestic demand.

On the other hand, consumers also have the opportunity to maximize utility by consuming a

certain product types exceeding the domestic supply or by consuming a more diverse product

types, not just limited to the types of products within the country. Both of the above mentioned

will eventually drive the exchange of goods between countries.

The results from the interaction of individuals in a certain country with individuals in

other country will lead to the exchange of goods, services, and factors, which is commonly

known as international trade, that caused a shift in the balance of the beginning (point A)

toward the balance on the basis of international trade (point C) (Figure III.2). Excess demand for

product x (xc-xp) can be met by imports from other countries so that consumers can choose a

basket of consumption that generate a higher level of utility, which is point C. The production

Figure III.1: The Equilibrium Modelof Closed Economy (Autarky)

Source : Markusen et al, International Trade and Evidence

A

Y

ap

aU

X

Y

X


of product y that exceeds domestic demand is a surplus and will be exported in international

market. In other words, international trade is the exchange of goods, services and factors that

occur between countries or one that has passed the national/international boundaries.

Theoretically at least there are 5 advantages from trade. The first advantage is the benefits

from the exchange. By trading, a country can produce a product exceeds its domestic demand

and export the surplus (excess supply) on international markets that will eventually expand the

market and increase profitability. On the other hand, excess demand for a product can be met

by imports from other countries so that consumers can choose a basket of consumption that

generates a higher level of utility.

The second advantage is the benefits of the specialization. By trading, a country can be

more focused on one type of product which they can produce with a relatively high level of

efficiency. While the need for a product that can not be efficiently produced domestically can

be met by importing these products from other countries.

The third advantage, that can be gained from trade, is associated with the diversity of

individual preferences. The existence of trade provides more choices of products to consumers

which will assist in the fulfillment and even can raise the level of consumer utility.

The fourth advantage is associated with diversity of endowment owned by a country. By

trading, a country that prior trading did not have any or very limited access to any type of

product, will have the demand fulfilled. The fifth advantage that might be achieved is the

transfer of modern technology. With international trade, a country will have the opportunity to

learn a more efficient and modern production techniques.

Figure III.2:The Equilibrium Model of Open Economy

Source : Markusen et al, International Trade and Evidence

Q

X

Y

C

Y

*p

X

pY

cY

pX cX


The literatures state that a country will tend to export a product that is abundant

domestically or in other words will tend to export a product that excess supply. On the other

hand, the Ricardian model predicts that a country will focus the production on the type of

product that has the highest comparative advantage.

Heckscher-Ohlin theorem states that a country will tend to export commodities that intensively

use the abundant factor of production. For example, a country with abundant labor but with a

limited level of capital will tend to export products that are labor intensive and will tend to import

products that are capital intensive. Differences in the production function of a country will also

contribute to determine the direction of the country»s trade. A country that can produce relatively

more efficiently in a type of product will tend to become exporter of this products.

In fact, free trade does not literally take place freely. Barriers to trading can take the form

of tariff and non-tariff. Tariff setting has influence over the balance of output and prices. Such

constraints may lead to higher prices resulting in reduced demand for goods from abroad,

according to the demand-supply mechanism.

As an illustration, the increase in import tariffs may cause the price of imported goods

become relatively more expensive and reduce demand for the goods. This provides an incentive

for domestic production of goods. On the other hand, export subsidies cause the price of

domestically produced goods to become relatively cheaper and will increase the demand from

overseas markets.

II.2. The Theories of International Trade Coorporation

With the liberalization of trade in both international and regional scope, trade barriers

can be reduced and even be eliminated. Regional economic integration is a process where

several economies in the region agreed to remove barriers and ease the traffic flow of goods,

services, capital and labor. Reduction or even elimination of tariff and non tariff barriers will

accelerate regional economic integration as the traffic of goods, services, capital and labor

getting smoother.

Regional free trade or arrangement is expected to generate efficiencies and improve

welfare. It cannot be denied that trade arrangement would also increase competition among

the members. But if it is addressed wisely, there are benefits that can be gained among others,

which are the increasing specialization and the increased trade. With the comparative advantage

of each country, each country can focus on the production of goods that have a comparative

advantage that will trigger reallocation of production factors. In the end it will create a balance

of lower prices and greater output that will provide greater prosperity to the countries involved.


Many studies conclude that free trade has a positive impact for the countries involved. In

addition to improving welfare (Kindleberger and Lindert, 1978), free trade will also increase the

quantity and efficiency of world trade (Hadi, 2003; Stephenson, 1994). Urata and Kiyota (2003)

found that the FTA in East Asia provide a positive influence on the economy. Exports with high

competitiveness will increase. The study of Saktyanu et al. (2007) showed that a decrease of

export subsidies in developed countries have an impact on increasing agricultural production

of Indonesia. In contrast to the results of most studies that generally state the positive impacts,

Haryadi et al. (2008) show that trade liberalization, by removing all trade barriers, causes a

reducing impact at the GDP of Indonesia and Australia-New Zealand.

One indicator to measure the impact of international trade arrangement is to look at the

occurrence of trade diversion and trade creation (Vinerian, 1950; Krueger, 1990). The positive

effects of trade creation is the occurrence of trade due to the shift of consumption of domestic

products which are high-cost to imported products from abroad which are low-cost (Vinerian,

1950); in other words the trade is increased among intra-country trade partners. However,

along with the difference in tariffs applied to partners and non-partners, it will change the

direction of trading trend, and impose negative effects of trade diversion, which refers to the

replacement of imported products that are low-cost from non-member countries with imported

products that are high-cost from partner countries (Vinerian, 1950). In other words there is a

decline of trade with the non-partner countries. Trade diversion would reduce the welfare

effects due to changes in supply orientation to a source that is relatively more expensive.

Benefits of free trade or regional cooperation are very much determined by one of the

more dominant effect. The overall effect can be positive, negative or neutral, it depends on the

size of the magnitude of trade creation and trade diversion. Free trade or the PTA would be

very advantageous if the impact on trade creation is greater than the impact on trade diversion.

Studies conducted by Lee and Shin (2006) confirmed that RTA will increase the trade between

its members. However, there was no significant decrease in trade between the RTA of members

and non-members countries. Even in some of the RTAs, trade among member and non-members

countries have increased. Despite the trade creation and trade diversion, RTA gives an overall

positive impact on trade.

II.3. Agreements on ASEAN China Free Trade Area (ACFTA)

Trade between ASEAN countries and China continues to show improvement from year

to year. For ASEAN countries, China is a major trading partners as an export destination. The

average share of exports to China by ASEAN countries from 2001-2008 varies but generally


quite high. Vietnam as a country that puts China as a major trading partner with the highest

share of 9%, while the Indonesian share of exports to China is recorded at 7% (Figure III.3). For

China, ASEAN countries became an important trading partner especially for the supply of raw

materials. The share of China»s imports from Singapore recorded 35% of total imports from

ASEAN or the highest market share among other Asean countries (Figure III.4). The share of

imports of goods from Indonesia is amounted to 13% of total imports from ASEAN. Trade

between ASEAN and China have a tendency to continue to rise which show the relative

importance of ASEAN-China trade for both sides. Thus, the potential gains from removing

trade barriers between ASEAN-China region will be relatively large.

The awareness on how important the role of each party, will raise the consciousness to

pioneer an agreement of economic cooperation. On November 4, 2002, an agreement of

cooperation framework emerged which is often called the ≈Framework Agreement on

Comprehensive Economic Cooperation∆. Within the framework, it was agreed that the free

trade formation for goods would take place in 2004, the service sector in 2007, and investment

in 2009. In terms of readiness for ASEAN, the free trade also applies gradually. Free trade will

be commenced in 2010 between China and ASEAN-6 which includes Indonesia, Singapore,

Thailand, Malaysia, Philippines, and Brunei. While in 2015, it will apply to China with ASEAN-

4: Cambodia, Vietnam, Laos, and Myanmar. Several issues related to the development of ACFTA,

especially in Indonesia, are shown in Diagram III.1.

From literature studies, among others by Park et al (2008), that analyzed the advantages

and prospects of ACFTA and revealed that ACFTA, which consists of 11 economies with a quite

large total population and GDP; it is possible for ACFTA to become an effective regional economic

cooperation. Relatively large intra-region tariff level is also the potential to increase trade creation.

Although China and ASEAN have sought to liberalize commerce, in fact, the level of tariffs and

barriers between them was still quite high, allowing the trade creation to take place. China

imposes tariffs on average by 9.4% for goods from ASEAN. In contrast, ASEAN nations imposed

tariffs on goods from China in average of 2.3%.

But it can not be denied that along the opportunities, there are the challenges with the

enactment of ACFTA. The biggest challenge is the raising product competition. Fear of inability

to compete in domestic market against the rushing flow of imported products from China and

the fear of inability to penetrate China»s wide open-potential market, is a challenge which, if

managed wisely, can turn into a potential opportunity. Yue (2004) takes the increase in intra-

industry trade in machinery and electrical equipment as an example of the impact of increased

trade ACFTA that considered quite successful. There are numerous studies that have considered

the impact of ACFTA trade, as shown in Table III.1.


Figure III.3:Market Share of Exports to China

Figure III.4: China»s Import fromASEAN Countries

Diagram III.1:Road Map of ACFTA Agreement

9%

8%

8%

8%

7%

7%

6%

4%

4%

1%

0 2 4 6 8 10

%

Vietnam

Singapore

Philippines

Thailand

Malaysia

Indonesia

Myanmar

Brunei

Lao

Cambodia China as Aim of Main Export

Singapore

Malaysia

Thailand

Indonesia

Philippines

Vietnam

Brunei

Myanmar

Lao

Cambodia

35%

21%

18%

13%

7%

6%

1%

1%

0%

0%

0 10 20 30 40

%

Share of China»s Importfrom ASEAN Countries

Head of theASEANcountries andChina signed aframeworkagreement onComprehensiveEconomicCooperationPhnom Penh

4 NovemberEconomicMinisters ofASEAN andChina signedthe protocolchanges to theframeworkagreement inBali

6 OctoberIndonesia ratifiedthe ACFTAframeworkagreementthroughPresidentialDecreeNo.48/2004

15 June

Rising:- Ministry of FinanceDecree No.355/KMK/01/2004on StipulationCustoms Tariff onImports of goods inthe Framework ofthe Early HarvestPackage (EHP)ACFTA- Minister of FinanceDecree No.355/KMK/01/2004on Stipulation oftariffs of import dutyon import of goodswithin theframework ofBilateral EHPIndonesia-China FTA

21 July

Rising:PermenkeuNo.56/PMK/010/2006 on CustomsTariffDetermination inthe framework ofASEAN-ChinaNormal Track

7 JulyRisingPermenkeuNo.21/PMK/010/2006 onStipulation oftariffs within theframework ofnormal track in2006 ACFTA

15 MarchRisingPermenkeu No.07/KMK.04/2007 of theMinister ofFinance DecreeNo. Extension.355/2004Permenkeu No.08/PMK.04/2007 of theMinister ofFinance DecreeNo. Extension.356/KMK.01/2004

6 FebRising:PermenkeuNo.235/PMK.011/2008 aboutthedetermination of import dutyin order ACFTA

23 DecMinistry of Industryasked for a delayACFTA from 2010 to2012 due to crisis

29 Jan

10 ACFTA industryassociation asked fora delay to the Houseof Representatives.

2 Dec

In the form of jointteams unt. ACFTAwhich led Menko,involving Apindo, Kadin,and the Ministry ofTrade.

25 Dec

2002 2003 2004 2005 2006 2007 2008 2009


Table III.1Previous Studies on ACFTA

- Overall, ACFTA will increase net trade, output and regional welfare

- The impact on each country varies

- Big advantage goes to countries like Singapore, Malaysia, Indonesia andThailand than other member countries that are relatively poorer such asCambodia, Laos and Myanmar.

- Optimistic about the prospects for implementation of ACFTA

- ASEAN is a potential huge market for Chinese exports as well asalternative import sources

- China is a potential market for ASEAN exports products mainly forintermediate and capital goods

- ACFTA will provide significant economic benefits to the economy ofASEAN and China

- The pressure of competition from China will bring negative impact in theshort term but will have a positive impact by increasing productivity andefficiency in the long term

- This study compares the impact of various trade cooperation, followed byChina. The findings for ACFTA showed that China will benefit from itsparticipation in the ACFTA

- Improvement of ASEAN exports to China

- Competition with imported products from China

- There was a trade creation of ASEAN-China which tends to be higherthan the growth of intra-trade among ASEAN countries

- Singapore and Malaysia to obtain the benefits of inter-and intra-industryspecialization while Thailand gain the advantage of intra-industryspecialization. However, Indonesia and the Philippines do not gain much

- Improvement of ASEAN exports to China and vice versa

- Indonesia, Malaysia, Singapore and Thailand will experience the biggestbenefit in terms of exports

- The main export commodities of ASEAN to China are the intermediategoods so that China»s increased exports would encourage increasedexports of ASEAN

- ASEAN»s GDP rose by 0.9% while China»s GDP increased by 0.3%

- Economic benefits: improved specialization and trade. However, tradediversion will also occur with the significant non-members.

- Impact of trade: an increase in ASEAN exports to China and vice versa.The biggest increase in exports will be experienced by Indonesia,Malaysia, Singapore and Thailand. By sector, the biggest advantage willbe enjoyed by textile and clothing products, machinery and electricalequipment, and other industries. There is a significant improvement forintra-industry trade.

- Impact on GDP: ASEAN»s GDP will increase by 0.9% and by 0.3% forChina»s GDP. Vietnam will experience the largest increase. While Indonesiawill experience a decline in GDP.

- Non-economic benefits: an increase in political and social relationship.

Researcher Finding(s)Analysis Method

Park et al 2008 Trading Indicator and GTAP

Park 2007 Qualitative

Jiang & 2008 GTAPMcKibbin

Tambunan 2005 Trading indicator

Okamoto 2005 Trading indicator

Universal GTAPAcces toCompititivenessand Trade(UACT)

Yue 2004 GTAP

Year


III. METHODOLOGY

III.1. Computable General Equilibrium Model

There are several approaches in the study of world trade, which two of the classifications

are the general equilibrium and partial models. General equilibrium theory explains the linkages

of the entire market mechanism as a system that interacts simultaniously. If the market, in the

equilibrium condition, changes or if there is any partial interference on the market, then there

will be adjustments in the relevant market and other markets. One model that is often used in

various studies is the General Trade Analysis Project (GTAP), a Computable General Equlibrium

(CGE) model developed by Purdue University.

CGE model is often used for the industrial, trade and fiscal sector2 . In this model,

production factors market conditions and market of production output are in equilibrium. The

primary basis of the CGE model is an understanding on how the economy works and then the

usage of the data in accordance with the developed model.

In this GTAP model, the economy is assumed to be in general equilibrium state, where all

agents in the economy do not have the ability to influence prices or act as a price taker, so the

price, that is entirely formed, is the interaction between demand and supply. Implicitly this

assumes that every market is in the perfect competition (competitive) condition and this approach

is widely referred as the Walrasian General Equilibrium

The general equilibrium in the CGE model is reflected in nominal terms (quantity multiplied

by price) that represent the flow of funds, either accompanied by the flow of goods (transaction)

or not (transfer). The CGE model consists of equations representing the balance of the entire

market, from the input markets to output markets for the whole sectors that are analyzed. The

CGE model also explicitly models the rational behavior of economic agents like producers who

tend to maximize their profits, households who maximize the satisfaction (utility) and other

economic agents. Included in this CGE model is the specification of the specification that is

related with the flow of funds between agencies, and other equations that define the formation

of price and quantity. Overall, the CGE model is a set of mathematical equations that can be

solved simultaneously.

GTAP model is a multi-sectors and multi-regions CGE model. Standard GTAP model consists

of households, government, and companies in each economy3 (Diagram III.2). Social welfare

function is assumed to consist of private expenditure, national savings, and government spending.

2 Working Paper 2009, Semar 2009: Suatu Model Financial Computable General Equilibrium, BRE DKM.3 TSQ Discussion Paper, How Will the Regional FTAs Shape the Indonesian Economy? Evaluation by the Computable General Equilibrium

Model, Masahiko Tsutsumi, August 2001.


Savings is considered as a proxy of the delayed consumption. Under the regional income

constraint, principal agents are to maximize their welfare.

Like the other CGE models. the GTAP standard model provides the specification of various

theories and the behaviour of agents, explicitly in the form of mathematical equation. Selection

of functional form refers to two main things, (i) the suitability of the theory, and (ii) empirical

facts, and (iii) the need for research. One form of the function (henceforth we refer as nesting),

which is often used, is a Cob-Douglas function where the parameter that indicates the proportion

of the forming components assumed to be fixed. If the relative price of a commodity changes,

the user √ let say for consumption - will also experience changes to maintain the nominal

proportion in accordance with the amount of the which was previously determined (relative

share).

Diagram III.2:Agen Blocks in GTAP Model

Consumption expenditure consists of a variety of tradable commodities in the model.

The households determine their demands for each commodity based on three factors: relative

price, minimum consumption, and income level. This demand system called the Constant

Difference elasticity (CDE). On the other hand, government spending on individual commodities

is still formulized under the Cob-Douglas function.

Commodities are produced by both domestic and foreign producers. Both party then

later combined in a bundle of commodities which is a composite of domestic and import

Source: A Graphical Exposition of GTAP Model, Brockmeier, 1996

World / ROW

PrivateHousehold

Producent

Government

RegionalHousehold

Global Saving

VIPAVIGA

TAXES

SAVE

TAXES

PRIVEXP GOVEXP

XTAX TAXES

VDPA

NETINV

VOAMTAX

VDGA

VIFAVDFA

VXMD


products. In the GTAP model, the composition of both products follows the Constant Elasticity

Substitute (CES) function. This import-domestic demand system is proposed by Armington

(1969) that allows the modeler to change elasticity of substitution between domestic and

import products depend on the experiments.

The companies are assumed to maximize their revenues. In the process of production,

labor, capital, land form the composite of primary input to follow the form of the CES nesting,

thus allowing the substitution of the three primary inputs. This is consistent with the theory

and empirical facts in which a sector can switch from labor-intensive to capital-intensive or

otherwise.

The composite of primary input is then combined with the intermediate input within the

nesting that takes form as a Leontief function. These specifications are clearly required to

maintain the complementarity between primary inputs with the intermediate inputs as it is

difficult to imagine if labor can be replaced, let say, by cooking oil in the production process in

hotels and restaurants sector, for example. Land is immobile while the labor and capital are

mobile within the industry. In this standard model of GTAP, the international mobility of

endowments is not allowed.

Saving in each country is carried out (collected) by a fiction institution, that is, global

banks and is allocated as a source of finance for investment. How to connect a savings to

investment depends on the theories and the empirical facts that can be altered based on the

purpose of the research.

In general, any question proposed in a research must be translated into a simulation

model. This simulation setting is critical and one of the important components is the closure,

which is the division of variables to be placed as an endogenous or exogenous variables. This

closure greatly affect the interest and the simulation results, one of them is to restrict whether

the dimension of this simulation is short-term (one of which is marked by the fixed sectored

capital) or long term.

III.2. Analysis Flow

This research aims to measure the impact of international trade ACFTA for Indonesia and

how they impact on Indonesia»s export commodities. Related to this, described in this section is

the analysis flow performed as shown in Diagram III.3.


Diagram III.3:Analysis Flowchart

The first stage is the process of aggregation and disaggregation of the countries and

commodities categories. Next is running the CGE model, using the GTAP model which is a CGE

model to perform simulation specifically related to international trade. After applying the shock,

in this case on the tariffs, then the model starts to run. The data used is the trade data world-

wide in 2004 is the standard data included within the GTAP model version 7 in 2008.

The simulation results based on CGE models are then analyzed to see the opportunities

and challenges which are encountered in real terms of economy as already simulated. The

simulation model will also be confronted with the analysis of international trade indicators on

export and import data from UNCOMTRADE period 2001-2008.

For period 2001-2008, the analysis is divided into two periods: Period I, in 2001-2004

which can be viewed as the period before implementation of the ACFTA. The next one is Period

II in 2005-2008 which is considered as the implementation period of ACFTA. The data used is

a 3-digit SITC data (ver.3) which is aggregated into 2 digits. Meanwhile, regrouping is done to

SimulationResult

Chance

Challenge

The process of aggregation anddisaggregation of State & Sector Run GTAP (CGE Model)

Shock Rate0%

Execution

RCAIndicator

IITIndicator

OverlapIndicator

SimilarityIndicator

CompetitivenessMap of Indonesia»s

Commodities

Chance

Challenge

China Market

ASEAN Market

Indonesiavs

ASEAN Countries

Indonesiavs

China

Spearman RCIndicator


equilize the commodity code between GTAP version with the version of SITC to achieve

compability of these two tools. Conversion from SITC commodity groups to the GTAP is using

primary reference presented in the GTAP website.

III.3. Setting of GTAP Model Simulation

Generally, closures that are used in the simulation follow the standard GTAP closure,

which are:4

1. Price and quantity variable of tradable commodities across countries that are not included

in the category of endowment commodities, are treated as endogenous variable.

2. Revenue from each region is endogenous.

3. All policy variables, productivity (technical changes) and populations are treated as exogenous

In simulating the impact of ACFTA implementation on international trade of ASEAN in

general and Indonesia in particular, particularly related to exports, the shocks applied are:

1. The tariffs applicable between the ASEAN countries with China is 0% (not applicable).

2. ACFTA member countries still impose tariffs on the non ACFTA member (rest of the world,

ROW).

3. Vice versa, ROW charges against members of ACFTA

III.4. Correlation Test on International Trading Indicator

Based on the previous GTAP model simulation results, to view the competitiveness map

of Indonesia and the challenges and opportunities encountered as the result of the establishment

of a forum for international trade, ACFTA, for international trade in Indonesia, the analysis is

resumed by using several trading indicators: (i) Revealed Comparative Advantage (RCA), ( ii)

Intra-Industry Trade (IIT), (iii) Index of Export Overlap (IEO), and (iv) Index of Export similarities

(IES).

Indicators of international trade are used to provide clarification and additional information

on the findings by GTAP. The trade indicators also complement the results of research because

they can provide information on the performance of Indonesia»s export commodities in greater

detail.

On the RCA indicators, we conducted Spearman»s rank correlation coefficient (SRC) which

is a statistical measure of non-parametric and can be calculated by the following formula:

4 The setting of this simulation is available by the author.


This test is needed to see if there are similarities on competitiveness rankings of the two

countries in pairs that were observed. Signs of the SRC indicate the direction of the relationship

between independent variable X and dependent variable Y. The value of SRC is between 0 and

1 that when X and Y perfectly monotonically related, then the SRC will be 1.

A. Revealed Comparative Advantage (RCA)

To see the competitiveness of export products, the indicator used is the Revealed

Comparative Advantage (RCA) indicator where if RCA> 1, it indicates the existence of

comparative advantage.

RCA = (Xij / Xj) / (Xi

w / X

w)

where:

Xij = exports of commodity i of country j

Xj = total exports of country j

Xiw = world exports of commodity i

Xw = total world exports

B. Intra Industry Trade (IIT)

To see the flow of international trade we use the indicator of Intra-Industry Trade, or also

often called the Grubel-Lloyd index (IIT). Based on the formula, the indicator is in a value

between 0 and 1. IIT that is approaching 0 reflects trade flows that are inter-industry, while if IIT

is approaching 1, it indicates that trade flows are intra-industry. In general, this indicator explains

that, a commodity of a country tends to have be related in the chain of international trade if

value is close to 1. This can be illustrated with export and import trade of a country for

manufacturing industry in the same group of goods (usually refers to groups of goods according

to SITC). A country can export electronic components and at the same time import of electronic

articles. On the other hand, for the trade of certain commodities such as natural resource-

based commodities such as oil and gas, a country tends to act as an exporter and do limited or

no import at all. If this happens then, the value of oil and gas commodity IIT is close to 0, or that

the trade is inter-industry.

(III.1)Σi i i

i i i i

ρ( (( (

) )) )

x x y y

Σi x x y y 22=

Σ


To measure the IIT level, then the Grubel and Llyod is used as follow:

(III.2)

Where:

X(i,j,t) is the export value of commodity i of country j in year t

M(i,j,t) is the import value of commodity i of country j in year t

The calculation here is using 3 digit-SITC data classification of commodity i which is then

aggregated into 2 digits. In the GLI calculation there is a tendency that the more detailed data

of commodities, the smaller the value of GLI will become. Referring to previous studies, this

research also uses 2 digits considering that it is sufficient to identify the IIT process in ACFTA

countries

C. Index of Export Overlap (IEO)

To measure the level of each ASEAN country competition with China in trade ACFTA and

also the level of competition among ASEAN countries in utilizing the export opportunity to

China, the index of export overlap (IEO) is used. The equation of overlapping index is expressed

by this equation:

GLI ( j,t ) =Σi X ( i, j, t )( + M ( i, j, t ) − X ( i, j, t ) M ( i, j, t )−

Σi X ( i, j, t )( M ( i, j, t )− )

IEO ( j1, j

2, t ) = 100 x Σ min (X (i, j

1, t), X (i, j

2, t) /

t tΣ X ( i, j

1, t )

IEO size is used to measure the level of competition which is indicated by the export share

that overlap between total export of the two economies. The greater the overlap area (area b)

indicates a greater competition between the two countries. The index ranged from 100 which

means the full overlap and 0 which means no overlap.


Diagram III.4:Competition Between Economy A and B with Overlapped Export Size

D. Index of Export Similarity (IES)

Index of Export Similarity is used to measure the extent of similarity of the export products

composition from two economies. The similarity index equation is expressed as the following:

where:

s(i,j,t) is the share of the export commodity i toward the total economy export j in year t

IES index value ranges from 0 to 100 where 100 indicates that the export composition of

the two economies are identical, whereas 0 if the two are very different. As IES ignores the size

effect of its exports, IES analysis is always juxtaposed with the IEO indicator.

III.5. Data

As stated before, The data used in the analysis of this research are derived from GTAP

version 7.0 data with benchmark data in 2004. The coverage of countries in the data base of

GTAP reaches 113 countries with 57 details of the commodities sector. Meanwhile, for the

analysis of the indicators of international trade we use data from UNCOMTRADE which mainly

include import export data to the countries within the scope of observation, which is ACFTA

members. The processed data is in period 2001-2008

Source : Regional Economic Outlook: Asia and Pasific, Oct 2007

a

b

c

Economy A

Economy BEXpo

rt V

alue

Comodity (i)

IEO for economy A = b/(a+b)IEO for economy B = b/(c+b)

Lower valueadded

Higher valueadded

IES ( j1, j

2, t ) = 100 x Σ s (i, j

1, t)s (i, j

2, t) /

i iΣ s ( i, j

1, t )2

iΣ s ( i, j

2, t )2


IV. RESULT AND ANALYSIS

Results of the simulation generated from the GTAP model includes a variety of indicators

that may be developed further. But even so, this research is more focused on the analysis of

export commodities of ACFTA member, especially Indonesia. In general there are two parts of

focus on the analysis. The first part is to see the effects of shocks giveb to the member countries

of ACFTA, while the second part leads to the quantitative impact of trade by commodity details.

The result of the first part of the analysis is to see how the balance between the impact of trade

direction and trade creation as a result of the implementation of the FTA.

IV.1. Calculation Result with GTAP Model

Various literature studies provide a general picture of the impact of trade between member

countries in a trading arrangement which tend to increase. But the trade with the non-member

countries will decline. Analysis of trade effects in a trade group is often known by the analysis

of trade creation and trade diversion. We can see the impact entirely, by comparing the magnitude

of each of the two trading effects. If the trade creation impact is larger, in general the trade

agreements benefit the members. And vice versa if the trade creation impact is lower, then the

trade agreements do not benefit the members overall

Though it was found that the impact of trade creation is more prominent than trade

divertion, we need a further observation in general to see, whether positive results are enjoyed

equally by member countries or not. Likewise, the details of export commodities which have

increased need to be further explored of whether a generic commodity, in general, is in control

by all or a certain group of member country

The increase in trade volume among ACFTA member is mainly caused by the introduction

of the Chinese market and the enactment of a lower tariff. Thus, the occurrence of trade

diversion which was originally addressed to the non-ACFTA trading partners shifts toward the

fellow members of the ACFTA. This process of change can be in analogy with the existence of

a nominal amount of funds held by economic agents (countries) that can be spent with more

goods as a result of declining prices. Importer preferences also change in the face of dynamics

of changes in import prices as a result of tariff reduction. If the reduction in import tariffs

causes the price to be cheaper than the price of goods originating from non-member countries

(assuming the quality of goods are the same), then a decline in trade with non-member country

or trade divertion will take place


GTAP simulation results to measure the impact of trade (trade effect) as a whole (net

effect) for ACFTA member countries are reflected in Figure III.5 and Figure III.6. Total net trade

creation in the region of ACFTA is 2.1%, sourced from the trade creation among member

countries ACFTA by 18.4% and the decline in trade diversion which is the reduction in trade

with non-member countries (rest of the world) by 1.8%5 .

Figure III.5 The Impact of ACFTA Policyon Trade in ASEAN

Figure III.6. The Impact of ACFTAon Net Trade Creation

From individual member states ACFTA, Vietnam and Thailand have the largest trade

creation, respectively 9.1% and 2.5%, while Singapore got the minimal results of 0.4% (Figure

III.6). The net value of trade creation is influenced by import tariffs during the simulation. The

average import tariff in Vietnam and Thailand are still relatively high, while in Singapore it has

recorded 0%. Based on preliminary data of GTAP, import tariffs for composite goods from

China in Vietnam and Thailand are respectively 18.0 and 11.3%. as for other countries

successively Indonesia (11.3%), Malaysia (7.5%), Philippines (5.3%), as well as other ASEAN

(7.8%). The amount of tariff goods are generally in line with tariffs imposed by China on goods

originating from those countries. Except for goods from Singapore where the China is still

applying the composite tariff of 4.2%

With the enactment of ACFTA trade agreements, the import export development among

ASEAN countries with China is changing. Import of goods from China to Vietnam and Thailand

is raising high by 147%, and 101%, while Singapore recorded a decline in imports by 1.2%

(Figure III.7). This is aligned with the previous explanation that the sensitivity of changes in

import tariffs is in line with the import conditions which was previously high and after the

5 The complete calculation result is shown at appendix 1.

Trade Creation

Trade Diversion

-4.0

-3.0

-2.0

-1.0

0.0

0 5 10 15 20 25 30 35 40

SIN

PHI

IND

THA

MAL

CHI

VIE

ASEAN

OTHERS ASEAN

ACFTA

Net Trade

Percentage (%)

0.0

2.0

4.0

6.0

8.0

10.0

Vietnam,9.1

Thailand,2.5

China,2.3

Indonesia,2.0

Malaysia,1.9 Philippines,

1.32

Singapore,0.4

OtherASEAN, 2.2

ASEAN,2.02

ACFTA,2.1


shock of 0% tariff (post ACFTA). With the changing dynamics of exports and imports as a

result of tariff changes in the scope of ACFTA is reflected in (Figure III.8).

The bilateral pair of Vietnam and China, before the implementation of ACFTA, also apply

high tariff structure on a reciprocal basis. Post-implementation of ACFTA, the simulation results

show the a great change in total exports and imports respectively 6.4% and 11.5%.6

6 The value of export and import growth, in the results of the GTAP model, is recorded as a change from the base value which is usedin the GTAP model data base

Figure III.7. The Impact of Export andImport Change with China (%)

Figure III.8. The Impact on Total Exportand Import Change (%)

For Indonesia, the impact of net creation is by 2.0% which is caused from the trade

creation by10.3% and the trade diversion by 1.5% (Figure III.5 and III.6). The calculation of

trade creation and trade diversion above is based on the total international trade which is the

sum of total value of exports and imports of Indonesia with all its trading partner countries.

Meanwhile, the calculation of net value creation, with approach to the total exports value

minus total imports (net exports), is conducted to see the impact on the balance of payments.

From the simulation of the impact to the balance of payments Indonesia, there is an increase

in total imports by 2.3% or higher compared with the rising in exports by 1.8%. Thus, overall

the Indonesia»s trade surplus fell by 2.3% or USD247 million (Figure III.9 and see Appendix 1

for complete results of net creation with the calculation of total exportsimports and the net

exports)

Although the surplus of Indonesia»s trade balance within the ACFTA region recorded an

increase, the overall impact on the total trade balance still recorded a decrease of surplus.

This is due to the share of Indonesia»s trade with ROW is more dominant compared with the

Export to China, growth (%)

Import from China, growth (%)

-10

20

50

80

110

140

ASEANothers ASEAN

SIN

PILIND

THA

MAL

VIE

0 10 20 30 40 50

Export to World, growth (%)

0.0

2.0

4.0

6.0

Import from World, growth (%)

0 1 2 3

ASEANothers ASEAN

ACFTA

SIN

PILIND

THA

MAL CHI

VIE11.5

6.4


Figure III.11. The Impact of ACFTA Policyon Indonesia»s Export and Import Change

Figure III.12. The Impact of ACFTAon Commodity Sectoral (%)

ROW

ASEAN

Export Growth (%)

INDONESIA 'S TRADE

-10

0

10

20

30

40

50

-10 -5 0 5 10 15 20 25 30 35 40 45

Import Growth (%)

TOTAL

CHINA

ACFTA

%

-8

-4

0

4

8

12

China ACFTA ASEAN ROW World

-2.3

6.5

8.3

-3.5-2.3

-6.2

8.39.5

-0.6

0.5

57 Commodities Sector42 Commodities Sector

Figure III.9. The Impact of ACFTAon Indonesia»s Net Exports (%)

Figure III.10. The ShareExport of Indonesia √ GTAP Data (%)

ACFTA region. As an illustration, Indonesia»s exports (GTAP level data base) with ROW trading

partners reached 74%, or far larger than the export with fellow members of the ACFTA by

26% (Figure III.10)

From the simulation results we obtained the change in Indonesia»s import and export,

with trading partners among members of ACFTA, each grew by 11.7% and 9.1%. With the

increase in exports greater than imports, the impact on Indonesia»s trade surplus recorded an

increase of 6.5% or USD253 (Figure III.9). Meanwhile, Indonesia»s import and export transactions

with trading partners from the ROW recorded a negative growth each by -1.7% and -1.3%,

thus the balance of trade fell by 3.5% or USD499 million

USD Millions Growth %

-600

-400

-200

0

200

400

600

Mutation of Trade Balance (USD)

Mutation of Trade Balance (%)-10.0

-8.0

-6.0

-4.0

-2.0

0.0

2.0

4.0

6.0

8.0

10.0

-15

268 253

-499

-247

-2.3

8.36.5

-3.5-2.3

China ASEAN ACFTA WOR Total China ASEAN ACFTA WOR Total

ASEAN16%

CHINA10%ACFTA

26%ROW74%


Based on figure III.9 above, it has been shown that the simulation results in the growth of

the trade balance fell by 2.3%. Simulation of the GTAP model of exports and imports are

derived from details of the 57 commodities consist of 42 export and import (tradable)

commodities, while the other 15 commodities are in the form of services or of non-tradable

commodities (Grouping Table of 42 tradable sectors and the conversion tables are available in

appendix 2 to 5). Separation of these groups of goods are required to facilitate the subsequent

analysis which use the import export data from UNCOMTRADE. As known, import export

statistics on international trade in various publications including UNCOMTRADE, are tradable

commodities. Meanwhile, in the analysis of the real sector in the context of GDP, the discussion

of commodities consists of tradable and non-tradable commodities. Therefore, the import and

export simulation results derived from GTAP can be further learnt for a more detailed analysis,

one of them by analyzing the tradable commodity

There is a difference the result of simulation when we compare the total results of 57

commodities and the 42 tradable commodities. The overall changes of impact in Indonesia»s

net exports toward the 42 commodities are presented in appendix 2. However, to facilitate

tabulation, the 42 tradable commodities can be further aggregated into 6 types of main tradable

commodities, as seen in table III.2 and III.3 (The conversion table of 6 types of primary tradable

commodities and 1 service commodity is shown in appendix 5). From Figure III.12 and table III.3

we can see that the simulation results of total net exports in 42 commodities (tradable) have

grown by 0.5%. The simulation results of other tradable commodities of Indonesia»s exports to

China increased high enough to 41.4% so that the overall exports to the ACFTA rose 11.9%

(Table III.2). Meanwhile, a negative impact of net exports (trade balance) arises in trading with

China and ROW (Table III.3).

Table III.2 The Post Impact of ACFTA Policyon the Growth of IndonesiaExport Commodities (in %)

Commodities Sector ASEAN ACFTA China ROW World

Agricultural Products -10.9 -5.3 33.9 -0.5 -2.0Food Products -4.7 4.7 16.5 -1.8 -0.1Extractive Industries -0.3 2.2 5.2 -0.6 -0.1Light Industry -21.3 17.6 60.4 -1.7 0.5Heavy Industry -3.2 18.2 48.7 -3.0 4.7Technology Intensive Industries -3.11 2.3 63.1 -1.8 3.9Total -4.4 11.9 41.4 -1.7 2.1

Table III.3 The Post Impact of ACFTA Policy onthe Growth of Indonesia

Net Export Commodities (in %) 7

Commodities Sector ASEAN ACFTA China ROW World

Agricultural Product -14.3 -49.2 -7.8 1.2 -3.8Food Product -37.8 4.9 9.3 -3.1 -1.9Extractive Industries 2.5 -0.5 -30.2 -1.1 -1.7Light Industry -32.2 -90.2 -256.2 0.3 -1.7Heavy Industry 27.7 79.3 70.7 -20.9 20.6Technology Intensive Industries 27.7 -9.2 -43.3 15.9 1.3Total 9.5 8.3 -6.2 -0.6 0.5

7 Negative growth indicates a contribution in declining the trade balance, while the positive balance of trade means a controbution inraising the balance


Table III.4The Growth of Reduction on Import Tariff

DutyTariff

Y E A R

0% 2,857 25.6% 2,864 25.6% 2,639 30.2% 2,639 30.2% 5,709 65.3% 7,306 83.6% 7,306 83.6% 7,778 89.0%5% 3,893 34.8% 3,888 34.8% 3,218 36.9% 3,219 36.8% 2,219 25.4% 622 7.1% 622 7.1% 150 1.7%8% 86 1.0% 85 1.0% 33 0.4% 33 0.4% 33 0.4% 33 0.4%8% 1,850 21.2% 1,866 21.4% 3 0.0% 3 0.0% 3 0.0% 3 0.0%

10% 1,702 15.2% 1,702 15.2% 131 1.5% 131 1.5% 95 1.1% 95 1.1% 95 1.1% 95 1.1%12% 90 1.0% 90 1.0% - 0.0% - 0.0% - 0.0% - 0.0%13% 18 0.2% 18 0.2% 48 0.5% 48 0.5% 48 0.5% 48 0.5% 48 0.5% 48 0.5%15% 1,537 13.8% 1,537 13.8% 315 3.6% 304 3.5% 278 3.2% 278 3.2% 278 3.2% 278 3.2%20% 269 2.4% 269 2.4% 126 1.4% 123 1.4% 123 1.4% 123 1.4% 123 1.4% 123 1.4%25% 318 2.8% 318 2.8% 20 0.2% 20 0.2% 19 0.2% 19 0.2% 19 0.2% 19 0.2%30% 39 0.3% 39 0.3% 39 0.4% 39 0.4% 39 0.4% 39 0.4% 39 0.4% 39 0.4%

>30% : 538 4.8% 538 4.8% 170 1.9% 173 2.0% 172 2.0% 172 2.0% 172 2.0% 172 2.0%TOTAL 11,171 100.0% 11,173 100.0% 8,732 100.0% 8,737 100.0% 8,738 100.0% 8,738 100.0% 8,738 100.0% 8,738 100.0%

2005 2006 2007 2008 2009 2010 2011 2012Total

Tariff PostPercentage Total







Tariff PostPercentage

AVERAGE OFAVERAGE OFAVERAGE OFAVERAGE OFAVERAGE OF 9.57%9.57%9.57%9.57%9.57% 9.49%9.49%9.49%9.49%9.49% 6.38%6.38%6.38%6.38%6.38% 6.38%6.38%6.38%6.38%6.38% 3.83%3.83%3.83%3.83%3.83% 2.92%2.92%2.92%2.92%2.92% 2.92%2.92%2.92%2.92%2.92% 2.65%2.65%2.65%2.65%2.65% DUTY TARIFF DUTY TARIFF DUTY TARIFF DUTY TARIFF DUTY TARIFF

IV.2. The Analysis Result on International Trade Indicator

Based on the output generated by the GTAP model, the development of analysis is aimed

at the direction of the opportunities and challenges of Indonesia»s export product development.

Development of the analysis is done by basing the GTAP model simulation results which are

combined with the analysis of trading indicators. Based on the findings in the model in the

previous section, we have produced details of commodities which hold the chance of positive

contribution to the balance of trade in some 42 commodities in the tradable goods groups.

From the increasing number of export commodities, we will map further by looking at the

competitiveness of the commodity in the ACFTA market.

At this stage of data processing, there are two main sources of the commodity details

based on the GTAP commodity and SITC 3-digit (ver.3). Therefore it is necessary to convert

some of the details of SITC commodities, as much as 261, into the details of commodities

which is in accordance with the number of GTAP commodities, which is only 42. The main

source of the conversion comes from the GTAP model discussion forums at Purdue University

website. 8

Meanwhile, to provide a better analysis, we divide the period of observation of the arranged

indicators. This periode separation, referred as period I and II, is also intended to see the impact

of international trade before and after the implementation of ACFTA policies. The first period

covers the incoming data of the year 2001-2004, while the period II covers the year 2005-

2008. The dividing line of this two period separation, is the time ACFTA policy was in

8 Complete conversion table is available and can be requested to the author or to the editor of BEMP.


implementation by 20049 . Based on data from the Ministry of Trade of Indonesia, the

implementation of ACFTA with the application of import tariffs has been gradually running the

25.6% or as many as 2857 tariff alredy recorded 0% in 2005 (Table III.4). Growth of these 0%

tariffs continued to increase to 83.6% or as much as 7306 postal tariffs in 2010. The progress

to all 0% tariff for members of the ACFTA, is according to the phasing scheme which has been

arranged in staging scheme of early harvest program, normal track and sensitive/highly sensitive

list.

IV .2.1.The Analysis Approach on Competitiveness of RCA and IIT ProductLinkages

There are two main indicators used for the analysis in the following sections. The use of

RCA and IIT indicators together, among others, presents in the Yumiko»s (2005) work. The

similar competitiveness of the commodities from the measurement of produced by the RCA

indicator will be tested further using the Spearman rank correlation (SRC). This SRC test also

has been used in a study conducted by Shafaeddin (2002).

RCA in this section analysis calculation is using market trading partners» data in the

ACFTA region as Indonesia»s total exports. The scope of coverage is to portray the RCA

competitiveness of Indonesian commodities in the market ACFTA. Similarly for the approach in

measuring IIT indicator we use the import export data with trading partners in the region

coverage ACFTA. Using this combination of the two indicators, the first step is to identify the

distribution of Indonesia»s export commodities based on comparative advantage and IIT indicators.

Thus the data processed will be mapped based on certain restrictions. For RCA, the

restriction of commodities with high and low competitiveness is determined by the division of

RCA values below and above 1. Meanwhile, the central determinant for IIT indicator is 0.5.

Based on the quadrant map, as reflected in Figure III.13 - III.14, quadrant I is also called the

main quadrant where the commodities with RCA above 1 and has a high-linkages in the chain

of trade with partner countries of the ACFTA region based on the IIT indicators. In general,

commodities with high IIT and RCA have the potential to have a greater chance to survive and

make penetration in a competitive market. High IIT indicators show a level of confidence of

export competitiveness of the RCA with a more convincing chance. Quadrants II and IV are also

considered potential since they have either high RCA or IIT as an advantage. Meanwhile the

quadrant III is the development quadrant since it has low value of RCA and IIT.

9 Ratification of ACFTA agreement framework through Keppres No.48/2004


In the two observation periods which are periode I and II, from Figure III.13 √ III.16 we

can obtain a general picture that there is a tendency to decreasing of the competitiveness

quality of Indonesian export commodities in ACFTA region. Based on the pattern of distribution

of commodities in the two periodes, as depicted in Figure III.13 and III.14, we can see the

development of export share shifts per quadrant. The export value share in quadrant I declined

from 33% to 19% with the number of commodities remains the same which is 9 (with different

composition or type). Some of Indonesia»s prime commodities that remain in the main quadrant

are oil, motored vehicles, textiles, and beverages. Relatively ideal conditions occur if the

development indicate a larger increase in export share in the quadrat I. The complete results of

composition and scope of the commodity per quadrant as measured in the RCA and IIT matrix,

are presented in appendix 6. To simplify the matrix table in appendix 6 can be simplified as

shown in Figure III.13 and III.14 for the 42 types of tradable commodities.

Figure III.13.Quadrant of RCA and IIT in Period I

Figure III.14.Quadrant of RCA and IIT in Period II

Quadrant IV

Quadrant II

Quadrant III

IIT

RCA

Quadrant I

-0.5 0.5 1.5 2.5 3.5 4.5-

0.2

0.4

0.6

0.8

1.0

Quadrant IV

Quadrant II

Quadrant III

Quadrant I

-0.5 0.5 1.5 2.5 3.5 4.5-

0.2

0.4

0.6

0.8

1.0IIT

RCA

Meanwhile, a more pessimistic result is shown when oil and gas commodities are excluded

in the calculation of RCA and IIT indicators. By using the data in the second period, the share of

export commodities in the first quadrant decreased from 19% to 12%. This development has

become important to be observed given the diminishing role of oil and gas commodities, while

the development of the non-oil commodities is still challenged by various obstacles. The complete

results for the analysis of this section are presented in Appendix 6, including the commodities

in each quadrant


Figure III.15. The Growth of Export Shareper Quadrant from Period I to II

Figure III.16. The Export Share Quadrant IIWith and Without Oil and Gas

IV.2.2. The Analysis Approach of Competition Intensity

To provide a more complete result, this study also illustrates the challenges and

opportunities of Indonesia»s export commodities in ACFTA market. The analysis is done by

using the index of export similarity (IES) and the index of export overlap (IEO) indicators. Technical

analysis is conducted by comparing the characteristics of each country»s exports in ASEAN

bilaterally with China. After the result of IEO and IES indicators is generated for each country,

the next stage is to compare the results between the two periods of observation: period I

(2001-2004) and period II (2005-2008). With the two observation period, the dynamics that

occur can be more interesting to be further reviewed

As China join the ASEAN market, there is a threat of a decline for Indonesian exports

among ASEAN member trading partners that have been established so far. From the

measurement of competition intensity of exports of each country in ASEAN with China, we

gain an overview of developments which tend to decrease the intensity of competition in two

observation periods (Figure III.17) 10 . The intensity of competition tends to increase when both

indicators are showing an increase. Of the two observation periods of both indicators, we

obtain that the development of Indonesian products have the tendency to diminish their intensity

of competition with the presence with China»s export products. The reduced intensity of

competition of Indonesian products in China are in line with the rising share of Indonesia»s

exports of natural resource-based commodities such as mining and other natural products such

33%

31%

19%

18%

Quadrant I,19%

Quadrant IV,41%

Quadrant III,III%

Quadrant II,37%

19%

41%

37%

Quadrant I,12%

Quadrant IV,41%

Quadrant III,4%

Quadrant II,43%

3%

10The first period is shown in blue and the seconf perios is shown in red.


as oil and gas, palm oil and rubber along with rising prices and global demand. On the other

hand, the composition of Chinese exports are likely to lead to industrial products (Figure III.18)11 .

Based on the observation of IEO indicator, countries with relatively small scale of economies

have a relatively high index value such as Brunei, Philippines, Chambodia, and Vietnam.

Meanwhile, from the IES, a relatively advanced countries like Singapore, Malaysia, and Thailand

have a relatively high index. The high index of IES of some relatively advanced countries in the

ASEAN countries with China is also aligned with the development of the Chinese exports

proportion that is relatively high in industrial products.

Figure III.17.The Growth of Competition Intensitywith China in 2 Observation Periods

Figure III.18. The Growth of ExportShare of Industrial Commodities

11The share of industrial exports is derived from the sum of the value of exports in SITC with code digit that begin with 5 to 9, whilefor the code digits from 0 to 4 are not the industry

12 The test is based on the 50 largest commodities, based on the share that ranges around 90 percent.

0

10

20

30

40

50

60

70

80

90

100

40 50 60 70 80 90 100

IES

IND

BRN

CAM

MAL

PILSIN

THA

VIE

IEO

%

0

20

40

60

80

100Period I (2001-2004)

Period II (2005-2008)

Indonesia Singapore Thailand Philippines Malaysia Vietnam Cambodia Brunei China

To provide support for the above analysis conclusion, where the intensity of export

commodities competition, particularly between Indonesia and China that are increasingly

declining, we conducted a test with additional analysis tools. Tests are conducted by performing

Spearman rank correlation test (SRC) on the RCA indicator. SRC test for RCA of Indonesia and

China concluded a negative relationship with a significant level of 1% to 50 commodity criteria

and RCA> 1 (Table III.5). By testing on the largest 50 commodity categories we obtain coefficient

of -0.3 with the significant level of 5%12 . Similarly to the tests on the commodities which have

high competitiveness or RCA> 1 it yielded coefficient of -0.54 with significant level of 1%. As

for the test on the overall commodity based on 2-digit SITC amount 69 commodities, it produced

negative but insignificant relationship. The negative Spearmans»s Rho coefficient can be

interpreted a different structure on the competitiveness of Indonesian export commodities to


China. These results may also mean that Indonesia»s main export commodity is not the Chinese

leading export. Tests on the two observation periods show consistent results for Indonesia,

which yield negative and significant coefficient.

Table III.5The Spearman Rank Correlation Test on RCA

Period I (2001 - 2004)

All CommoditiesAll CommoditiesAll CommoditiesAll CommoditiesAll Commodities(69 Commodities)(69 Commodities)(69 Commodities)(69 Commodities)(69 Commodities)Spearman»s rho: -0.04 0.03 0.03 0.08 -0.31 0.34 0.08 0.34 -0.20 0.04 0.02 -0.02 -0.18 0.21 0.16 0.31degrees of freedom: 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67 67P-value: 0.71 0.82 0.79 0.52 0.01 0.00 0.53 0.00 0.10 0.75 0.90 0.86 0.13 0.09 0.18 0.01

50 Commodities50 Commodities50 Commodities50 Commodities50 CommoditiesSpearman»s rho: -0.30 -0.25 -0.14 0.00 -0.57 0.19 0.02 0.28 -0.47 -0.28 -0.28 -0.14 -0.53 -0.07 -0.19 0.16degrees of freedom: 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48P-value: 0.03 0.08 0.34 1.00 0.00 0.20 0.90 0.05 0.00 0.05 0.05 0.32 0.00 0.62 0.20 0.26

RCA >1RCA >1RCA >1RCA >1RCA >1Spearman»s rho: -0.54 -0.10 -0.07 -0.28 -0.59 -0.23 -0.7 10.00 -0.51 -0.61 -0.37 -0.69 -0.56 -0.36 -0.28degrees of freedom: 31 18 30 7 18 21 17 2 28 19 29 11 23 24 7P-value: 0.00 0.67 0.69 0.46 0.01 0.29 0.00 1.00 0.00 0.00 0.04 0.01 0.00 0.07 0.46

Period II (2005 - 2008)

C h i n a C h i n a

IND SING THAI PHI MAL VIET CAMB BRU IND SING THAI PHI MAL VIET CAMB BRU

Similar results to Indonesia are also found in other ASEAN countries. In general, the test

on 50 commodities, and on commodities with high RCA showed a negative and significant

relationship. This shows that the Chinese exports commodities to ASEAN is not a primary

commodity from other ASEAN countries.

With the intensity of competition indicators and SRC test for the RCA indicator, we obtained

that more evidence for the conclusion that the decrease in the intensity of competition between

China and Indonesia is accompanied with the structure of export commodities which does not

compete one with another, which is similar with the exports commodities of other ASEAN

members. These results illustrate the existence of a more complementary relationship so that

the Chinese export to ASEAN is relatively easy. From the quantitative results of GTAP model, it

is also shown the increase of Chinese exports to ASEAN that reached 50.5% (Appendix 1).

Analysis of China»s market opening opportunities can also be done with the IES and the

IEO indicators. Unlike the measurement of IEO and IES indicators in the previous section, in

which China became the center of attention, we can use Indonesia as the central point of

attention. Bilaterally between Indonesia and each ASEAN country, there is a pattern of diminishing

competition relationships, supported by the IEO and IES indicators that go down (Figure III.19).

This indicates the relatively reduced level of product competition among ASEAN countries to

China. The GTAP simulation results also indicated that the overall exports from ASEAN to China


increased by 31.1% with the lowest range of Philippine exports of 16.1% and the highest is

Thailand»s exports by 43.3% (Appendix 1). Unlike the case when the export commodity that is

used is the total exports of each country. Figure III.18 reflects the degree of homogeneity

between Indonesian export products with each of the ASEAN countries in the world market is

higher than exports to the market ACFTA13 . Among ASEAN countries, Vietnam export products

relative have the highest index of IES.

13 Blue field represents the index size for export to the world market, and the red field represents the export to the ACFTA market

Figure III.19. The Growth of Indonesia»sCompetition Intensity with ASEAN

to Chinese Market

Figure III.20. The Comparation of Growthof Competition Intensity in ACFTA and

World Market

IES

BRN

CAMMAL

PILSIN

THA

VIE

IEO0

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 1000

10

20

30

40

50

60

70

80

90

100

0 20 40 60 80 100

IES

MAL

PILSIN

THAVIE

IEO

V. CONCLUSIONS

Trade arrangement within the ACFTA framework provides opportunities to increase exports

of Indonesia. From the results of GTAP model, Indonesia overall has a net trade creation of 2%

that comes from the effects of trade creation of 10.3% from ACFTA members and trade diversion

of -1.5% with ROW trading partners. Although the cooperation agreement ACFTA imposes

negative impact on overall Indonesia»s trade balance by a decrease of 2.3%, the results of

further analysis of the international export commodities (tradable) show the positive impact of

0.5%.

In terms of export, Indonesia commodities are likely to increase by 2.1% mainly due to

the increase in exports to China. Opportunities for market expansion into China is supported

by the characteristics of the export commodities of Indonesia and other ASEAN countries which

have a relatively low degree of competition. Thus, export goods from Indonesia and ASEAN in


general are much easier to expand. Result analysis of IEO and IES indicators in the two periods

of observation lead to the conclusion that, the degree of competition intensity, of Indonesian

export goods to the ACFTA region bilaterally with individual ASEAN countries, is declining. The

conclusion is also supported by the degree of homogeneity of export commodities to ACFTA

that is lower than the overall exports to world markets. With such a low homogeneity, the level

of competition with other ASEAN countries to the Chinese market is relatively reduced.

However, Indonesia»s exports face a new challenge with the entry of imported goods

China in ASEAN region. The trade with other countries in the region, which has been interwoven

so far, is potentially decreasing. From the results of GTAP model, we obtained the estimates of

ASEAN countries» exports to the ASEAN region that has decreased by 4.9%, including Indonesian

exports decline by 4.4%. On the other hand, China»s export to ASEAN increased by 50.5%.

The results of this paper shows that the export commodities of China and ASEAN countries

tend to indicate the decline of the level of commodity equation. This is in line with the growth

of goods exports from China that are moving towards exports of industrial goods. From the

Spearman Rank Correlation test results on RCA indicators, it generally shows a more

complementary relationship between China»s export goods with ASEAN countries.

The challenge of improving Indonesia»s exports in ACFTA era is increasing with the declining

of Indonesian exports competitiveness of. Based on historical data which is divided into two

periods, we found that the share of principal commodity groups decreased from initially 33%

to 19% of total Indonesian exports. The challenge is growing as one component in the formation

of export share comes from oil and gas sector. If we remove the oil and gas export commodities

in the calculation, the share of primary commodity exports which intially reached 19% will be

dropped to 12%.

To take advantage of the ACFTA agreement on export development, we need a strategy

to move the basket of commodities, especially non-oil exports from quadrants II and IV to the

quadrant I. Development of export commodities which have high competitiveness needs to

consider also the characteristics of commodities that have high relevance within the international

trade chain. The results of this research show that potential commodities with high IIT indicators

that need to have the competitiveness strengthened are the machinery and parts, chemical

industry, electronic equipment, and metal and iron industry. As for potential commodities with

high RCA but need a high added value, in general are natural resource-based commodities that

should be processed further in the form of product diversification and higher value products.

Meanwhile, related to the challenges faced by the rampage of China»s products, we

should utilize the imported goods from China with medium and high technology which comes


from countries outside the region. Thus, we can provide a broader option for producers to

invest in machinery and equipment with a range of goods from China with more competitive

price. Furthermore, we expected that the direction from ACFTA cooperation can improve welfare

in the region and particularly for Indonesia.


Deardorff, V. A. (1995). ≈Determinants of Bilateral Trade: Does Gravity Work in a Neoclassic

World?∆ NBER Working Paper No. 5377.

Endy Tjahjono, M. Barik Bathaluddin, dan Justina Adamanti (2009): ≈ Semar 2009: Suatu Model

Financial Computable General Equilibrium∆. WP/ / 2009 (Desember 2009).

Frankel, Jeffrey (1997): ≈Regional Trading Blocs in The World Economic System,∆ NBER Working

Paper Series 4050.

Haryadi, Rina Oktaviani, Mangara Tambunan dan Noer Azam Achsani. ≈Dampak Penghapusan

Hambatan Perdagangan Sektor Pertanian Terhadap Kinerja Ekonomi Negara Maju dan

Berkembang∆. April 2008.

IMF (2007): World Economic Financial Surveys: Regional Economic Outlook Asia and Pacific.

Oct 2007.

Joseph F. Francois, Luis Rivera dan Hugo Rojas-Romagosa. ≈Economic perspectives for Central

America after CAFTA. A GTAP-based analysis∆, February 2008.

Kemp, Murray C., and Henry Y.Jr.Wan. (1976). ≈An Elementary Proposition Concerning the

Formation of Customs Unions.∆Journal of International Economics 6 (1976): 95-97.

Kemp, Murray, C. (1964): The Pure Theory of International Trade. Prentice-Hall, Englewood

Cliffs, N.J., 176-177.

Krueger, Anne O (1999). ≈Trade creation and Trade Diversion under NAFTA∆. National Bureau

of Economic Research, WP 7429.

Ohyama, M. (1972). ≈Trade and Welfare in General Equilibrium∆. Keio Economic Studies 9, 37-

73.

Okamoto, Yumiko (2005). ≈ASEAN, China, and India: Are they more competitive or

complementary to each other?∆.

Saktyanu K. Dermoredjo, Wahida, dan Budiman Hutabarat; Analisis Dampak Penurunan Subsidi

Ekspor Negara Maju terhadap Produksi Pertanian Indonesia. Desember 2007.

Sanchez, Manuel dan Karp, Nathaniel (2000). ≈NAFTA»s Economic Effects on Mexico∆. NBER.

Shujiro Urata dan Kozo Kiyota; The impacts of an East Asia FTA on Foreign Trade in East Asia.

Desember 2003.

Thomas W. Hertel, Global Trade Analysis, Modeling dan Applications, Cambridge University

Press 1997.

REFERENCES


Tsutsumi, Masahiko (2001): ≈How Will the Regional FTAs Shape the Indonesian Economy?

Evaluation by the Computable General Equilibrium Model∆. TSQ Discussion Paper 2001/02-

No. 2. (August 2001).

Tubagus Feridhanusetyawan, Yose Rizal Damuri; Economic Crisis and Trade Liberalization: A

CGE Analysis On The Forestry Sector. February 2004.

Vanek, Jaroslav (1965). General Equilibrium of International Discrimination: The Case of Customs

Unions. Cambridge, MA: Harvard University Press.

Viner, Jacob (1950). The Customs Union Issue, Carnegie Endowment for International Peace,

New York.


Appendix 1RESULT FROM RUNNING MODEL GTAP (i)

Total Trade with the worldBefore 87,511.4 152,058.6 150,571.0 119,465.2 50,743.5 32,196.5 12,418.0 604,964.2 680,765.6 1,285,729.8After 89,068.8 154,265.3 151,110.7 119,834.8 51,305.8 34,243.0 12,601.5 612,429.9 694,627.9 1,307,057.8

Trade Between MembersBefore 22,913.2 50,074.3 51,485.6 33,341.4 13,183.3 6,680.2 3,136.4 180,814.4 50,581.0 231,395.4After 25,597.1 55,134.6 54,122.1 38,146.3 13,915.4 7,263.7 3,164.2 197,343.4 76,135.7 273,479.1

Trade with ChinaBefore 8,194.3 22,580.3 16,600.2 14,436.8 5,495.9 2,988.6 439.9 70,736.0 0.0 70,736.0After 11,521.6 28,812.4 21,022.3 20,682.0 6,415.2 3,767.2 491.3 92,712.0 0.0 92,712.0

Trade with ASEANBefore 14,718.9 27,494.0 34,885.4 18,904.6 7,687.4 3,691.6 2,696.5 110,078.4 50,581.0 160,659.4After 14,075.5 26,322.2 33.099.8 17,464.3 7,500.2 3,496.5 2,672.9 104,631.4 76,135.7 180,767.1

Trade with non-Members (ROW)Before 64,598.3 101,984.5 99,085.4 86,123.9 37,560.3 25,516.5 9,281.5 424,150.4 630,184.7 1,054,335.1After 63,471.8 99,130.6 96,988.6 81,688.8 37,390.4 26,979.3 9,437.2 415,086.7 618,492.3 1,033.579,0

export to the WorldValue 1,557.4 2,206.7 539.7 369.6 562.3 2,046.5 183.5 7,465.7 13,862.3 21,328.0% 1.8 1.5 0.4 0.3 1.1 6.4 1.5 1.2 2.0 1.7

export to ACFTAValue 2,683.9 5,060.3 2,636.5 4,804.9 732,1 583.5 27.8 16,529.0 25,554.7 42,083.7% 11.7 10.1 5.1 14.4 5.6 8.7 0.9 9.1 50.5 18.2

export to ChinaValue 3,327.3 6,232.1 4,422.1 6,245.2 919.3 778.6 51.4 21,976.0 0,0 21,976.0% 40.6 27.6 26.6 43.3 16.7 26.1 11.7 31.1 0.0 31.1

export to ASEANValue -643.4 -1,171.8 -1,785.6 -1,440.3 -187.2 -195.1 -23.6 -5,447.0 25,554.7 20,107.7% -4.4 -4.3 -5.1 -7.6 -2.4 -5.3 -0.9 -4.9 50.5 12.5

export to ROWValue -1,126.5 -2,853.9 -2,096.8 -4,435.1 -169.9 1,462.8 155.7 -9,063.7 -11,692.4 -20,756.1% -1.7 -2.8 -2.1 -5.1 -0.5 5.7 1.7 -2.1 -1.9 -2.0

Indonesia Malaysia Singapore Thailand Philippines Vietnam Other ASEAN China ACFTAASEAN

E X P O R T


Total Trade with the worldBefore 76,947.2 106,330.1 160,658.5 102,806.7 48,824.9 36,636.9 9,142.6 541,346.9 599,116.4 1,140,463.3After 78,751.1 109,023.8 161,353.0 108,042.8 49,580.5 40,841.0 9,438.2 557,030.4 614,267.2 1,171,297.6

Trade Between MembersBefore 26,780.3 37,141.4 47,641.5 25,166.2 14,151.3 13,068,8 5,771.6 169,721.1 74,668.3 244,389.4After 29,211.4 40,947.6 46,777.7 32,180.0 15,585.8 20,307,6 6,235.2 191,245.3 98,412.1 289,657.4

Trade with ChinaBefore 8,828.6 10,088.2 13,723.0 7,946.1 5,708.3 5,624,5 1,616.2 53,534.9 0.0 53,534.9After 12,170.8 15,346.2 13,562.8 15,956.9 7,567.9 13,890,7 2,378.8 80,874.1 0.0 80,874.1

Trade with ASEANBefore 17,951.7 27,053.2 33,918.5 17,220.1 8,443.0 7,444,3 4,155.4 116,186.2 74,668.3 190,854.5After 17,040.6 25,601.4 33,214.9 16,223.1 8,017.9 6,416,9 3,856.4 110,371.2 98,412.1 208,783.3

Trade with non-Members (ROW)Before 50,167.0 69,188.7 113,017.2 77,640.5 34,673.7 23,568.3 3,371.0 371,626.4 524,448.1 896,074.5After 49,539.7 68,076.2 114,575.2 75,862.9 33,994.6 20,533.5 3,202.8 365,784.9 515,855.0 881,639.9

Import from the WorldValue 1,803.9 2,693.7 694.5 5,236.1 755.6 4,204.1 295.6 15,683.5 15,150.8 30,834.3% 2.3 2.5 0.4 5.1 1.5 11.5 3.2 2.9 2.5 2.7

Import from ACFTAValue 2,431.1 3,806.2 -863.8 7,013.8 1,434.5 7,238.8 463.6 21,524.2 23,743.8 45,268.0% 9.1 10.2 -1.8 27.9 10.1 55.4 8.0 12.7 31.8 18.5

Import from CinaValue 3,342.2 5,258.0 -160.2 8,010.8 1,859.6 8,266.2 762.6 27,339.2 0,0 27,339.2% 37.9 52.1 -1.2 100.8 32.6 147.0 47.2 51.1 0.0 51.1

Import from ASEANValue -911.1 -1,451.8 -703.6 -997.0 -425.1 -1,027.4 -299.0 -5,815.0 23,743.8 17,928.8% -5.1 -5.4 -2.1 -5.8 -5.0 -13.8 -7.2 -5.0 31.8 9.4

Import from ROWValue -627.3 -1,112.5 1,558.0 -1,777.6 -679.1 -3,034.8 -168.2 -5,841.5 -8,593.1 -14,434.6% -1.3 -1.6 1.4 -2.3 -2.0 -12.9 -5.0 -1.6 -1.6 -1.6


Appendix 1RESULT FROM RUNNING MODEL GTAP (ii)

I M P O R T


Total Trade with the worldBefore 164,458.6 258,388,7 311,229,5 222,271,9 99,568,4 68,833,4 21,560,6 1,146,311,1 1,279,882,0 2,426,193,1After 167,819.9 263,289,1 312,463,7 227,877,6 100,886,3 75,084,0 22,039,7 1,169,460,3 1,308,895,1 2,478,355,4

Trade Between MembersBefore 49,693.5 87,215,7 99,127,1 58,507,6 27,334,6 19,749,0 8,908,0 350,535,5 125,249,3 475,784,8After 54,808.5 96,082,2 100,899,8 70,326,3 29,501,2 27,571,3 9,399,4 388,588,7 174,547,8 563,136,5

Trade with ChinaBefore 17,022.9 32,668,5 30,323,2 22,382,9 11,204,2 8,613,1 2,056,1 124,270,9 0,0 124,270,9After 23,692.4 44,158,6 34,585,1 36,638,9 13,983,1 17,657,9 2,870,1 173,586,1 0,0 173,586,1

Trade with ASEANBefore 32,670,6 54,547,2 68,803,9 36,124,7 16,130,4 11,135,9 6,851,9 226,264,6 125,249,3 351,513,9After 31,116,1 51,923,6 66,314,7 33,687,4 15,518,1 9,913,4 6,529,3 215,002,6 174,547,8 389,550,4

Trade with non-Members (ROW)Before 114,765,3 171,173,2 212,102,6 163,764,4 72,234,0 49,084,8 12,652,5 795,776,8 1,154,632,8 1,950,409,6After 113,011,5 167,206,8 211,563,8 157,551,7 71,385,0 47,512,8 12,640,0 780,871,6 1,134,347,3 1,915,218,9

Total Net Trade CreationValue 3,361,3 4,900,4 1,234,2 5,605,7 1,317,9 6,250,6 479,1 23,149,2 29,013,1 52,162,3% 2,0 1,9 0,4 2,5 1,3 9,1 2,2 2,0 2,3 2,1

Trade Creation among MemberValue 5,115,0 8,866,5 1,772,7 11,818,7 2,166,6 7,822,3 491,4 38,053,2 49,298,5 87,351,7% 10,3 10,2 1,8 20,2 7,9 39,6 5,5 10,9 39,4 18,4

Trade Creation with ChinaValue 6,669,5 11,490,1 4,261,9 14,256,0 2,778,9 9,044,8 814,0 49,315,2 0,0 49,315,2% 39,2 35,2 14,1 63,7 24,8 105,0 39,6 39,7 0,0 39,7

Trade Creation with ASEANValue -1,554,5 -2,623,6 -2,489,2 -2,437,3 -612,3 -1,222,5 -322,6 -11,262,0 49,298,5 38,036,5% -4,8 -4,8 -3,6 -6,7 -3,8 -11,0 -4,7 -5,0 39,4 10,8

Trade DiversionValue -1,753,8 -3,966,4 -538,8 -6,212,7 -849,0 -1,572,0 -12,5 -14,905,2 -20,285,5 -35,190,7% -1,5 -2,3 -0,3 -3,8 -1,2 -3,2 -0,1 -1,9 -1,8 -1,8

TOTAL TRADE Indonesia Malaysia Singapore Thailand Philippines Vietnam Other ASEAN China ACFTA(EXPORT + IMPORT) ASEAN

Appendix 1RESULT FROM RUNNING MODEL GTAP (iii)


Total TradeBefore 10.564.2 45.728.5 -10.087.5 16.658.5 1.918.6 -4.440.4 3.275.4 63.617.3 81.649.2 145.266.5After 10.317.7 45.241.5 -10.242.3 11.792.0 1.725.3 -6.598.0 3.163.3 55.399.5 80.360.7 135.760.2

Trade between MemberBefore -3.867.1 12.932.9 3.844.1 8.175.2 -968.0 -6.388.6 -2.635.2 11.093.3 -24.087.3 -12.994.0After -3.614.3 14.187.0 7.344.4 5.966.3 -1.670.4 -13.043.9 -3.071.0 6.098.1 -22.276.4 -16.178.3

Trade with ChinaBefore -634.3 12.492.1 2.877.2 6.490.7 -212.4 -2.635.9 -1.176.3 17.201.1 0.0 17.201.1After -649.2 13.466.2 7.459.5 4.725.1 -1.152.7 -10.123.5 -1.887.5 11.837.9 0.0 11.837.9

Trade with ASEANBefore -3.232.8 440.8 966.9 1.684.5 -755.6 -3.752.7 -1.458.9 -6.107.8 -24.087.3 -30.195.1After -2.965.1 720.8 -115.1 1.241.2 -517.7 -2.920.4 -1.183.5 -5.739.8 -22.276.4 -28.016.2

Trade with non-Members (ROW)Before 14.431.3 32.795.8 -13.931.8 8.483.4 2.886.6 1.948.2 5.910.5 52.524.0 105.736.6 158.260.6After 13.932.1 31.054.4 -17.586.6 5.825.9 3.395.8 6.445.8 6.234.4 49.301.8 102.637.3 151.939.1

Total Net Trade CreationValue -246.5 -487.0 -154.8 -4.866.5 -193.3 -2.157.6 -112.1 -8.217.8 -1.288.5 -9.506.3% -2.3 -1.1 -1.5 -29.2 -10.1 -48.6 -3.4 -12.9 -1.6 -6.5

Trade Creation among MemberValue 252.8 1.254.1 3.500.3 -2.208.9 -702.4 -6.655.3 -435.8 -4.995.2 1.810.9 -3.184.3% 6.5 9.7 91.1 -27.0 -72.6 -104.2 -16.5 -45.0 7.5 -24.5

Trade creation with ChinaValue -14.9 974.1 4.582.3 -1.765.6 -940.3 -7.487.6 -711.2 -5.363.2 0.0 -5.363.2% -2.3 7.8 159.3 -27.2 -442.7 -284.1 -60.5 -31.2 -31.2

Trade Creation with ASEANValue 267.7 280.0 -1.082.0 -443.3 237.9 832.3 275.4 368.0 1.810.9 2.178.9% 8.3 63.5 -111.9 -26.3 31.5 22.2 18.9 6.0 7.5 7.2

Trade DiversionValue -499.2 -1.741.4 -3.654.8 -2.657.5 509.2 4.497.6 323.9 -3.222.2 -3.099.3 -6.321.5% -3.5 -5.3 -26.2 -31.3 17.6 230.9 5.5 -6.1 -2.9 -4.0


Appendix 1RESULT FROM RUNNING MODEL GTAP (iv)

EXPORT NET

61The Impact of ACFTA Implementation on International Trade of IndonesiaA

pp

end

ix 2

CO

MPA

RA

TIO

N O

F IM

PAC

T O

F A

CTF

A O

N IN

DO

NES

IA (

CO

MM

OD

ITIE

S) -

(i)

VIMS

1. R

ice0.0

0.00.0

0.00.0

00.0

0.00.0

0.00.0

00.0

0.00.0

0.00.0

00.2

0.1-0.

1-50

.00.0

00.2

0.1-0.

1-50

.00.0

02.

Whe

at2.7

2.70.0

0.00.0

02.7

2.70.0

0.00.0

00.0

0.00.0

0.00.0

03.0

3.00.0

0.00.0

00.3

0.30.0

0.00.0

03.

Cerea

l Grai

n10

.210

.0-0.

2-2.

00.0

010

.19.9

-0.2

-2.0

0.00

0.10.1

0.00.0

0.00

16.4

16.2

-0.2

-1.2

0.00

6.26.2

0.00.0

0.00

4.Ve

getab

le89

.987

.2-2.

7-3.

0-0.

0166

.959

.7-7.

2-10

.8-0.

0523

.027

.54.5

19.6

0.06

257.4

253.4

-4.0

-1.6

0.00

167.5

166.2

-1.3

-0.8

0.00

5.Oi

l See

ds20

.720

.4-0.

3-1.

40.0

020

.319

.9-0.

4-2.

00.0

00.4

0.50.1

25.0

0.00

37.3

36.8

-0.5

-1.3

0.00

16.6

16.4

-0.2

-1.2

0.00

6.Su

gar C

ane

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

7.Fib

er2.9

3.00.1

3.40.0

02.5

2.50.0

0.00.0

00.4

0.50.1

25.0

0.00

7.27.2

0.00.0

0.00

4.34.2

-0.1

-2.3

0.00

8.Cr

opsN

ec40

8.136

4.0-44

.1-10

.8-0.

2039

4.534

0.8-53

.7-13

.6-0.

3713

.623

.29.6

70.6

0.12

1,381

.31,3

33.7

-47.6

-3.4

-0.06

973.2

969.7

-3.5

-0.4

-0.01

9.An

imal

Husb

andr

y1.0

1.00.0

0.00.0

01.0

1.00.0

0.00.0

00.0

0.00.0

0.00.0

04.6

4.5-0.

1-2.

20.0

03.6

3.5-0.

1-2.

80.0

010

.Anim

al Pro

ducts

106.5

119.5

13.0

12.2

0.06

64.4

64.8

0.40.6

0.00

42.1

54.7

12.6

29.9

0.16

295.4

307.2

11.8

4.00.0

118

8.918

7.7-1.

2-0.

60.0

011

.Pur

e Milk

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.20.2

0.00.0

0.00

0.20.2

0.00.0

0.00

12.W

ool

0.10.2

0.110

0.00.0

00.1

0.10.0

0.00.0

00.0

0.10.1

0.00.0

01.7

1.70.0

0.00.0

01.6

1.5-0.

1-6.

20.0

013

. For

estry

46.9

57.8

10.9

23.2

0.05

12.4

12.1

-0.3

-2.4

0.00

34.5

45.7

11.2

32.5

0.14

94.6

104.3

9.710

.30.0

147

.746

.5-1.

2-2.

50.0

014

. Fish

ery13

5.514

3.98.4

6.20.0

411

5.511

6.30.8

0.70.0

120

.027

.67.6

38.0

0.09

447.1

453.3

6.21.4

0.01

311.6

309.4

-2.2

-0.7

0.00

15.C

oal

547.5

551.3

3.80.7

0.02

470.6

454.0

-16.6

-3.5

-0.11

76.9

97.3

20.4

26.5

0.25

4,024

.04,0

19.6

-4.4

-0.1

-0.01

3,476

.53,4

68.3

-8.2

-0.2

-0.01

16.O

il1,6

68.5

1.670

.41.9

0.10.0

159

3.059

6.83.8

0.60.0

31.0

75.5

1.073

.6-1.

9-0.

2-0.

024,1

07.6

4,074

.7-32

.9-0.

8-0.

042,4

39.1

2,404

.3-34

.8-1.

4-0.

0617

.Gas

0.60.6

0.00.0

0.00

0.60.6

0.00.0

0.00

0.00.0

0.00.0

0.00

4,810

.94,7

84.6

-26.3

-0.5

-0.03

4,810

.34,7

84.0

-26.3

-0.5

-0.04

18.M

ineral

Nec

604.4

613.9

9.51.6

0.04

467.5

475.7

8.21.8

0.06

136.9

138.2

1.30.9

0.02

2,140

.02,1

46.0

6.00.3

0.01

1,535

.61,5

32.1

-3.5

-0.2

-0.01

19.M

eat

0.70.8

0.114

.30.0

00.6

0.60.0

0.00.0

00.1

0.20.1

100.0

0.00

4.44.4

0.00.0

0.00

3.73.6

-0.1

-2.7

0.00

20.M

eat P

rodu

cts3.0

3.20.2

6.70.0

02.6

2.3-0.

3-11

.50.0

00.4

0.90.5

125.0

0.01

32.8

31.4

-1.4

-4.3

0.00

29.8

28.2

-1.6

-5.4

0.00

21.V

egeta

ble O

il1,3

77.6

1.479

.010

1.47.4

0.45

584.7

582.3

-2.4

-0.4

-0.02

792.9

896.7

103.8

13.1

1.29

4,433

.74,4

76.0

42.3

1.00.0

53,0

56.1

2,997

.0-59

.1-1.

9-0.

1022

.Dair

y Pro

ducts

28.2

27.8

-0.4

-1.4

0.00

28.0

27.6

-0.4

-1.4

0.00

0.20.2

0.00.0

0.00

76.5

74.4

-2.1

-2.7

0.00

48.3

46.6

-1.7

-3.5

0.00

23.P

roce

ssed

rice

2.42.2

-0.2

-8.3

0.00

1.31.3

0.00.0

0.00

1.10.9

-0.2

-18.2

0.00

33.5

33.3

-0.2

-0.6

0.00

31.1

31.1

0.00.0

0.00

24.S

ugar

3.43.8

0.411

.80.0

02.8

2.90.1

3.60.0

00.6

0.90.3

50.0

0.00

20.1

20.4

0.31.5

0.00

16.7

16.6

-0.1

-0.6

0.00

25. F

ood

Produ

cts40

8.142

8.820

.75.1

0.09

326.7

308.3

-18.4

-5.6

-0.13

81.4

120.5

39.1

48.0

0.49

2,867

.52.8

48.0

-19.5

-0.7

-0.02

2,459

.42,4

19.2

-40.2

-1.6

-0.07

26.B

evera

ges

158.7

128.8

-29.9

-18.8

-0.13

157.2

126.4

-30.8

-19.6

-0.21

1.52.4

0.960

.00.0

122

9.519

9.1-30

.4-13

.2-0.

0470

.870

.3-0.

5-0.

70.0

027

.Tex

tiles

839.1

1.032

.219

3.123

.00.8

551

9.438

4.1-13

5.3-26

.0-0.

9331

9.764

8.132

8.410

2.74.0

84,8

44.5

5,079

.823

5.34.9

0.29

4,005

.44,0

47.6

42.2

1.10.0

728

.App

arel

120.2

108.2

-12.0

-10.0

-0.05

116.0

92.7

-23.3

-20.1

-0.16

4.215

.511

.326

9.00.1

44,0

25.4

3,931

.4-94

.0-2.

3-0.

123,9

05.2

3,823

.2-82

.0-2.

1-0.

1429

. Lea

ther

Produ

cts13

1.217

7.546

.335

.30.2

090

.168

.4-21

.7-24

.1-0.

1541

.110

9.168

.016

5.50.8

52,2

02.6

2,180

.5-22

.1-1.

0-0.

032,0

71.4

2,003

.0-68

.4-3.

3-0.

1230

.Woo

d Pro

ducts

790.2

894.2

104.0

13.2

0.46

258.7

228.9

-29.8

-11.5

-0.20

531.5

665.3

133.8

25.2

1.66

5,766

.25,7

25.6

-40.6

-0.7

-0.05

4,976

.04,8

31.4

-144.6

-2.9

-0.24

Com

mod

ities

Expo

rt to

ACF

TAEx

port

to A

SEAN

Expo

rt to

CHI

NAEx

port

to th

e W

ORLD

Expo

rt to

ROW

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

%%

%%

%42


31.P

aper

Produ

cts1,5

50.6

1,667

.611

7.07.5

0.52

578.3

557.5

-20.8

-3.6

-0.14

972.3

1,110

.113

7.814

.21.7

13,9

49.4

4,005

.856

.41.4

0.07

2,398

.82,3

38.2

-60.6

-2.5

-0.10

32. P

etrole

um31

7.035

5.538

.512

.10.1

714

5.514

4.1-1.

4-1.

0-0.

0117

1.521

1.439

.923

.30.5

01,1

59.1

1,191

.732

.62.8

0.04

842.1

836.2

-5.9

-0.7

-0.01

33. C

hemi

cal

3,373

.34,4

63.7

1,090

.432

.34.8

31,7

95.6

1,766

.8-28

.8-1.

6-0.

201,5

77.7

2,696

.91,1

19.2

70.9

13.91

9,386

.010

,293.5

907.5

9.71.1

16,0

12.7

5,829

.8-18

2.9-3.

0-0.

3134

. Mini

ng Pr

oduc

t27

2.130

5.833

.712

.40.1

520

6.917

9.1-27

.8-13

.4-0.

1965

.212

6.761

.594

.30.7

61,1

75.2

1,186

.611

.41.0

0.01

903.1

880.8

-22.3

-2.5

-0.04

35. Ir

on M

etal

209.5

210.7

1.20.6

0.01

185.6

179.2

-6.4

-3.4

-0.04

23.9

31.5

7.631

.80.0

962

3.161

3.4-9.

7-1.

6-0.

0141

3.640

2.7-10

.9-2.

6-0.

0236

. Log

amNe

c1,4

91.3

1,505

.414

.10.9

0.06

1,280

.31,2

33.7

-46.6

-3.6

-0.32

211.0

271.7

60.7

28.8

0.75

3,814

.13,7

43.6

-70.5

-1.8

-0.09

2,322

.82,2

38.2

-84.6

-3.6

-0.14

37. M

etal P

rodu

cts17

6.117

5.3-0.

8-0.

50.0

016

6.315

3.9-12

.4-7.

5-0.

099.8

21.4

11.6

118.4

0.14

591.2

581.5

-9.7

-1.6

-0.01

415.1

406.2

-8.9

-2.1

-0.02

38. M

otor

Veh

icles

423.0

444.8

21.8

5.20.1

038

8.337

1.5-16

.8-4.

3-0.

1234

.773

.338

.611

1.20.4

891

3.792

6.312

.61.4

0.02

490.7

481.5

-9.2

-1.9

-0.02

39.P

erTran

sport

230.5

211.1

-19.4

-8.4

-0.09

226.6

202.3

-24.3

-10.7

-0.17

3.98.8

4.912

5.60.0

658

6.155

5.1-31

.0-5.

3-0.

0435

5.634

4.0-11

.6-3.

3-0.

0240

.Elec

tronic

s4,7

22.9

5,374

.565

1.613

.82.8

83,3

07.0

3,219

.2-87

.8-2.

7-0.

601,4

15.9

2,155

.373

9.452

.29.1

910

,789.6

11,34

8.755

9.15.2

0.68

6,066

.75,9

74.2

-92.5

-1.5

-0.16

41.M

achin

ery Eq

pt.1,9

83.0

2,265

.028

2.014

.21.2

51,6

45.1

1,612

.6-32

.5-2.

0-0.

2233

7.965

2.431

4.593

.13.9

15,0

19.1

5,242

.322

3.24.4

0.27

3,036

.12,9

77.3

-58.8

-1.9

-0.10

42. In

dustr

yNec

332.5

363.4

30.9

9.30.1

431

0.930

0.3-10

.6-3.

4-0.

0721

.663

.141

.519

2.10.5

21,5

09.8

1,517

.57.7

0.50.0

11,1

77.3

1,154

.1-23

.2-2.

0-0.

04To

tal22

,590.1

25,27

5.22,6

85.1

11.9

11.9

14,54

6.613

,902.9

-643.7

-4.4

-4.4

8,043

.511

,372.3

3,328

.841

.441

.481

,682.0

83,35

6.81,6

74.8

2.12.1

59,09

1.958

,081.6

-1,01

0.3-1.

7-1.

7

Com

mod

ities

Expo

rt to

ACF

TA

42

Expo

rt to

ASE

ANEx

port

to C

HINA

Expo

rt to

the

WOR

LD

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

%%

%%

%

Expo

rt to

ROW

Ap

pen

dix

2C

OM

PAR

ATI

ON

OF

IMPA

CT

OF

AC

TFA

ON

IND

ON

ESIA

(C

OM

MO

DIT

IES)

- (

ii)

63The Impact of ACFTA Implementation on International Trade of IndonesiaA

pp

end

ix 2

CO

MPA

RA

TIO

N O

F IM

PAC

T O

F A

CTF

A O

N IN

DO

NES

IA (

CO

MM

OD

ITIE

S) -

(iii

)

VIMS

1.Ric

e3.2

3.50.3

9.40.0

03.1

2.9-0.

2-6.

50.0

00.1

0.60.5

500.0

0.01

9.810

.20.4

4.10.0

06.6

6.70.1

1.50.0

02.

Whe

at15

.316

.61.3

8.50.0

10.1

0.10.0

0.00.0

015

.216

.51.3

8.60.0

284

7.384

4.2-3.

1-0.

4-0.

0183

2.082

7.6-4.

4-0.

5-0.

013.

Cerea

lGrai

n37

.437

.0-0.

4-1.

10.0

032

.932

.6-0.

3-0.

90.0

04.5

4.4-0.

1-2.

20.0

011

2.811

3.20.4

0.40.0

075

.476

.20.8

1.10.0

04.

Vege

table

302.8

327.9

25.1

8.30.1

082

.277

.2-5.

0-6.

1-0.

0322

0.625

0.730

.113

.60.3

644

5.346

6.020

.74.6

0.03

142.5

138.1

-4.4

-3.1

-0.01

5.Oi

lSeed

s19

.920

.50.6

3.00.0

014

.113

.3-0.

8-5.

70.0

05.8

7.21.4

24.1

0.02

535.0

539.6

4.60.9

0.01

515.1

519.1

4.00.8

0.01

6.Su

garC

ane

0.10.1

0.00.0

0.00

0.10.1

0.00.0

0.00

0.00.0

0.00.0

0.00

0.30.3

0.00.0

0.00

0.20.2

0.00.0

0.00

7.Fib

er3.8

3.6-0.

2-5.

30.0

00.7

0.70.0

0.00.0

03.1

2.9-0.

2-6.

50.0

091

2.189

5.0-17

.1-1.

9-0.

0390

8.389

1.4-16

.9-1.

9-0.

058.

Crop

sNec

97.3

107.2

9.910

.20.0

447

.846

.8-1.

0-2.

1-0.

0149

.560

.410

.922

.00.1

332

4.733

0.45.7

1.80.0

122

7.422

3.2-4.

2-1.

8-0.

019.

Lives

tock

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

159.9

160.4

0.50.3

0.00

159.9

160.4

0.50.3

0.00

10.A

nimal

Produ

cts16

.517

.51.0

6.10.0

06.3

6.2-0.

1-1.

60.0

010

.211

.31.1

10.8

0.01

70.3

71.2

0.91.3

0.00

53.8

53.7

-0.1

-0.2

0.00

11.P

ure M

ilk0.0

0.00.0

0.00.0

00.0

0.00.0

0.00.0

00.0

0.00.0

0.00.0

01.8

1.80.0

0.00.0

01.8

1.80.0

0.00.0

012

.Woo

l0.1

0.10.0

0.00.0

00.0

0.00.0

0.00.0

00.1

0.10.0

0.00.0

02.4

2.40.0

0.00.0

02.3

2.30.0

0.00.0

013

.For

estry

11.6

12.3

0.76.0

0.00

8.17.9

-0.2

-2.5

0.00

3.54.4

0.925

.70.0

159

.159

.90.8

1.40.0

047

.547

.60.1

0.20.0

014

. Fish

ery8.8

8.80.0

0.00.0

07.5

7.4-0.

1-1.

30.0

01.3

1.40.1

7.70.0

018

.819

.00.2

1.10.0

010

.010

.20.2

2.00.0

015

.Coa

l0.4

0.40.0

0.00.0

00.2

0.1-0.

1-50

.00.0

00.2

0.30.1

50.0

0.00

0.40.4

0.00.0

0.00

0.00.0

0.00.0

0.00

16. O

il1,1

44.8

1,127

.7-17

.1-1.

5-0.

0792

0.990

5.9-15

.0-1.

6-0.

0922

3.922

1.8-2.

1-0.

9-0.

033,7

60.4

3,770

.910

.50.3

0.02

2,615

.62,6

43.2

27.6

1.10.0

817

.Gas

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.00.0

0.00.0

0.00

0.10.1

0.00.0

0.00

0.10.1

0.00.0

0.00

18.M

ineral

Nec

107.8

109.4

1.61.5

0.01

62.0

61.6

-0.4

-0.6

0.00

45.8

47.8

2.04.4

0.02

546.6

549.5

2.90.5

0.00

438.8

440.1

1.30.3

0.00

19.M

eat

6.96.8

-0.1

-1.4

0.00

6.86.7

-0.1

-1.5

0.00

0.10.1

0.00.0

0.00

105.6

107.1

1.51.4

0.00

98.7

100.3

1.61.6

0.00

20.M

eat P

rodu

cts10

.413

.43.0

28.8

0.01

3.83.8

0.00.0

0.00

6.69.6

3.045

.50.0

412

1.012

6.55.5

4.50.0

111

0.611

3.12.5

2.30.0

121

.Miny

akSa

yur

55.6

54.1

-1.5

-2.7

-0.01

52.8

51.2

-1.6

-3.0

-0.01

2.82.9

0.13.6

0.00

527.3

536.7

9.41.8

0.02

471.7

482.6

10.9

2.30.0

322

Dairy

Prod

ucts

85.4

85.1

-0.3

-0.4

0.00

84.5

84.1

-0.4

-0.5

0.00

0.91.0

0.111

.10.0

048

6.149

3.17.0

1.40.0

140

0.740

8.07.3

1.80.0

223

Proce

ssed

rice

106.3

106.5

0.20.2

0.00

105.4

104.2

-1.2

-1.1

-0.01

0.92.3

1.415

5.60.0

212

6.012

6.70.7

0.60.0

019

.720

.20.5

2.50.0

024

Suga

r29

1.730

1.59.8

3.40.0

428

5.727

1.2-14

.5-5.

1-0.

086.0

30.3

24.3

405.0

0.29

376.2

387.2

11.0

2.90.0

284

.585

.71.2

1.40.0

025

Food

Prod

ucts

592.2

638.6

46.4

7.80.1

834

4.333

5.3-9.

0-2.

6-0.

0524

7.930

3.355

.422

.30.6

71,1

86.6

1,227

.641

.03.5

0.07

594.4

589.0

-5.4

-0.9

-0.01

26Be

verag

es16

3.916

6.22.3

1.40.0

115

7.915

6.2-1.

7-1.

1-0.

016.0

10.0

4.066

.70.0

520

8.720

9.30.6

0.30.0

044

.843

.1-1.

7-3.

80.0

027

Tex ti

les1,0

71.8

1,514

.144

2.341

.31.7

336

9.531

2.0-57

.5-15

.6-0.

3370

2.31,2

02.1

499.8

71.2

6.04

2,788

.42,9

97.5

209.1

7.50.3

41,7

16.6

1,483

.4-23

3.2-13

.6-0.

6428

Appa

rel20

4.827

5.270

.434

.40.2

896

.370

.0-26

.3-27

.3-0.

1510

8.520

5.296

.789

.11.1

732

6.636

4.938

.311

.70.0

612

1.889

.7-32

.1-26

.4-0.

0929

Leath

er Pro

ducts

199.6

254.3

54.7

27.4

0.21

98.9

87.5

-11.4

-11.5

-0.07

100.7

166.8

66.1

65.6

0.80

441.3

472.5

31.2

7.10.0

524

1.721

8.2-23

.5-9.

7-0.

0630

Woo

d Pro

ducts

121.9

140.8

18.9

15.5

0.07

80.1

74.6

-5.5

-6.9

-0.03

41.8

66.2

24.4

58.4

0.29

318.4

333.7

15.3

4.80.0

219

6.519

2.9-3.

6-1.

8-0.

01

Com

mod

ities 42

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

%%

%%

%

Expo

rt to

ACF

TAEx

port

to A

SEAN

Expo

rt to

CHI

NAEx

port

to W

ORLD

Expo

rt to

ROW


Com

mod

ities 42

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

pre

post

chg

%sh

are

%%

%%

%31

Pape

r Pro

ducts

287.8

299.2

11.4

4.00.0

422

7.422

1.0-6.

4-2.

8-0.

0460

.478

.217

.829

.50.2

21,6

34.1

1,655

.621

.51.3

0.03

1,346

.31,3

56.4

10.1

0.80.0

332

Petro

leum

4,318

.94,4

18.3

99.4

2.30.3

92,8

71.8

2,831

.4-40

.4-1.

4-0.

231,4

47.1

1,586

.913

9.89.7

1.69

6,192

.66,2

72.5

79.9

1.30.1

31,8

73.7

1,854

.2-19

.5-1.

0-0.

0533

Chem

ical

4,128

.14,4

14.5

286.4

6.91.1

23,2

03.5

3,095

.210

8.3-3.

4-0.

6392

4.61,3

19.3

394.7

42.7

4.77

9,888

.410

,207.8

319.4

3.20.5

25,7

60.3

5,793

.333

.00.6

0.09

34M

ining

Prod

uct

326.5

365.9

39.4

12.1

0.15

163.9

154.1

-9.8

-6.0

-0.06

162.6

211.8

49.2

30.3

0.59

646.3

679.8

33.5

5.20.0

531

9.831

3.9-5.

9-1.

8-0.

0235

Iron

Meta

l83

5.091

1.476

.49.1

0.30

383.8

362.5

-21.3

-5.5

-0.12

451.2

548.9

97.7

21.7

1.18

2,955

.02,9

58.4

3.40.1

0.01

2,120

.02,0

47.0

-73.0

-3.4

-0.20

36Lo

gamN

ec30

1.433

7.235

.811

.90.1

415

7.014

7.8-9.

2-5.

9-0.

0514

4.418

9.445

.031

.20.5

41,3

70.2

1,381

.311

.10.8

0.02

1,068

.81,0

44.1

-24.7

-2.3

-0.07

37Pro

dukM

etal

627.9

866.2

238.3

38.0

0.93

291.7

252.2

-39.5

-13.5

-0.23

336.2

614.0

277.8

82.6

3.36

1,345

.61,5

12.5

166.9

12.4

0.27

717.7

646.3

-71.4

-9.9

-0.20

38M

otor

Veh

icles

982.2

1,045

.563

.36.4

0.25

844.0

806.2

-37.8

-4.5

-0.22

138.2

239.3

101.1

73.2

1.22

3,423

.93,4

70.6

46.7

1.40.0

82,4

41.7

2,425

.1-16

.6-0.

7-0.

0539

Trans

porta

tion E

qp.

870.0

1,083

.521

3.524

.50.8

451

2.338

3.8-12

8.5-25

.1-0.

7435

7.769

9.734

2.095

.64.1

31,5

36.2

1,609

.773

.54.8

0.12

666.2

526.2

-140.0

-21.0

-0.39

40Ele

c tron

ic s3,9

05.8

4,103

.219

7.45.1

0.77

2,983

.62,8

44.0

-139.6

-4.7

-0.81

922.2

1,259

.233

7.036

.54.0

76,6

39.7

6,788

.714

9.02.2

0.24

2,733

.92,6

85.5

-48.4

-1.8

-0.13

41 M

achin

ery Eq

pt.3,8

73.8

4,265

.139

1.310

.11.5

32,5

30.7

2,366

.1-16

4.6-6.

5-0.

951,3

43.1

1,899

.055

5.941

.46.7

210

,595.4

10,78

6.619

1.21.8

0.31

6,721

.66,5

21.5

-200.1

-3.0

-0.55

42Ind

ustry

Nec

393.1

512.9

119.8

30.5

0.47

217.4

173.6

-43.8

-20.1

-0.25

175.7

339.3

163.6

93.1

1.98

667.7

739.9

72.2

10.8

0.12

274.6

227.0

-47.6

-17.3

-0.13

Total

25.53

0.827

,972.1

2,441

.39.6

9.617

,259.1

16,35

7.5-90

1.6-5.

2-5.

28,2

71.7

11,61

4.63,3

42.9

40.4

40.4

61,71

4.463

,280.7

1,566

.32.5

2.536

,183.6

35,30

8.6-87

5.0-2.

4-2.

4

Ap

pen

dix

2C

OM

PAR

ATI

ON

OF

IMPA

CT

OF

AC

TFA

ON

IND

ON

ESIA

(C

OM

MO

DIT

IES)

- (

iv)

Expo

rt to

ACF

TAEx

port

to A

SEAN

Expo

rt to

CHI

NAEx

port

to W

ORLD

Expo

rt to

ROW


APPENDIX 3TABEL OF DATA AGREGATION OF14 REGIONS DAN 42 SECTORS

1 Japan Japan2 Korea Korea3 Cina Cina4 India India5 Indonesia Indonesia6 Malaysia Malaysia7 Singapore Singapore8 Thailand Thailand9 Philippines Philippines

10 Vietnam Vietnam11 Other ASEAN Cambodia, Lao PDR, Myanmar, Brunei Darussalam12 USA USA13 EU 25 Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland,

France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania,Luxemborg, Malta, Netherlands, Poland, Portugal, Slovakia, Slovenia,Spain, Sweden, UK

14 Rest of The World Australia, New Zealand, Rest of Oceania, Hongkong, Taiwan, Rest ofEast Asia, Bangladesh, Pakistan, Sri Lanka, Rest of South Asia, Canada,Mexico, Rest of North America, Argentina, Bolivia, Brazil, Chile,Colombia, Ecuador, Paraguay, Peru, Uruguay, Venezuela, Rest ofSouth America, Costa Rica, Guatemala, Nicaragua, Panama, Rest ofCentral America, Caribbean, Switzerland, Norway, Rest of EFTA,Albania, Bulgaria, Belarus, Croatia, Romania, Russian Federation,Ukraine, Rest of Eastern Europe, Rest of Europe, Kazakhstan,Kyrgyztan, Rest of Former Soviet Union, Armenia, Azerbaijan, Georgia,Iran, Turkey, Rest of Western Asia, Egypt, Morocco, Tunisia, Rest ofNorth Africa, Nigeria, Senegal, Rest of Western Africa, Central Africa,South Central Africa, Ethiopia, Madagascar, Malawi, Mauritius,Mozambique, Tanzania, Uganda, Zambia, Zimbabwe, Rest of EasternAfrica, Bostwana, South Africa, Rest of South African Customs

No. Regional Aggregation Member

I. Region Aggregation


1 PDR Paddy rice2 WHT Wheat3 GRO Cereal grains nec4 V_F Vegetables, fruit, nuts5 OSD Oil seeds6 C_B Sugar cane, sugar beet7 PFB Plant-based fibers8 OCR Crops nec9 CTL Cattle, sheep, goats, horses

10 OAP Animal products nec11 RMK Raw milk12 WOL Wool, silk-worm cocoons13 FRS Forestry14 FSH Fishing15 COA Coal16 OIL Oil17 GAS Gas18 OMN Minerals nec19 CMT Meat: cattle, sheep, goats, horse20 OMT Meat products nec21 VOL Vegetable oils and fats22 MIL Dairy products23 PCR Processed rice24 SGR Sugar25 OFD Food products nec26 B_T Beverages and tobacco products27 TEX Textiles28 WAP Wearing apparel29 LEA Leather products30 LUM Wood products31 PPP Paper products, publishing32 P_C Petroleum, coal products33 CRP Chemical,rubber,plastic prods34 NMM Mineral products nec35 I_S Ferrous metals36 NFM Metals nec37 FMP Metal products38 MVH Motor vehicles and parts39 OTN Transport equipment nec40 ELE Electronic equipment41 OME Machinery and equipment nec42 OMF Manufactures nec43 OTHERS Electricity; Gas manufacture, distribution; Water; Construction; Trade;

Transport nec; Sea transport; Air transport; Communication; Financialservices nec; Insurance; Business services nec; Recreation and otherservices; Public Administration/Defence/Health/Education; Dwellings

No. Sectoral Aggregation Member

II. Sector Aggregation


APPENDIX 4CONVERTION TABLE FOR 7 COMMODITIES SECTOR

Agricultural Product Paddy rice, wheat, cereal grains nec, vegetable, fruit, nuts, oil seeds, sugar cane, sugarbeet, plant-based, crops nec, bovine cattle, sheep and goats, horse, animal product,raw milk, wool silk-worm cocoons, bovine cattle, sheep and goats, horse meat product

Food Product Meat product nec, vegetable oil and fats, dairy products, processed rice, sugar, foodproducts nec, beverages and tobacco products

Extractive Industry Forestry, fishing, coal, oil, gas, minerals nec, petroleum, coal productsLight Manufacturing Textiles, wearing apparel, leather product, wood productsHeavy Paper products, publishing, chemical, rubber, plastic products, mineral products nec,

ferrous metals, metalsManufacturing necTechnology-intensive Metal products, motor vehicle and parts, transport equipment nec,Manufacturing electronic, machinery and equipment nec, manufacturing necServices Electronic, gas manufacturing, distribustion, water, construction trade, transport,

financial, business, recreational services, public administrayion and defense, education,health, dwellings and services

Sector Commodities

Source : ADB, WP No 130, 2008nec : not elsewhere classified


APPENDIX 5TABLE MAP OF RCA AND IIT QUADRANTS (TOTAL)

I 9 33 Chemical 1.11 0.85 1,644,317,782 11.62%

16 Oil 2.26 0.82 1,382,885,185 9.77%

27 Textils 1.62 0.64 588,106,204 4.16%

38 Motor Vehicles 1.12 0.85 332,659,537 2.35%

35 Metals 1.04 0.88 251,830,711 1.78%

34 Mining products 1.48 0.69 196,203,561 1.39%

26 Beverages 1.30 0.82 177,537,037 1.25%

11 Pure Milk 1.11 0.73 30,045,361 0.21%

22 Dairy products 1.09 0.52 534,984 0.00%

Total 4,604,120,362 32.54%

Quadrant Number Code Classification of Main Commodities RCA IIT Export Value Export Share

Period I (Average 2001-2004)

II 8 41 Machinery and equipment 0.42 0.83 1,986,833,043 14.04%

37 Metal products 0.77 0.87 163,943,461 1.16%

4 Vegetables 0.87 0.77 119,663,788 0.85%

25 Food products 0.98 0.89 111,614,849 0.79%

29 Leather products 0.89 0.88 71,961,181 0.51%

20 Meat products 0.68 0.60 36,946,777 0.26%

5 Oil Seeds 0.94 0.51 11,503,016 0.08%

19 Meat 0.20 0.68 80,856 0.00%

Total 2,502,546,970 17.69%


III 14 40 Electronics 0.73 0.30 1,840,619,562 13.01%32 Petroleum 0.49 0.31 414,346,141 2.93%42 Industry Nec 0.64 0.48 228,466,798 1.61%28 Apparel 0.79 0.22 125,014,619 0.88%39 Transport equipment 0.56 0.34 56,274,523 0.40%3 Cereal Grain 0.22 0.09 3,990,231 0.03%24 Sugar 0.05 0.03 2,398,110 0.02%2 Wheat 0.78 0.45 2,288,071 0.02%10 Animal products 0.18 0.39 1,437,850 0.01%23 Processed Rice 0.01 0.01 503,919 0.00%1 Rice 0.00 0.00 0 0.00%6 Sugar Cane 0.00 0.00 0 0.00%7 Fiber 0.00 0.00 0 0.00%12 Wool 0.00 0.00 0 0.00%

Total 2,675,339,823 18.91%





IV 11 8 Crops Nec 2.60 0.15 510,303,657 3.61%9 Livestock 2.15 0.14 29,072,147 0.21%13 Forestry 4.10 0.04 3,619,860 0.03%14 Fishing 1.98 0.24 179,706,122 1.27%15 Coal 7.00 0.07 344,450,420 2.43%17 Gas 1.57 0.14 148,201,956 1.05%18 Minerals Nec 5.59 0.34 360,869,596 2.55%21 Vegetable Oil 3.82 0.06 683,396,677 4.83%30 Wood products 3.19 0.12 493,267,281 3.49%31 Paper Products 4.57 0.27 916,030,861 6.47%36 Metal Nec 2.79 0.33 697,866,399 4.93%

Total 4,366,784,974 30.86%



Note : Calculation is based on SITC data conversion from UNCOMTRADE to 42 tradable commodities in GTAP

I 9 16 Oil 2.45 0.82 2,716,031,454 9.51%

38 Motor Vicle 1.23 0.74 859,808,533 3.01%

27 Textiles 1.01 0.74 684,776,101 2.40%

30 Wood Products 1.68 0.64 410,094,630 1.44%

26 Beverages 1.31 0.79 261,927,830 0.92%

25 Food Products 1.05 0.96 224,759,805 0.79%

29 Leather Products 1.16 0.82 179,757,549 0.63%

5 Oil Seeds 2.36 0.62 27,301,185 0.10%

22 Dairy Products 1.47 0.83 1,913,165 0.01%

Total 5,366,370,249 18.78%


Period II (Average 2005-2008)

II 11 41 Machine Equipment 0.42 0.82 3,459,339,492 12.11%

33 Chemical 0.87 0.71 2,554,706,472 8.94%

40 Electronics 0.48 0.61 2,072,101,828 7.25%

35 Iron Metal 0.69 0.58 685,974,318 2.40%

42 Industry Nec 0.47 0.56 427,122,284 1.49%

39 Transport Equipment 0.81 0.71 379,215,365 1.33%

37 Metal Products 0.55 0.66 285,388,289 1.00%

4 Vegetable 0.82 0.64 214,127,650 0.75%

34 Mining Product 0.90 0.86 207,486,926 0.73%

28 Apparel 0.46 0.67 151,515,810 0.53%

10 Animal Product 0.28 0.58 2,958,244 0.01%

Total 10,439,936,678 36.54%




III 10 32 Petroleum 0.38 0.19 935,618,846 3.27%

11 Pure Milk 0.67 0.33 25,279,380 0.09%

24 Sugar 0.27 0.11 17,854,983 0.06%

3 Cereal Grain 0.55 0.40 8,389,705 0.03%

23 Processed rice 0.00 0.01 330,037 0.00%

19 Meat 0.01 0.20 7,325 0.00%

1 Rice 0.00 0.00 0 0.00%

6 Sugar Cane 0.00 0.00 0 0.00%

7 Fiber 0.00 0.00 0 0.00%

12 Wool 0.00 0.00 0 0.00%

Total 987,480,275 3.46%



IV 12 36 Metal Nec 3.77 0.29 2,482.296,551 8.69%

21 Vegetable Oil 5.11 0.03 2,156.452,594 7.55%

17 Gas 6.78 0.05 1,846.798,666 6.46%

8 Crops Nec 2.91 0.13 1,498.317,400 5.24%

31 Paper Products 4.39 0.37 1,442.952,860 5.05%

15 Coal 7.08 0.05 1,170.145,644 4.10%

18 Mineral Nec 5.39 0.35 709,176,692 2.48%

14 Fishery 1.89 0.25 229,429,381 0.80%

20 Meat Products 2.12 0.37 203,014,679 0.71%

9 Livestock 1.63 0.06 27,159,304 0.10%

2 Wheat 1.45 0.32 6,619,736 0.02%

13 Forestry 5.36 0.12 5,956,443 0.02%

Total 11,778,319,950 41.22%



Note : Calculation is based on SITC data conversion from UNCOMTRADE to 42 tradable commodities in GTAP


I 10 33 Chemical 1.18 0.85 1,644,317,782 13.03%27 Textiles 1.72 0.64 588,106,204 4.66%38 Motor vehicle 1.19 0.85 332,659,537 2.64%35 Iron Metal 1.11 0.88 251,830,711 2.00%34 Mining Products 1.57 0.69 196,203,561 1.55%26 Beverages 1.39 0.82 177,537,037 1.41%25 Food Products 1.04 0.89 111,614,849 0.88%11 Pure Milk 1.18 0.73 30,045,361 0.24%5 Oil Seeds 1.00 0.51 11,503,016 0.09%22 Dairy Products 1.16 0.52 534,984 0.00%

Total 3,343,818,057 26.50%



APPENDIX 6TABLE MAP OF RCA AND IIT QUADRANTS (NON OIL AND GAS)

II 6 41 Machine Equipment 0.45 0.83 1,986,833,043 15.75%

37 Metal Products 0.82 0.87 163,943,461 1.30%

4 Vegetable 0.93 0.77 119,663,788 0.95%


20 Meat Products 0.73 0.60 36,946,777 0.29%

19 Meat 0.21 0.68 80,856 0.00%

Total 2,379,429,106 18.86%



III 14 40 Electronics 0.77 0.30 1,840,619,562 14.59%32 Petroleum 0.52 0.31 414,346,141 3.28%42 Industry Nec 0.69 0.48 228,466,798 1.81%28 Apparel 0.85 0.22 125,014,619 0.99%39 Transport Equipment 0.59 0.34 56,274,523 0.45%3 Cereal Grain 0.24 0.09 3,990,231 0.03%24 Sugar 0.05 0.03 2,398,110 0.02%2 Wheat 0.83 0.45 2,288,071 0.02%10 Animal Products 0.19 0.39 1,437,850 0.01%23 Processed Rice 0.01 0.01 503,919 0.00%1 Rice 0.00 0.00 0 0.00%6 Sugar Cane 0.00 0.00 0 0.00%7 Fiber 0.00 0.00 0 0.00%12 Wool 0.00 0.00 0 0.00%

Total 2,675,339,823 21.20%




IV 10 31 Paper Products 4.87 0.27 916,030,861 7.26%36 Metal Nec 2.98 0.33 697,866,399 5.53%21 Vegetable oil 4.07 0.06 683,396,677 5.42%8 Crops Nec 2.77 0.15 510,303,657 4.04%30 Wood Products 3.39 0.12 493,267,281 3.91%18 Mineral Nec 5.96 0.34 360,869,596 2.86%15 Coal 7.45 0.07 344,450,420 2.73%14 Fishery 2.11 0.24 179,706,122 1.42%9 Livestock 2.29 0.14 29,072,147 0.23%13 Forestry 4.37 0.04 3,619,860 0.03%

Total 4,218,583,018 33.43%



Note: Calculation is based on SITC data conversion (non oil and gas) from UNCOMTRADE to 42 tradable commodities in GTAP

I 9 38 Motor vehicle 1.39 0.74 859,808,533 3.58%

27 Textiles 1.14 0.74 684,776,101 2.85%

30 Wood Products 1.89 0.64 410,094,630 1.71%

26 Beverages 1.49 0.79 261,927,830 1.09%

25 Food Products 1.18 0.96 224,759,805 0.94%

34 Mining Products 1.02 0.86 207,486,926 0.86%


5 Oil Seeds 2.65 0.62 27,301,185 0.11%

22 Dairy Products 1.67 0.83 1,913,165 0.01%

Total 2.857.825.721 11.90%



II 10 41 Machinery Eqpt. 0.47 0.82 3,459,339,492 14.41%

33 Chemical 0.98 0.71 2,554,706,472 10.64%

40 Elektronics 0.54 0.61 2,072,101,828 8.63%

35 Iron Metal 0.78 0.58 685,974,318 2.86%

42 IndustryNec 0.53 0.56 427,122,284 1.78%

39 Transport Eqpt. 0.92 0.71 379,215,365 1.58%

37 Metal Products 0.62 0.66 285,388,289 1.19%

4 Beverages 0.92 0.64 214,127,650 0.89%

28 Apparel 0.52 0.67 151,515,810 0.63%

10 Animal Products 0.32 0.58 2,958,244 0.01%

Total 10,232,449,752 42.62%




III 10 32 Petroleum 0.43 0.19 935,618,846 3.90%

11 Pure Milk 0.76 0.33 25,279,380 0.11%

24 Sugar 0.31 0.11 17,854,983 0.07%

3 Cereal Grain 0.62 0.40 8,389,705 0.03%

23 Processed Rice 0.00 0.01 330,037 0.00%

19 Meat 0.01 0.20 7,325 0.00%

1 Rice 0.00 0.00 0 0.00%

6 Sugar Cane 0.00 0.00 0 0.00%

7 Fiber 0.00 0.00 0 0.00%

12 Wool 0.00 0.00 0 0.00%

Total 987,480,275 4.11%



IV 11 36 Metal Nec 4.26 0.29 2,482,296,551 10.34%

21 Oil Vegetable 5.78 0.03 2,156,452,594 8.98%

8 Crops Nec 3.29 0.13 1,498,317,400 6.24%

31 Paper Products 4.96 0.37 1,442,952,860 6.01%

15 Coal 8.01 0.05 1,170,145,644 4.87%

18 Mineral Nec 6.09 0.35 709,176,692 2.95%

14 Fishery 2.14 0.25 229,429,381 0.96%

20 Meat Products 2.40 0.37 203,014,679 0.85%

9 Livestock 1.84 0.06 27,159,304 0.11%

2 Wheat 1.66 0.32 6,619,736 0.03%

13 Forestry 6.07 0.12 5,956,443 0.02%

Total 9,931,521,284 41.37%



Note: Calculation is based on SITC data conversion (non oil and gas) from UNCOMTRADE to 42 tradable commodities in GTAP


This page is intentionally left blank

75Is ACFTA Proper Strategy of Sustainable Poverty Alleviation?: Proof From The Depletion of Saving Rate

The outcome of Regional Free Trade Area (R-FTA) still remains a conundrum. Regional free trade

area (R-FTA) is one of the manifestations of the economy integration phenomenon. R-FTA brings many

pros and cons to the economists. It allows better allocation of resources especially by eliminating tariffs,

thus making people have higher purchasing power for goods. While the increase of purchasing power is

good for growth engine and poverty alleviation progress, this paper proves that there is potency for the

agreement to be detrimental in the long run.

The main focus in this paper is the potential impact of ACFTA to the saving rate as the shock buffer

for the poor in time of recessions and crises, where purchasing power decreases significantly. We view

the ACFTA impact through the series of net import, defined as the difference between imports from

export. We use Dynamic Panel Data (DPD) to estimate the impact of net import to the saving rate, assuming

that there is a dynamic relationship between saving rate and its lagged value. The estimation result proves

that there is a negative relationship between import and the saving per capita, which indicates the

consumptive behavior of ASEAN people under high import. Moreover, the dynamic relationship shows

that saving per capita is not persistent, meaning that the saving rate will be decreased gradually.

Therefore, we can expect that in the long rung, the savings will be depleted into nothing if we

keep letting the import flooded domestic market without imposing any pre-emptive and reactive policies.

This paper provides a set of historical estimation of the potential impact of ACFTA on saving rate and its

policy implication to endure the impact.

Keywords: : : : : Free Trade, Poverty Alleviation, Saving BehaviorFree Trade, Poverty Alleviation, Saving BehaviorFree Trade, Poverty Alleviation, Saving BehaviorFree Trade, Poverty Alleviation, Saving BehaviorFree Trade, Poverty Alleviation, Saving Behavior

JEL Classification Code: : : : : E38, F15E38, F15E38, F15E38, F15E38, F15

1 Bagus Arya Wirapati is bachelor graduate from Faculty of Economics University of Indonesia and currently serving as Pengajar Mudain Gerakan Indonesia Mengajar. [email protected]

2 Niken A.S. Kusumawardhani is bachelor graduate from Faculty of Economics University of Indonesia and currently a Master Studentat Institut D»Etudes Politiques (Sciences Po) Paris. [email protected]

IS ACFTA A PROPER STRATEGYOF SUSTAINABLE POVERTY ALLEVIATION?:

PROOF FROM THE DEPLETION OF SAVING RATE

Bagus Arya Wirapati 1 danNiken Astria Sakina Kusumawardhani 2 *****

Abstract


I. INTRODUCTION

According to Mid-Term National Development Plan (Rencana Pembangunan Jangka

Menengah/RPJM) 2010-2014, the government of Indonesia has targeted economic growth

rates of 5.5% in 2010, 7% in 2012, and above 7% in 2014. Meanwhile, in the National Long-

Term Development Plan (Rencana Pembangunan Jangka Panjang/RPJP) 2005-2025, the

government has targeted to achieve the prosperity of the nation at an equal level with other

middle-income countries and to maintain open unemployment rate and poverty rate of less

than 5%.

Government»s targets and efforts above are determined in order to face the free trade

agreement between Indonesia and other countries. Indonesia has many multilateral or bilateral

free trade agreements with other countries, including South Korea (2007), Japan (2007), Australia

and New Zealand (2009), India (2009), and China (2010). Those free trade agreements may

bring opportunities and threats to Indonesia economy.

The agreement of ASEAN-China Free Trade Area (ACFTA) reduced tariffs of 90 percent of

imported goods to zero. ASEAN countries, especially the developing ones (note that Singapore

is considered as developed country), will be flooded by flow of goods under ACFTA. Increase of

access to great quantity of low-price goods, in term of expenditure, would be very beneficial

for the poor. Todaro and Smith (2008, [59]) argued that an increase of the poor»s access to the

goods and services is one proof of the poverty alleviation progresses. It would increase the

fulfillment of the poor»s primary and secondary needs. Therefore, based on expenditure point

of view, the number of poverty will decrease due to the increase of the poor»s ability to access

goods under this free trade agreement.

It is indeed would reduce poverty level but the sustainability of this poverty alleviation still

remains as a conundrum. Since the poor has greater marginal propensity to consume than the

have, the poor will likely choose to consume more; consequently, reducing the proportion of

savings from their income. They tend to increase the consumption rather than savings for

future buffer against economic shocks and instability. This behavior will lead them to lower

level of resilience against the economic downturn. Therefore, imposing Regional Free Trade

Area, in this case ACFTA, to increase the availability low-price goods is hypothesized to be an

improper strategy for sustainable poverty alleviation, especially in the long run.

This paper aims to answer the main question of whether ACFTA is a proper strategy of

sustainable poverty alleviation. To answer such question, main goal of this paper is to get an

empirical result of relationship between net import and savings rate as a proxy of the country»s

poverty rate. The paper will be organized in following manner: chapter 2 describes about


ACFTA. Chapter 3 presents literature review and conceptual framework of the model used in

this research. Chapter 4 explains about research methodology, while chapter 5 includes analysis

and discussion of empirical result. Finally, the summary and policy recommendation will be

presented in chapter 6.

II. ASEAN-CHINA FREE TRADE AGREEMENT (ACFTA)

Regional Free Trade Area (R-FTA) is one of the manifestations of the economy integration

phenomenon. R-FTA brings many pros and cons to the economists. It allows better allocation of

resources especially by eliminating tariffs, thus making people have higher purchasing power for

goods. ASEAN-China Free Trade Area (ACFTA) is implemented by eliminating or reducing barriers

to trade in goods (both tariff and non tariff), improving access to service market and also investment

rules and regulations, as well as improvement of economic cooperation in order to improve the

welfare of ASEAN and China community. ACFTA brings various fortunes for ASEAN countries, as

well as its misfortunes. Government of Indonesia hopes that ACFTA will bring future favorable

implications, such as wider opportunity for Indonesia to enter China markets by means of relatively

low tariff and large population, increased cooperation between businessmen in both countries

through the establishment of strategic alliances, increased purchasing power of China goods

due to reduced tariffs or costs, and improved possibility of transfer of technology between

business people in both countries. Whether the expectations will turn into reality or not, it still

takes many years to come to finally see the actual impact of ACFTA.

Chinese Premier Zhu Rongji originated the idea of a free trade area between China and

ASEAN at the China-ASEAN Summit, November 2000. In October 2001, a group of economic

experts from China and ASEAN issued a recommendation for establishment of ASEAN-China

within ten years in the future. A month later, in November 2001, during another China-ASEAN

Summit, the leaders from respected countries started to negotiate the possibility for such an

idea. A year later, the ASEAN leaders and Chinese Premier Zhu Rongji signed the ACFTA

Framework Agreement. This agreement served as a roadmap for the establishment of the free

trade area between China and ASEAN. The agreement stated that the free trade area should be

completed by 2010, considering that four ASEAN members are expected to join the network by

2015. The ACFTA Framework Agreement is a groundbreaking document, for while individual

ASEAN members had previously created free trade agreements, ASEAN as an organization had

never before made such a bond with an outside nation. Moreover, the ACFTA Framework

Agreement was China»s first free trade agreement with a foreign nation. Since the ACFTA

Framework Agreement, both China and ASEAN have entered into negotiations with other

countries regarding free trade agreements.


According to ACFTA agreement, tariff elimination should be done gradually. The steps

are Early Harvest Program (EHP), Normal Track I and II, and Sensitive/Highly Sensitive List. Each

step is scheduled between each ASEAN countries and China bilaterally, which means that each

country decides its own schedule for tariff reduction or elimination for each category of product.

Since November 2002, ASEAN 6 (Indonesia, Singapore, Thailand, Malaysia, Philippine, Brunei)

and China have agreed to sign ACFTA, for 0% entry tariff per January 2004 exclusively for

products categorized as EHP. Beginning from 2004, each year Indonesia reduced tariff for

imported products from China. During 2004-2009, around 65% Chinese products have been

identified as free-entry products from Dirjen Bea Cukai, Indonesia Ministry of Finance. In January

2010, around 1598 or 18% products form China received reduction of 5% tariff while 82% of

total 8.738 import products from China have been completely excluded from tariff charge. On

the contrary, during 2004-2009, balance of trade between Indonesia and China showed that

Indonesia imports more products from China rather than exports. Therefore, during 2003-

2009, Indonesia has accumulatively a trade deficit (on non-oil trading) with China as of USD

12.6 million (around 120 trillion Rupiah). Compared to other ASEAN countries, Singapore is

the biggest exporter to China, while Indonesia is at the 5th ranking right after Thailand. The

biggest deficit of trade between Indonesia and China is around USD 7.2 million in 2008.

Indonesia»s participation in various agreements of free trade agreements can not be

prevented or reversed, although the manufacturing sector expressed its reluctance due to fear

of competition. However, typically in the agreement of free trade, there are clauses that provide

opportunity for involved parties to modify and ability to temporary suspense the concessions in

order to improve its competitiveness or strength. To protect the manufacturing sector from the

invasion of import products, government should enacted cross-ministries coordination that

involves representatives from real sector and related associations.

Since the establishment of ACFTA this January 2010, negative reactions from the

associations of real sector players have been heard. Most of them stated that they are not ready

yet to compete with China, and they asked for the government to postpone the implementation

of ACFTA agreement. Especially for the case of ACFTA and Common Effective Preferential

Tariff-ASEAN Free Trade Agreement (CEPT-AFTA), Indonesia still agree to reduce the tariff

according to schedule, where products categorized as Normal Track (NT1) ACFTA and Inclusion

List (IL) CEPT-AFTA for ASEAN planned to have 0% of entry tariff beginning January 1, 2010.

The Minister of Trade has postponed the elimination of entry tariff for some products due to

unpreparedness of some domestic sectors. At the moment being, Indonesia is in the process of

postponing tariff cut in 227 categories of product.


III. REVIEW LITERATURE

III.1. The Role of Saving For an Economy

Savings plays an important role for an economy and each type of savings plays different

important roles. Savings are done by three entities in an economy: households, companies, and

government. Households save to cover expenses of their children and for future buffer during

retirement period. Companies retain a part of their profit as retained earning for future investment

to expand their businesses. On the other hand, government saves if the tax revenue exceeds

the government expenditure. Government saves to build public facilities or infrastructures such

as hospitals, bridges, or harbors. Lack of savings by each entity leads to different impact.

Households may have to struggle to fund their big expenses, so they have to make big loans to

banks for school expenses. If companies save too little, for example they disburse all their

income for shareholders in form of dividends; they may find it hard to fund the expansion of

their branches. Therefore, the companies lose their potential to grow. Government who saves

too little also will not be able to build physical infrastructure, and it is going to affect the

economy of the country as a whole. Foreign investors would not prefer to invest in the country

due to lack of infrastructure, and domestically, less development by government means higher

rate of unemployment and sub-optimal economic growth.

In order to possess high level of national income or prosperity, firstly a country must

possess high level of productivity. Determinants of productivity are working capitals such as

physical capital, human capital, natural resources, and technological knowledge. The more

working capitals a country has, the faster it grows compared to the others. Possession of

working capitals determines the level of productivity that a country may achieve. It»s clear to

see that one way to improve one country»s productivity is to invest its resources in form of

working capitals. The endogenous growth theories since the mid 1980s by Romer (1986,

1990), Lucas (1988), and Barro (1990) in Mikesell and Zinser (1973, [41]) confirmed the view

that the accumulation of physical capital is the critical driver of long-run economic growth.

Investment in working capital should be translated as increased saving rate of the country

itself. It»s because more usage of resources today to produce working capitals means reducing

resources available to for consumption at the time being. Reduced consumption means

increased saving. Therefore, we can conclude that more saving allows better investment in

working capitals and productivity, which in the future will lead to higher level of national

income. Development economists regard saving rate as a key performance indicator, and it is

labeled as a primary condition for achieving a satisfactory rate of economic growth (Mikesell

and Zinser, 1973, [41]).


A classic view of the macro-economic dynamics of the growth process was that increasing

savings when transformed into productive investment would help achieve an economic growth

(Harrod, 1939; Domar, 1946; Lewis, 1954; Solow, 1956 in AlFoul (2010, [1])). These studies

provide empirical support for hypotheses that savings growth promotes economic growth. The

conventional perception is that savings contribute to higher investment and hence higher GDP

growth in the short run (Japelli and Pagano, 1994, [32]). Finally, a study by AlFoul (2010, [1])

confirmed that during period of 1965-2007 in Morocco, a long-run two-way relationship

between real GDP and real gross domestic saving (GDS) is proved to be exist; while in the same

period in Tunisia, the results reveal that saving stimulates growth, not the other way around.

Supported by previous studies, we believe that higher savings would lead to higher growth

rate. Saving itself is defined as the result of income deducted by consumption, or can be

expressed by S = Y √ T √ C, where S = saving, Y = income, T = tax, and C = consumption.

Figure IV.1.Consumption Function

Source: Azzopardi (2004, [4])

Consumption Consumption = Disposable Income

Negative Saving

Positive Saving

Consumption FunctionC = a + c (Y-T)

a 45 degree

Disposable Income

The consumption function in the Figure IV.1 above states that consumption equals a

fixed amount of «a» plus a fraction «c» of disposable income (Y-T). A household has positive

saving when its disposable income exceeds its consumption, and it has negative saving when

its consumption exceeds its disposable income. Priorities of consumption of each household

may differ one another, but generally the basic necessities will be on the top of consumption

list. For example, during economic crisis and income falls very low, households take out their

money from savings to buy basic necessities of their life. Keynes concluded in his book, ≈The

General Theory of Employment, Interest, and Money∆, that savings depends on disposable

income. The conventional wisdom is that rich people save larger fraction of their disposable

income compared to poor people. Poor people have less disposable income, and generally they

spend all of their income for their needs, making them have no chance to save. Therefore, we


assume that the poor has lower marginal propensity to save compared to the rich. When poor

people begin to save or to save more than they used to, it»s a signal that their wealth is improving.

II.2 Determinants of Saving Rate

Savings has been considered a critical macro-economic variable with micro-economic

foundation for achieving price stability and promoting employment opportunities thereby

contributing to sustainable economic growth (Mishra et al., 2010, [42]). As Keynes said that

saving depends on disposable income, we should criticize whether there is a dynamic relationship

between saving rate and its lagged value. People do not get higher disposable income all of a

sudden, there»s a process that people usually go through to earn high level of income. Since

previous period»s disposable income usually relates to disposable income of the next period, the

same should apply for saving rate. Higgins and Williamson (1996, [23]) estimated the relationship

for 16 Asian countries from 1950 to 1992, using IMF data on savings rates, and Penn World

Table (PWT) data on income and prices, and demographic data from United Nations database.

Higgins and Williamson (1996, [23]) used lagged value of savings, dependency ratio, annual

growth in real GDP, and relative price of investment good which encouraged saving as explanatory

variables for savings (Schultz, 2004, [54]). This equation becomes unique because it assumes

there is a dynamic relationship between saving rate (Sti) and its lagged value (St-1). Schultz

(2004, [54]) contended that saving rate is expected to change gradually over time to new

condition, and a year is not an adequate time for saving rate to achieve its new condition.

Saving rate should adapt in more than a year period, adjusting to individual»s level of disposable

income. Since we assume that saving rate of period t has a relationship with saving rate at

period t-1, it implies that whatever errors are present in the savings equation in one year will

not be independent of the error in savings in the prior or following years (Schultz, 2004, [54]).

This dynamic relationship of saving and its lagged value shows that lagged value of saving

should be included as one of the determinants of saving rate as a dependent variable.

Government saves if its revenue from taxes exceeds its spending. The summary of

government activities of spending and receiving tax revenue can be seen in its budget balance.

According to Keynesian open-economy model, there is a positive association between budget

balance and trade balance. In Keynesian open-economy model, budget deficit may lead to

trade deficit. The higher budget deficits put upward pressures on interest rates, where higher

interest rates would raise the foreign exchange value of the currency, and the stronger currency

would in turn reduce net exports, in other words, trade deficit. However, this conventional

view of the twin deficits has not gained much empirical support. Evans (1985, 1986) in Darrat

(1988, [12]) has found no reliable relationship for the US between budget deficits on the one


hand and either interest rates or exchange rates on the other. The empirical evidence is somewhat

less ambiguous and suggests that trade deficits in the US are inversely related to the exchange

value of the dollar, though the response is both small and sluggish. Proponents of this

conventional view found partial relationship between higher budget deficits and higher interest

rates (Plosser (1982, [46]), Hoelscher (1983, [25]), Cebula (1987, [10]), and Wachtel and Young

(1987, [60]). The other proponents such as Feldstein (1982) in Islam (1998, [30]) concluded

that larger budget deficits result in higher interest rates, which causes the appreciation of

exchange rate, thereby worsening the trade imbalance.

Different empirical results of the relationship between the twin deficits attracted more

research in respected topic. Another hypotheses being developed were (1) trade deficits because

budget deficits, (2) the two deficits are causally independent, and (3) the two deficits have

bidirectional causality. Over the 3 hypotheses, the hypothesis of bidirectional relationship between

budget deficit and trade deficit gained much empirical support. Islam (1998, [30]) examined

the direction of causality between budget deficits and trade deficits based on Granger test for

Brazil during 1973:1Q through 1991:4Q. Based on Granger»s causality test, Islam (1998, [30])

concluded that there is a bilateral causality between trade and budget imbalances. Another

empirical result presented by Darrat (1988, [12]) also concluded that there is a mutual causality

relationship between budget and trade deficit. Darrat (1988, [12]) hypothesized that not only

budget deficit causes trade deficit, but trade deficit may also cause budget deficit. According

to Darrat (1988, [12]), when a country»s level of net export fell off (caused by other factors than

the budget deficit), the pressure on the government would be increased. Decrease in the level

of net export would harm domestic industries, leading to higher unemployment rate and loss

of foreign market shares. This situation would in turn decrease the revenue of the government

from tax, since business activities in the export sector were depressed. The government also

would spend more to stimulate the depressed sector or to give aid to harmed domestic industries.

The empirical results of Darrat (1988, [12]) only partially support the conventional view that

budget deficits caused trade deficit, but strongly support the causality between trade deficits

to budget deficits. The empirical result of Darrat (1988, [12]) and Islam (1998, [30]) supported

the view that trade deficit has bidirectional causality with budget deficit.

The Keynesian revolution based on under-employment equilibrium made saving a function

of income and income a function of investment, as opposed to the Neoclassical view of saving

as a determinant of investment (Mikesell and Zinser, 1973, [41]). Empirical tests of saving-

income relationship have been conducted in two big groups: Keynesian or non-Keynesian

hypotheses. Kuznets (1960, [25]) in Mikesell and Zinser (1973, [41]) was among the first to do

a cross-sectional study between per capita income and saving. Kuznets (1960, [25]) achieved a


conclusion that there was a tendency for countries with high capita income to have higher

saving ratios, but the tendency was not very consistent. Singh (1971) in Mikesell and Zinser

(1973, [41]) as a proponent of the Keynesian also concluded that when per capita GNP rose

from $100 to $1000 the gross saving ratio increased by 8 percentage points. Singh (1971) also

found that at a per capita GNP growth rate of 2 percent, it would take 50 years to increase the

saving rate by 3 percentage points. On the other hand, the proponent of non-Keynesian

hypotheses came up with a very theory of saving behavior. Dusenberry (1949, [15]), Friedman

(1957, [17]), Modigliani et al. (1954, [43]) concluded a rise in per capita income would not

merely lead to a higher savings ratio. One of the studies, conducted by Friedman (1957, [17]),

resulted in a new hypothesis called ≈Permanent Income Hypothesis (PIH)∆. Friedman»s hypothesis

is that people consume permanent income, and all of the transitory income (difference between

actual income and permanent income) will be allocated to saving. This implies a heavy reliance

on past behavior as a determinant of consumption spending; but changes in transitory income

will immediately lead to changes in the level of saving.

Classic analyses on savings and growth have focused on two main issues: (1) the effect of

higher savings on long run growth, and (2) the impact of higher savings on investment.

Neoclassical models inspired by Solow (1956, [57]) suggested that an increase in saving ratios

generates higher growth only in the short run, during the transition between steady states

(Edwards, 1995, [16]). More recent studies by Romer (1986, [50]) predicted that higher savings

(and the related increase in capital accumulation) might lead to permanent increase in growth

rates. Proponents of this conventional perception conclude that savings contribute to higher

investment and also higher GDP growth in the short run (noted that the catching up effect and

the law of diminishing return are exist). That»s why according to Quah (1993) in Edwards (1995,

[16]) middle-income countries are slowly vanishing. As the countries are in transition to achieve

similar steady state as high-income countries, this assumption provides a basis for researchers

to study the direction of causality between growth rate and saving rate. Mohan (2006, [45])

studied about the direction of causality between growth rate and saving rate using the concept

of Granger causality. His study is supported by previous studies that revealed that higher level

of growth rate led to higher level of saving (Caroll and Weil (1994, [9]), Sinha (1996, [55]), Saltz

(1999, [52], and Anoruo and Ahmad (2001, [2]). Caroll and Weil (1994, [9]) examined the

relationship between income growth and saving using both cross-country and household data.

At the aggregate level, they found that growth causes saving, and households with higher

income growth save more than households with low growth at household level. Caroll and

Weil (1994, [9]) explained this phenomenon by using theory of habit stock effect. They contended

that initially, a country has its own saving habit. When the rate of growth is increased in the first

period of life, the country»s income is going to be increased more than its consumption, and


therefore increased first period saving rate. The average saving rate of a fast-growing economy

will be higher than that of a slow-growing economy (Modigliani, 1970, [44]). What makes

Mohan»s (2006, [45]) work interesting was that he divided countries that become his samples

into different income levels (LIC/LMC/UMC/HIC). The primary hypothesis of Mohan (2006, [45])

is whether the income level of the economy influences the direction of causality between

growth rate and savings. Using time series annual data, Granger causality tests were conducted.

Mohan (2006, [45]) concluded that his study favored the hypothesis that the causality is from

economic growth rate to growth rate of savings. Mohan (2006, [45]) contended that income

levels play an important role in determining the direction of causality, he argued that the

explanation of positive causality between economic growth rate and saving rate could be best

explained by the human wealth effect theory.

The relationship between interest rates and aggregate saving involves a number of complex

theoretical and econometric problems; the most important are separating out income and

substitution effects of interest changes, quantifying the role of expectations and planning

horizons in saving decisions, and solving a difficult econometric identification problem. Williamson

(1968 in Balassa (1989, [5]) in an empirical study of six Asian countries found that with the

exception of Burma, real rates of interest were negatively correlated with national savings. In

turn, Gupta (1970, [19]) found the interest elasticity of savings to be positive and statistically

significant at the 1 percent level for India, when per capita disposable income was used as

explanatory variable. A study by Yusuf and Peter (1984, [61]) concluded that a one percent rise

in the interest rate was accompanied by an approximately one percent increase in gross national

saving; i.e. an interest elasticity of savings of 1 (Balassa, 1989, [5]). Several other studies have

concentrated principally on the effects of interest rate reforms in Korea, Taiwan and Indonesia,

where increases in bank deposit rates (along with increased load rates) have been accompanied

by sharp rises in savings deposits without dampening the business demand for loans. But this

may simply entail a redirection of savings and a change in the pattern of investment toward

more productive forms rather than an increase in the saving propensity.

Inflation is a good macroeconomic proxy for stability. Several studies proved different

results concerning the relationship between inflation and saving rates. Some studies analyzed

effect of inflation and savings showed a negative effect (Heer and Suessmuth (2006, [22])).

Haan (1990) in Heer and Suessmuth (2006, [22]) found that a rise of the inflation rate from

0%-5% decreased savings by almost 10%. However, proponents of positive relationship between

saving rate and inflation are more common. Based on precautionary saving theory, households

increase their savings whenever they feel threatened by the instability of the country»s economy.

As previously said, inflation is often used as a proxy for economic stability. Consequently, savings


will increase whenever inflation is set at a higher rate. Deaton (1977, [14]) argued that unexpected

inflation caused involuntary saving because individual consumers were not sophisticated enough

to differentiate between relative price changes and absolute price changes. This lack of possible

means for individual customers to compare relative and absolute price changes would eventually

lead them to think that all goods are relatively more expensive; so that they choose to consume

less and save more (assume real income is maintained at same level). According to Deaton

(1977, [14]), as unexpected inflation rises, saving ratio also rises. Meanwhile, Howard (1978,

[27]) argued that inflation influences saving in two different assumptions. As long as the inflation

is unexpected, it will increase saving rate, since it creates pessimism about economic stability,

so that people are encouraged to save more. But if the inflation is expected (provided in advance),

it encourages people to increase their purchase of durable goods, therefore decreasing their

savings during inflationary period.

Modern consumption theory starts with the presumption that consumers like to smooth

out consumption over time, whether over the life cycle (Modigliani and Brumberg, 1954, [43])

or in the face of temporary fluctuations to income (the permanent income hypothesis of Friedman

(1957, [17])). Life cycle saving theory from Modigliani and Brumberg (1954, [43]) suggested

that consumers tend to smooth consumption over a lifetime. Modigliani and Brumberg (1954,

[43]) assumed in their model that savings would be high when incomes are high (during

productive working age), and people will dis-save during retirement. Life-cycle theory of saving

predicts a rise in saving as the youth-dependency ratio declines in the later stages of demographic

transition. Young-dependency ratio is regarded as a constraint for saving because children

charge a heavy expenditure for the working age population. Children contribute to consumption,

but not to production. That»s why high young dependency ratio is expected to impose a constraint

for saving (Leff, 1969, [37]). Leff (1969, [37]) found that dependency ratio significantly influence

aggregate savings. High dependency ratio is also used to evaluate the disparity between

developing and developed countries. Old-dependency ratio is also regarded as another constraint

for saving in the countries with no retirement plan. The elderly will be a burden for their working

children since they have no more income, or if the retired adult still should spend some expenses

for their young children. Both cases will be constraints for saving. Formally, if adults with fewer

children have more resources available over a lifetime, and these additional resources are

consumed by the adults themselves (rather than on children»s education for example),

consumption smoothing implies that consumption will also be higher after retirement, and

hence saving for retirement will have to be higher (Attanasio et al., (1999, [3]); Scholz et al.

(2006, [53]); Skinner (2004, [56])). Many studies find evidence of an impact of the youth and

old-age dependency ratios. For the youth-dependency ratio, Rijckeghem and Üçer (2009, [48])

estimated that a 1% point reduction in this ratio is associated with a 0.3 percentage point


increase in the saving rate in the short-run (0.5 percentage points in the long-run). The

corresponding are 1.4 and 2.8 for the old-age dependency ratio.

IV. METHODOLOGY

IV.1. Estimation Method and Model

We use Dynamic Panel Data (DPD) model to estimate the characteristic and the

determinants of saving. We assume that there are dynamic relationship between savings and

its lagged value. We define the lagged value relationship on current savings as the persistence

of savings over time. Savings are considered as persistent if the coefficient of the lagged value

approach 1, since it means that under the rest remains constant, savings tends to be constant

over time. However, if the coefficient is significantly different from 1, savings are considered as

not persistent, since the value will change over time, either increasing or decreasing, with the

rest remain constant. Savings is increasing if the coefficient is higher than 1, and reversely

decreasing when the coefficient is lower than 1. For this estimation, we use gross domestic

savings per capita to show individual savings, replacing household savings which cannot be

used due to unavailability of data for all ASEAN countries.

The main focus on this model is the import from ASEAN countries and China as the main

variable. We use ratio of net import from ASEAN countries and China from the total GDP for

estimation. Why net import while others use net export? The reason is to simplify the

interpretation so that we put import on our main focus in trade, nor the reverse. This variable

can be explained as the contribution of the ACFTA on the total GDP of ASEAN countries. The

main hypothesis is that import from ASEAN countries and China has negative impact on the

savings, which proves that an increase in the respective import will decrease the savings, due to

an increase in consumption. We are also going to compare the elasticity of import to the

persistency of savings to see whether under the ACFTA savings will be depleted over time,

indicating an increase of the poor»s vulnerability.

In order to acquire a more accurate, precise coefficient to compare, we insert more regressor

as control variables. Their role is simply as the explanatory terms which specify the model to get

more accurate coefficients, and also for a direction in applying it to the policy implications. The

control variables in the model are as follows:

1. The income of people, represented by the GDP per capita. An increase in people»s income

provides people with more funds to save. Therefore, the relationship is expected to be positive.

2. The economic growth, defined as the percent change of current GDP from the previous

year. An increase in economic growth, which expands the economy, increase the potency of


economic activities and a rise in income per capita which has positive relationship with

savings.

3. The deposit interest rate. This is one of the pull factors for the people to save more since

interest rate reflects the rate of return for not holding cash in some periods. Though people

do not usually care for deposit interest rate, but the impact should be positive since rationally

people would aim for higher return. However, in the end, it depends on the opportunity

cost.

4. Price change or inflation. It has reverse effect from the interest rate, or we could call it as the

opportunity cost we have mentioned before. An increase in price level requires people to

hold more cash to consume even on the same volume of consumption. If the inflation rate

is higher than the interest rate, the opportunity cost of saving will increase and motivate

people to hold cash, and reversely. We could compare the elasticity of this variable with the

interest rate elasticity to gain a conclusion which is more important between interest rate

and inflation rate. We could expand the result into a policy implication, especially for the

monetary policy on interest rate and inflation.

5. Dependency Ratio. This is the only demographical indicator among those macroeconomic

indicators. The impact of this variable can be twofold. It is whether the increase of dependency

ratio will increase or decrease savings. Generally, we would expect negative impact since an

increase in dependency ratio will increase current spending, leaving the savings being depleted

currently. However, a forward-looking paradigm might exist where an increase in dependency

ratio will motivate people to prepare for this dependent people»s needs in the future, like

school or health.

ittititi

titititi

DEPENDINFLINTR

GROWTHINCOMEIMPORTSAVINGSSAVINGS

εβββ

βββα

++++

+++= −

,6,5,4

,3,2,11,

where,

SAVINGS is savings per capita

IMPORT is ratio of net import from ASEAN-China countries from total GDP

INTR is deposit interest rate

INFL is inflation rate (based on CPI)

DEPEND is dependency ratio

i is individual, consist of ASEAN countries3

t is yearly time dimension

3 Note that we exclude China from the panel estimation since we assume that China bears more benefit under this ACFTA, whileASEAN»s developing countries hold more risks of losses.


IV.2. Data

We estimate it using panel data of all ASEAN countries from 2000 to 2008. Since ACFTA

has not been imposed for long, we use historical data to predict the impact of ACFTA in the

present and the future. We receive data for savings per capita and import from United Nations»

UNSTATS and UNCOMTRADE. For interest rate and inflation, we use data from IMF»s International

Financial Statistics (IFS) and for dependency ratio we use data from CEIC.

The DPD methodology that we use for this model is Arellano-Bond 1st Difference GMM

due to the following reasons:

1. Relationship is existed within savings and its lagged value.

2. We assume that there are dynamic relationship within savings and economic growth, as

explained in Mohan (2006, [45]), also with the interest rate and inflation.

3. The unobserved country-specific error term (wi) in terms of demographical indicators are

correlated with the dependency ratio.

4. The country numbers as cross section data (N = 10) is relatively higher than the number of

time series. (T = 7).4

Now that we have several problems arises above, we are obliged to eliminate the problems,

which are solvable using the Arellano-Bond GMM. The Arellano-Bond GMM itself is an estimation

methodology to observe the effect of dynamic relationship between the dependent variables

and its lagged value. As for the endogeneity problem, we impose instrumental variables on the

GMM. For instrumental variables imposed in this model, we put the lagged value of the

endogenous regressor (growth, interest rate and inflation)

The third problem, correlation of unobserved country-specific error term is eliminated

using the first difference in Arellano-Bond GMM following this formula:

4 Since Arellano Bond GMM use first difference and we put first lag of savings on the model, the estimator is automatically drop twofirst observation, therefore the remaining time observation is 7.

where,

then,

wi is the unobserved country-specific error term. As we can see from the equation above, we

have eliminated the unobserved country-specific error term using the first difference term.

∆yi,t

= α1∆y

i,t-1 + α

2∆X

i,t + ∆

i,t

yi,t − y

i,t-1 = α

1 (y

i,t-1 − y

i,t-2)

+ α

2 (X

i,t − X

i,t-1) + (

i,t −

i,t-1)

i,t = w

i + u

i,t

i,t −

i,t-1 = (w

i − w

i,t)

+ (u

i,t − u

i,t-1) = u

i,t − u

i,t-1 = ∆u

i,t


Therefore, the error term remains is vi,t which is panel data error term from the estimation.

Hence, we should worry anymore about this correlation between errors and the independent

variables since the problematic unobserved country-specific error term has been eliminated

from the estimation.

V. RESULT AND ANALYSIS

Using Arellano-Bond GMM in two-step estimation from Stata 11, we acquire the following

result:

Coef. (Std. Error) [Prob.]

Saving (-1)Saving (-1)Saving (-1)Saving (-1)Saving (-1) 0.1439227* 0.149482** 0.5340291***(0.0605343) (0.0697293) (0.0995964)

[0.022] [0.032] [0.000]ImportImportImportImportImport -5.13867 -6.781835*** -3.844283

(5.173365) (1.697932) (5.063839)[0.326] [0.000] [0.448]

IncomeIncomeIncomeIncomeIncome 0.6441702*** 0.6125879*** 0.2395092***(0.0509349) (.0662613) (0.0486907)

[0.000] [0.000] [0.000]GrowthGrowthGrowthGrowthGrowth 1.540742 10.84045 12.7541**

(3.339246) (19.5483) (6.328766)[0.647] [0.579] [0.044]

IntrIntrIntrIntrIntr 2.08293 40.2688 14.3462(10.82252) (41.9624) (12.91631)

[0.848] [0.337] [0.267]InflInflInflInflInfl -1.352008 -6.455056 3.970357

(2.637306) (8.411291) (4.28696)[0.611] [0.443] [0.354]

DependDependDependDependDepend 4.962723 12.66345* 4.704166(5.290578) (7.258577) (3.515673)

[0.354] [0.081] [0.181]

FE GMM OLSVARIABLES

*** (**) [*] significant under 1% (5%) [10%] critical value

ContinuumContinuumContinuumContinuumContinuumFE 0.1439227 UNBIASEDUNBIASEDUNBIASEDUNBIASEDUNBIASED

GMM 0.149482OLS 0.5340291

ValidityValidityValidityValidityValiditySargan 1.000000 VALIDVALIDVALIDVALIDVALID

ConsistencyConsistencyConsistencyConsistencyConsistencyM1 0.4301 INCONSISTENTINCONSISTENTINCONSISTENTINCONSISTENTINCONSISTENTM2 0.4489

GMM POST-ESTIMATION ΩΩΩΩΩ


First, we are going to see the post-estimation. The continuum indicates that lagged value

coefficient in GMM (Arellano-Bond First Difference) is slightly higher than Fixed Effect estimation,

while the OLS»s coefficient is significantly higher than the GMM, which makes sense since OLS

usually provides us with a somewhat excessively high coefficient. Therefore, we receive unbiased

estimators in this model due to the continuum condition.

The Sargan test shows us that there are no correlations between the residuals and the

over-identifying restrictions of instrumental variables if they are truly exogenous. In this case,

this might be because we do not put any instrumental variables in our estimation. Therefore,

we should not worry about the validity of our model, since the Sargan Test shows us good

result.

However, the Arellano-Bond test indicates that there is no autocorrelation in M1 which

makes the estimators to be inconsistent, but the bright side is that there is no autocorrelation

in M2, since if otherwise, the estimation would be completely inconsistent. We have done

many statistical engineering on the variables as well as adding and dropping variables or changing

their definitions, yet this is the best outcome we could have in terms of p-value of M1. Moreover,

since this is our basic model, therefore, we decided to use this model as our estimation.

Now, we are going to compare the results between the three methodologies before we

emphasize the whole result of Arellano-Bond GMM. The key variables, Import, have negative

impact in all of the three, which means that this negative correlation is not due to the utilization

of DPD in our estimation. The differences between methodologies are located in the difference

of coefficient measurements. It happens not only in the key variable, but also in the control

variables. The regressor does have same relationship in all of the methodologies, except one for

the inflation in OLS. While difference methodology might provide us with significantly different

relationship, our model here provides us with similar results. Therefore, as we said before, we

should not worry about the distortion of estimation result due to the difference of methodology

and the existence of control variables.

The next step is to emphasize the Arellano-Bond results. The import has negative

relationship to savings just like our hypothesis stated. It means that if savings will be depleted

under increasing import. While import has negative relationship, the estimation shows that

savings is not quite persistent over time to hold the waves of import which will devour savings

in the process over time. The savings is considered as not persistent since the dependent lagged

value»s coefficient is significantly lower than 0, precisely 0.1779878. It means that under the

rest remains constant, the savings are going to deplete continuously, even without increase in

imports. Both dependent lagged value and import are significant under 5% critical value which

means that their impact is consistent over time.


Now for the control variables, only income per capita and dependency ratio which is

significant under 5% critical value, while the remaining is not significant. Income per capita has

positive relationship with savings which means that an increase in income per capita will increase

the savings. Economic growth also encourages people to save since it has positive relationship

with savings. So does the interest rate. An increase in deposit interest rate brings positive

impact on the motivation of people to save. While lastly, as expected, inflation holds negative

impact on the savings since people have to hold more money.

The estimation has provided us with the required information on how import affects the

saving rate along with other macroeconomic and demographic explanatory terms. We are

going to focus more on how the regressors affect the dependent variable. Significance does

matter but even for the insignificant variables, we are still analyzing the impact of the regressor

since we can still consider the coefficient as the tendency of how the variables affect the

dependent variable.

V.1. The Savings Behavior in ASEAN

We consider the estimation result as a behavior model of savings in our specific region,

ASEAN, under flowing trade of goods within ASEAN and China. Let us recall the estimation

result of Arellano-Bond GMM for analyzing purpose.

The lagged dependent variable»s coefficient shows us the persistency of savings per capita

over time, ceteris paribus. It indicates the behavior of people to keep their savings over time in

condition where the rest remain constant. The coefficient value of lagged dependent variable is

significantly below 1.00, precisely 0.15, which means the saving per capita would decrease by

85% over time. It indicates that people will draw their savings in high proportion in order to

fulfill both their needs and wants. If we take wants into account, since needs are basic goods

that cannot be eliminated from routine consumption buckets, we could expect people tend to

become consumptive since they consume goods outside basic needs which along with the

basic needs consumption, it depletes the saving per capita by 85%. Recall that we assume the

rest remain constant, so that it means there is no adjustments of consumption under price

changes; therefore the coefficient shows only the depletion series of saving rate. Based on this

estimation, we take a simple and quick conclusion that ASEAN people weigh more on

consumptive behavior, which is a behavior that can be found in developing countries, recall

that most of ASEAN countries are developing countries.

The net import has negative impact on saving rate. It means that an increase in the

import over the export level will deplete the consumption. This is just like our hypothesis


stated earlier in this paper. An increase in import level, while export remains constant, will

reduce people»s savings. This is due to the increase of consumption under the increase of

goods availability in economy. As estimated before in the coefficient of saving»s lagged value,

ASEAN people tend to consume more over time in such a high proportion of 85%. This data

is estimated before the ACFTA being implemented in ASEAN (ACFTA was started in January,

2010). Therefore, we can expect that under ACFTA, the flow of goods will surely become

high as the import on ASEAN countries increases; the consumption pattern of ASEAN people

would increase. If the other variables assumed to be constant, the savings would be decreased

to nothing in a short time. But, this is not without solution. The answer for it lies on the half-

side of net import, which is the export side. The export here acts as the counter-effect of the

import that reversely will increase saving rate. This logic comes from the formula of Net

Import, which is the subtraction of import with the export. An increase in export would

decrease the net import. Therefore, the export has reverse effect from the import. An increase

in export would let people to produce more products, allowing them to gain more income

from the economic activity. For another simple explanation, the export is an additional

component of GDP, so an increase in export would increase GDP and bear the potential of

increasing income per capita.

Speaking of income per capita, the estimation shows us that the income per capita has

positive effect on the savings, and moreover has positive impact. The coefficient of this variable

is 0.61. The implication of this coefficient is that ASEAN people would provide 61% of their

income change for saving and use up to 39% of it to consume. It could also work on the

reverse, when the income per capita reduced, people will draw their savings by 69% of their

income change, since they need more liquidity to consume needs under decreasing income,

probably under recession or crisis. This is also one of the answers to endure the impact of

ACFTA that is in line with export solution. Income is surely an important component to improve

if we are aim to increase or keep savings from the community. As discussed before, export is

one of the components of GDP and income, which means that export, needs to be one our

vital solution to increase people»s income.

We might think that there is some inconsistency inside this estimation analysis. At first,

we thought that people tend to become consumptive since the persistency of savings is very

low. But, reversely, the income per capita»s coefficient shows that people distribute more of

their income change to the savings, rather than to consumption. One point that we need to

see is that the consumptive behavior we analyze first is under the assumption where the income

is constant. With constant income over time, people tends to deplete their savings to consume

more and this might be because of the insufficiency of income for ASEAN people, especially


those who live in developing countries, to fulfill their needs and wants. Therefore, people keep

on drawing their savings to fulfill their needs and wants.

For the income change that is allocated more to the savings, the explanation might lie

within the estimation of dependency ratio parameter. The dependency ratio has significant

positive impact on saving rate. It can be explained through precautionary saving behavior theory,

but this time we relate the instability discussed in the theory to high expense that the productive

groups bear. More people depend on the productive age; more funds will be needed to prepare

for future consumption. One simplest example is for children who are still enrolling on school

or university. The parents who are in productive population must allocate more of their income

for their children educational plan. This is also shows that most of ASEAN people are a risk-

averse kind of people when they have more people under their care. However, this is not to be

proud of, since this variable only explains why people allocate more of their income change for

saving. We cannot use this variable as our hope to increase savings. Increasing dependency

ratio is surely not an answer to keep the savings rate; it is just an explanatory term.

The remaining variables are not significant; however we are still going to analyze the

insignificant impact to see the potential impact these variables can do for the saving rate.

Growth has positive impact which means that the expansion of the economy could provide

people with opportunity to increase income and furthermore the saving. This is due to rapid

growth of population that usually happens in ASEAN as a region of developing countries. An

increase in growth itself might not affect the saving rate since it might not increase the individual

income of the people. If the economic growth is not as fast as the population growth, then

basically the income per capita, our significant variable, will be decreased. That is why growth

is not significant in affecting the saving rate.

Deposit interest rate will increase saving rate since it is a proxy of return if depositors save

their funding on banks. An increase in return would encourage people to save more, in hope to

gain more return from the interest. Interest rate is not significant because return on saving is

not quite encouraging for most people. This is because most people which have only regular

income would not save huge amount of money, like about billions Rupiah. This interest rate

would not provide them with significant return if not invested in more than hundred billions

Rupiah. Since most people only save up to millions Rupiah, the potential return would not be

that encouraging for them to save more.

Reversely, inflation has negative impact on the saving since under high price, people have

to consume more in terms of value, not the quantity. Therefore, they have to reduce saving in

order to adjust their money allocation on the increasing price to consume needs in the same

quantity. The reason why this variable is not significant is because people might have more


proportion on wants in their consumption rather than basic needs. If people consume more

basic needs they will adjust their savings to keep them being able to consume this basic needs.

But, if people consume wants in high proportion, when price increases, they can just decrease

their consumption on these wants to keep them being able to access basic needs. This is

because wants is a normal goods which the quantity demanded will decrease if the price

increase, while basic needs is an inferior goods which the quantity demanded will only be

adjusted under the change of income (price does not matter). Therefore, under high proportion

of wants consumption, high inflation can still allow people to reduce their normal goods

consumption so that they can keep more of their savings.

Usually we will compare the coefficient of these two variables to see which one has more

impact on the saving rate, but unfortunately these two variables are not significant. We cannot

compare the parameter estimated in this model since the coefficient might not work like stated

in the estimation. Therefore, we are not going to put them on our focus of our policy

recommendation. But, we must keep in mind that these variables might have these impacts in

the future which can be potential tools in the future.

V.2. ACFTA and The Poverty Allevation Strategy

Our estimation on import concludes that import is not an appropriate answer for

sustainable poverty alleviation. While we might think that this trade openness could increase

people»s access to more goods and services which in terms of Expenditure Poverty, the poverty

rate would be decreased even though the income of people does not change, we missed one

point where these expenditures could be quite bothersome in the future. This is due to the

depletion behavior the savings bear under the increasing of imports. Therefore, this depletion

of poverty rate might be a temporary one since it depends on the availability of goods from

abroad. We can expect that if one day a shock would occur, and the flow of trade needs to be

halted, the availability of goods would be depleting and therefore the poverty rate would be

re-bounced. Moreover, under the depletion of savings, people (especially the poor) would not

be prepared to adjust their income to overcome the increasing price due to the decreasing

quantity supplied. This is where our key variable, savings, enters to become the buffer for these

people for future risk preparation. The potency of savings depletion is the reason why we

conclude that the ACFTA is not an answer, or a proper strategy for poverty alleviation, even if

in the other hand it could boost economic growth.

The estimation shows us that this ACFTA is probably a big disadvantage for sustainable

poverty alleviation strategy. However, the ACFTA has been implemented and is progressing for


months up until now. It is impossible to suddenly alienate the agreement in this moment, and

probably for long time to the future. In addition, it is not like that the ACFTA is completely

malice for ASEAN countries since, in fact; it provides us with various opportunities, even for

poverty alleviation. The most important thing is how to make use of these opportunities to

provide us with enough savings so that sustainable poverty alleviation could be achieved.

Based on our estimation, the most important variable to increase saving rate is the income

per capita. It means that the key on expanding people»s savings lies on how we harness the

potentials of trade openness in ACFTA to increase income per capita. The lifting of tariff barriers

across ASEAN and China for trades must not be used to increase our domestic availability of

goods so that people could easily access goods since that would be our disadvantage in terms

of savings. We must take advantage of this agreement to improve our export side so that we

could increase income per capita. The estimation shows that in reverse of the negative impact

of import on saving rate, the export does have positive impact since net import is the subtraction

of import by export. Increase in export means that the productive side of the economy is

progressing since the increase of GDP is the result from the productions rather than solely

consumptions. Moreover, the increase in export employs more people to increase the output,

so the income of the people could be improved due to the increase of employment or the

potential of increasing wage due to the increase in output growth.

Therefore, the government should support the export side to overcome the ACFTA

challenges. This can be done by providing facilities for producers, especially the export-oriented

ones, to produce more goods which can be potentially circulated in ASEAN and China. Export

subsidy would be one of the solutions to promote export however it could be distortive on

international price that is avoided in the free trade agreement.

Commodity-imported control might be better than the export commodity. However, the

commodity-imported control we talk about here is not how we limit the goods imported to our

country. It is about how we counterbalance the flow of consumption goods with the imports of

raw materials required for export-oriented industries. As we stated before, under free trade

agreement, we can expect cheaper goods even for the raw materials. We must view this as an

opportunity to access cheaper raw materials in order to increase productivity and impose a

more competitive price for our export commodities. This way, we can improve our export side

without sacrificing the import side which is required for maintaining availability of goods. The

solution provides us with income side and expenditure side of poverty alleviation.

Price stabilization is also required expand people»s saving. The price needs to be stabilized

in low condition. This is a concern for central banks to achieve this condition. How can this

price stabilization be important for this? The reason is twofold. First is that high domestic price


is one of the factors which determines the motivation to trade. Any basic trade theorems as

explained in textbooks like Markusen, et al (1994, [40]) and Krugman and Obstfeld (2006, [35])

emphasized the role of price relativity in the trade creation. Exporters would like to export their

goods if the goods» price at the partner country is higher than the price in their country, assume

that there is no dumping policy. This is due to the potential of capturing more profit from the

trade since they can sell at a higher price. Increase in domestic price will flood the domestic

market with imported goods which aim to be sold at higher price. It would result on the

increase of consumption which is what we avoid in ACFTA. Moreover, the price itself is also the

determinant of exchange rate since they are both related to the currency»s purchasing power.

High price means weaker exchange rate and the reverse. By maintaining price at low level,

exchange rate can stay at strong level which is encouraging for exporters to export more.

Second, the price level is also a motivation for people to hold liquid money than to save.

This is because the increase in price means that the people are required to expend more even

for the same level of consumption. High price would put savings at disadvantage. In addition,

price fluctuations would be even worse. This is due to the uncertainty that the people face so

that they begin to be more preserve on the economic condition. In that case, no matter high or

low price the economy has, people would not be motivated to save.

Therefore, simply a low level of price is not sufficient to draw people to save, not only

because that fluctuations will increase uncertainty, but also the low price and strong currency

might decrease our export commodities» quantity demanded from partner countries which is

detrimental if we are aiming to improve saving potential through export promotion. A stable

price in relatively low level is more appropriate than only a low price condition. This might also be

the reason why on the estimation before, the inflation rate was proved to be insignificant. It

might be due to the additional uncertainty component that determines savings along with inflation.

Lastly, the other opportunity that the government needs to utilize is the possibility of more

direct investment that the ACFTA could provide. We must not forget that ACFTA is not solely an

agreement for trades for goods and services, but also for an increasing opportunity for more

foreign direct investment (FDI). The question mark that might arise from this recommendation is

probably how can this FDI increase saving rate since the transmission mechanism might be quite

long, but it is possible to utilize the mechanism. The FDI can open more job opportunity to

employ more domestic workers. This will increase the employment side so that people»s income

can be raised. Moreover, if we impose this FDI more at the export-oriented industries, we could

improve the productivity of the industries, allowing them to export more to expand our income

per capita. In the end, again, the FDI is a mechanism to expand our exports and income per

capita since it is considered as our key variable here to increase saving rate.


We might be considering that increasing productivity of export-oriented products

excessively might be detrimental for us if crisis and recession occur in the region. Under crisis

and recession, the purchasing power of our partner countries might be reduced and the trade

activity would be frozen temporarily. This will create a big shock to our economy since our

export side will be devoured by the decreasing import demand to partner countries. It is indeed

something that we must be cautious for, but at the same time, this is something that our

demographical advantage takes into account.

Most of ASEAN countries have great population, especially Indonesia that has population

of approximately two hundred million people. This is a demographical advantage for ASEAN,

since they have an abundant domestic market for times when the foreign demand is depleting.

Moreover, by increasing saving rate, we have provided our people with enough purchasing

power in times like this which, in fact, is what the saving rate»s role from the beginning. So,

countries like Singapore which rely so much on trade, while at the same time does not bear the

great population advantage, can still survive the recession due to the mountains of saving they

have provided in the first place to overcome ACFTA. It is not only working for small population

countries, but also on the other ASEAN countries as well. Therefore, we are not going to suffer

great increase in the number of poverty, as we are afraid of in the beginning.

The policies stated above need a good coordination and cooperation between the

government and the central bank. The central bank is in charge of the price stability task, while

the government is in charge of the real sector policies that improve the export directly. This

cannot be completed well without a good cooperation from both parties. This way, the saving

rate can be maintained for people»s buffer against future shocks that could throw more people

into poverty, resulting in an overshoot of poverty rate. Keep this in mind, that we are not

rejecting ACFTA with this research. Reversely, we view this as an opportunity to support the

poverty alleviation strategy. However, the ACFTA itself is not a proper strategy for sustainable

poverty alleviation since the impact on the poverty alleviation is only on the short run. Despite

of it being an inappropriate strategy, the ACFTA provides us with opportunity to expand the

sustainable poverty alleviation strategy. The authentic proof of this is how the ACFTA can be

taking into advantage as the policy recommendation we emphasized above. ACFTA is not

something we must afraid of. It is an opportunity that we must look at the bright side.

VI. CONCLUSION

This paper has proved that, despite of being an effective growth engine as practitioners

emphasized, there is potency that regional free trade like ACFTA might be detrimental, in


some ways, for developing countries in ASEAN, especially for sustainable poverty alleviation

strategy.

Depletion of the saving rate is something that we propose in this paper. Saving rate, as a

shock buffer for the poor under recession, is an important part of sustainable poverty alleviation.

The estimation has proven that import from ASEAN and China impacts on the depletion of

savings for ASEAN countries. This is due to the increasing circulation of goods in the region that

allows people to access goods easily, accommodating the consumptive behavior that a developing

countries» population bear. In addition, the saving rate itself in ASEAN is not a persistent being

since when the rest remain constant; it will deplete itself gradually due to the continuous

consumption.

Developing people»s income per capita is the key solution if we want to successively

overcome this challenge. Estimation proved that people still tend to save when they get extra

income. This is the important point that we must take into advantage. Under this circumstance,

unable to severe the ties of ACFTA no matter how detrimental it is, governments of ASEAN

countries must increase its people»s income per capita using the opportunity that ACFTA provides.

There are four policies recommendation that we emphasized in this paper. Those are: (1)

Counterbalance the import wave that ACFTA brings by promoting exports, since the barriers

have been gradually lifted, across the ASEAN and China, in order to boost income per capita;

(2) Controlling the commodities exported to our market, focusing more on raw materials import,

to avoid over-consumptive behavior on consumption goods and increasing the productivity of

domestic industry, especially the export oriented industry; (3) Stabilize the price fluctuations to

encourage people to save more and strengthen the currency»s purchasing power so that exporters

are encouraged to export more and the import waved can be endured; and (4) Promoting

foreign direct investment to boost employment and increase the productivity of export oriented

industry. These policies must be done under good cooperation and coordination by government

and central bank.

We do not reject ACFTA in this paper; we view ACFTA as an opportunity to develop

ASEAN even more. It is reflected by our recommendations. Despite that we stated ACFTA as

detrimental in some ways, used ACFTA as the vessel to increase saving rate to counterbalance

the depletion impact of it to saving rate. In conclusion, ACFTA itself is not a proper strategy for

poverty alleviation if we leave it as it be, but can still be utilized to support sustainable poverty

alleviation strategy with the authentic opportunity it can provide. ACFTA is not something we

must afraid of. It is an opportunity that we must look at the bright side.


AlFoul, Bassam Abu, 2010, ≈The Causal Relation between Savings dan Economic Growth:

Some Evidence from MENA Countries∆.<http://econpapers.repec.org>, diakses pada 13 Juli

2010.

Anoruo, E. dan Ahmad, Y., 2001, ≈Causal Relationship Between Domestic Savings and Economic

Growth: Evidence from Seven African Countries∆. African Development Bank, Vol. 13, Issue

2, pp. 238-249.

Attanasio, Orazio, James Banks, Costas Meghir, Guglielmo Weber, 1999, ≈Humps and Bumps

in Lifetime Consumption∆. Journal of Business & Economic Statistics, Vol. 17, hal. 22-35.

Azzopardi, Franco, 2004, ≈The Propensity to Save and Interest Rates∆,∆,∆,∆,∆, <http://www.ssrn.com>,

diakses pada 16 Juni 2010.

Balassa, Bela. ≈The Effects of Interest Rates on Savings in Developing Countries∆. World Bank

Working Paper Series, Vol. 55. 1989.

Berube, Gilles dan Denise Cote. ≈Long-Term Determinants of the Personal Savings Rate: Literature

Review and Some Empirical Results for Canada∆. Working Paper √ Bank of Canada. 2000.

Birdsall, Nancy. ≈Why Low Inequality Spurs Growth: Savings And Investment By The Poor∆.

Inter-American Development Bank Working Paper, No. 327, 1996.

Brumberg, Richard E. ≈An Approximation to the Aggregate Saving Function∆. Economic Journal,

Vol. 66, hal. 66-72. 1956.

Carroll, Christopher D. and David N. Weil. ≈Saving and Growth: A Reinterpretation∆. Working

Paper Series, No. 4470. 1993.

Cebula, R. J. ≈Federal Government Budget Deficit and Interest Rates: A Note∆. Public Choice.

1987.

Darrat, Ali F., 1988, ≈Have Large Budget Deficits Caused Rising Trade Deficits?∆. Southern

Economic Journal, Vol. 54, No. 4, hal. 879-887.

Davidson, Russell and James G. MacKinnon, 1982, ≈Inflation and the Savings Rate∆. Queen»s

Economics Department Working Paper, No. 493.

Deaton, Angus, 1977, ≈Involuntary Saving Through Unanticipated Inflation∆. The American

Economic Review, Vol. 67, No. 5, hal. 899-910.

Dusenberry, J.S., 1949, ≈Income, Saving, and the Theory of Consumer Behavior∆. Cambridge,

Mass.: Harvard University Press.

Edwards, Sebastian, 1995, ≈Why Are Savings Rate So Different Across Countries?: An

International Comparative Analysis∆. NBER Working Paper Series, No. 5097.

REFERENCES


Friedman, Milton. 1957. ≈405.html∆The Permanent Income Hypothesis∆. NBER Chapters.

Gourinchas, Pierre-Olivier and Jonathan A. Parker, 2001, ≈The Empirical Importance on

Precautionary Savings∆. NBER Working Paper Series, No. 8017.

Gupta, Kanhaya L., 1971, ≈Dependency Rates and Savings Rates: Comment∆. American Eco-

nomic Review, Vol. 61, hal. 469-71.

Gylfason, Thorvaldur., 1993, ≈Optimal Saving, Interest Rates, and Endogenous Growth∆. Journal

of Economics, Vol. 95, hal. 517-533.

Harvey, Ross, 2004, ≈Comparison of Household Saving Ratios: Euro Area/United States/ Japan∆.

Paper of Organisation for Economic Co-operation and Development (OECD), No. 8, 2004.

Heer, B. and Suessmuth, B., 2006, ≈The Savings-Inflation Puzzle∆. Cesifo Working Paper, No.

1645.

Higgins, Mathew and Jeffrey G. Williamson, 1996, ≈Asian Demography and Foreign Capital

Dependence,∆ NBER Working Paper Series, No. 5097.

Higgins, Matthew, 1999, ≈Demography, National Saving, and International Capital Flows∆,

International Economic Review, Vol. V/39, hal. 343-69.

Hoelscher, G. P., 1983, ≈Federal Borrowing and Short-Term Interest Rates∆. Southern Economic

Journal, Vol. 50, hal. 319-33.

Horioka, Charles Yuji dan Junmin Wan., 2006, ≈The Determinants of Household Saving In

China: A Dynamic Panel Analysis of Provincial Data∆. NBER Working Paper Series, No. 12723.

Howard, David H., 1978, ≈Personal Saving Behavior and the Rate of Inflation∆. The Review of

Economics and Statistics, Vol. 60, No. 4, hal. 547-554.

Hufbauer, Gary Clade dan Yee Wong., 2005, ≈Prospects for Regional Free Trade in Asia∆.

Working Paper Series of Institute for International Economics, No. 05-12.

Huggett, Mark and Gustavo Ventura, 1995, ≈Understanding Why High Income Households

Save More Than Low Income Households∆. Discussion Paper Federal Reserve Bank of

Minneapolis, No. 106.

Islam, M. Faizul, 1998, ≈Brazil»s Twin Deficits: An Empirical Examination∆. Atlantic Economic

Journal.

Japelli, T., dan Pagano, 1994, M. ≈Savings, Growth and Liquidity Constraints∆. Quarterly Journal

of Economics, Vol. 109, hal. 83-109.

Kendall, Patrick, 2000, ≈Interest Rates, Savings, and Growth in Guyana∆. Paper of Caribbean

Development Bank.

Kuznets, S., 1960, ≈Quantitative Aspects of the Economic Growth of Nations: Capital Formation

Proportions∆. Economic Development Cultural Change, Vol. 8, No. 4, Part II.

Krugman, Paul and Maurice Obstfeld, 2006, ≈International Economics: Theory and Policy∆. 7th

ed. Addison Wesley - Prentice Hall.


Labonte, Marc, 2003, ≈The Budget Deficit and the Trade Deficit: What Is Their Relationship?∆.

Congressional Report Service Report For Congress.

Leff, Nathaniel H., 1969, ≈Dependency Rates and Savings Rates∆. The American Economic

Review, Vol. 59, No. 5, hal. 886-896.

Lindh, Thomas., 1999, ≈Age Structure and Economic Policy: The Case of Saving and Growth∆.

Population Research and Policy Review, Vol. 18, , , , , hal. 261√277.

Mankiw, N. Gregory, 2001, ≈Pengantar Ekonomi∆. 2nd ed. Jakarta: Penerbit Erlangga.

Markusen, James R., James R. Melvin, William M. Kaempfer, and Keith Maskus, 1994,

≈International Trade: Theory and Evidence∆. McGraw Hill.

Mikesell, Raymond F. and James E. Zinser, 1973, ≈The Nature of The Savings Function in

Developing Countries: A Survey of the Theoretical and Empirical Literature∆. Journal of

Economic Literature, Vol. 11, No. 1, hal. 1-26.

Mishra, P.K, S. K. Mishra, and J. R. Das., 2010, ≈The Dynamics of Savings and Investment

Relationship in India∆. European Journal of Economics, Finance and Administrative Sciences,

ISSN 1450-2887 Issue 18.

Modigliani, F. and Brumberg, R., 1954, ≈Utility Analysis and the Consumption Function: An

Interpretation of Cross Section Data∆ in Kurihara K., ed. Post-Keynesian Economics. New

Brunswick: Rutgers University Press.

Modigliani, Franco, 1970, ≈The Life Cycle Hypothesis of Saving and Intercountry Differences in

the Saving Ratio,∆ in W. A. Eltis et al., eds., Induction, Growth and Trade, Oxford, hal. 197-

225.

Mohan, Ramesh, 2006,≈Causal Relationship Between Savings and Economic Growth in Countries

With Different Income Levels∆. Economics Bulletin, Vol. 5, No. 3 hal. 1_12.

Plosser, C. I., 1982, ≈Government Financing Decisions and Asset Returns∆. Journal of Monetary

Economics, Vol. 9, hal. 325-52.

Ram, Rati, 1983, ≈Dependency Rates and Aggregate Savings: A New International Cross-

Section Study∆. The American Economic Review, Vol. 72, No. 3, hal. 537-544.

Rijckeghem, Caroline Van and Murat Ücer, 2009, ≈The Evolution and Determinants of The

Turkish Private Saving Rate: What Lessons for Policy?∆. ERF Research Report Series, No. 09-

01. 2009.

Romer, David, 2001, ≈Advanced Macroeconomics∆. 2nd ed. McGraw Hill, New York.

Romer, Paul M., 1986, ≈Increasing Returns and Long Run Growth∆. Journal of Political Economy,

Vol. 94, hal.1002-1037.

Rossi, Nicola, 1988, ≈Government Spending, the Real Interest Rate, and the Behavior of Liquidity-

Constrained Consumers in Developing Countries∆. Staff Papers - International Monetary

Fund, Vol. 35, No. 1, hal. 104-140.


Saltz, I.S., 1999, ≈An Examination of the Causal Relationship between Savings and Growth in

the Third World,∆ Journal of Economics and Finance, Vol. 23, hal. 90-98.

Scholz, John Karl, Ananth Seshadri, and Surachai Khitatrakun, 2006, ≈Are Americans Saving

«Optimally» for Retirement∆. Journal of Political Economy, Vol. 114(4), hal. 607-643.

Schultz, T. Paul., 2004, ∆Demographic Determinants of Savings: Estimating and Interpreting

the Aggregate Association in Asia∆. Paper Series of Economic Growth Centre √ Yale University,

No. 901.

Sinha, Dipendra, 1996, ≈Saving and Economic Growth In India∆. MPRA Paper, No.18283,

University Library of Munich, Germany.

Skinner, Jonathan, 2004, ≈Comment on «Aging and Housing Equity: Another Look in Perspectives

on the Economics of Aging∆. Chicago: The University of Chicago Press.

Solow,Robert M., 1956, ≈/viewitem.fcg/00335533/di951743/95p0039f/0?config = jstor&frame

= frame&userID = [email protected]/018dd5254c00502d8f04&dpi = 5∆A Contribution

to the Theory of Economic Growth.∆ Quarterly Journal of Economics, Vol. 70, hal. 65-94.Ω

Song, Byung-Nak, 1981, ≈Empirical Research on Consumption Behavior: Evidence from Rich

and Poor LDCs∆. Economic Development and Cultural Change, Vol. 29, No. 3, hal. 597-

611.

Todaro, Michael P. and Stephen C. Smith., 2008, ≈Economic Development∆. Pearson Education

Limited.

Wachtel, P. and J. Young., 1987, ≈Deficit Announcements and Interest Rates∆. American

Economic Review, Vol. 77, hal. 1007-12.

Yusuf, S. and R. Kyle Peters., 1984, ≈Savings Behavior and its Implications for Domestic Resource

Mobilization: The Case of the Republic of Korea∆. World Bank Staff Working Paper, No.

628.

103Making East Asian Regionalism Works

1 Earlier version of this paper has been presented on the International Conference on Business and Economics held in Bukit Tinggi,Indonesia (April 2010) and Thessaloniki, Greece (May 2010)

2 Graduate School of Asia-Pacific Studies (GSAPS), Waseda University 1-21-1 Nishi-Waseda, Shinjuku-ku, Tokyo 169-0051, JAPANE-mail: [email protected]

MAKING EAST ASIAN REGIONALISM WORKS1

Fithra Faisal Hastiadi 2

A b s t r a c t

For the past few years, regionalism has been progressing in East Asia with the likes of China,

Japan, and Korea (CJK) as the most prominent actors. Unfortunately, with the absence of trade arrangement

amongst the CJK, the present regional trade scheme is not sufficient to reach sustainability. This paper

uncovers the inefficient scheme through Engle-Granger Cointegration and Error Correction Mechanism.

Moreover, the paper underlines the importance of triangular trade agreement for accelerating the phase

of growth in CJK which eventually create a spillover effect to East Asia as a whole. Employing Two Stage

Least Squares in a static panel fixed effect model, the paper argues that the spillover effect will function

as an impetus for creating region-wide FTA. Furthermore, the paper also identifies a number of economic

and political factors that can support the formation of East Asian Regionalism.

JEL ClassificationJEL ClassificationJEL ClassificationJEL ClassificationJEL Classification: F15, C13, C22, C33

Keywords: Regionalism, Engle-Granger Cointegration, Error Correction Mechanism, Fixed Effect, Two

Stage Least Squares


I. INTRODUCTION

In this new millennium, regionalism has begun to emerge in East Asia. East Asian Countries

have been focusing on ways to expand intra regional trade that include: the establishment of

Regional Trade Agreements (RTAs) in the form of Free Trade Agreements (FTAs) and Economic

Partnership Agreements (EPAs). The trend towards regionalism has created a profound regional

and indeed global significance (Harvey and Lee, 2002). Japan, Korea and China are regarded as

the key actors for such action in East Asia.

Being acknowledged as the economic front runners, Japan, China and Korea are assumed

to have heavy responsibility for the economic welfare in the East Asian region. It is very obvious

that East Asian regionalism cannot be put into practice without these countries» strong support.

Unfortunately, the lack of institutional arrangements among these giant countries has stalled

the overall welfare effect for the East Asian communities. The present driving force of the

China-Japan-Korea (CJK) relationship is the market by which in some sense is not enough; it

should be matched by regionalism. The main focus of the regionalism is to make these countries

grow together so that it can spread positive externalities throughout the East Asian region. In

the long run it is expected that CJK will lead regionalism in East Asia.

The remainder of the paper is organized as follows. The second section studies the

economic structures and trade patterns in the CJK. The third section examines the effect of

openness in the CJK to economic growth in these particular countries. The fourth section

analyzes the prospects of the CJK increased welfare in creating spillover effect to ASEAN4,

which in this paper serves as a proxy for ASEAN countries. The fifth section presents the future

trend and path towards East Asian Regionalism, and finally the last section presents some

concluding remarks.

II. REVIEW ON JAPAN, CHINA AND KOREA ECONOMIC RELATION

Tracing back the relations since the post war era, economic ties between Japan, Korea and

China has evolved in somewhat gradual ways. The evolution of trade activities emerged from the

likes of China, which has a substantial transformation of trade structures. In the early 90»s, primary

commodities accounted for more than one third of China»s total export to Japan and Korea. In

this new millennium, it is still top Chinese export to Japan and Korea, but it is persistently followed

by the fast growth of machinery and transport (Chan and Chin Kuo, 2005). From this point of

view, trade within the north East Asian region is deemed to have substantial movement as a

result from the shift of trade towards a more industrialized structure. The emergence of China as

a regional manufacturing center is a dominant factor that contributes the trade shift.


The overall picture of the trade amongst these countries is described in Diagram V.1. It is

clear that trade activity is very intense by which performs as the major contributing factor for

economic growth in the region. The vast amount of trade has been very likely steered up by the

amount of FDI flows among them with Japan as the sole leader of it (Diagram V.2). In other

words, the creation of economic transformation in China and Korea that geared up the trade

was enchanted by Japan»s role in making investment in those countries.

Diagram V.1.Trade among Japan, China and Korea (2006, $Billion)

Diagram V.2.Investment among Japan, China and Korea (2005, $Billion)

Source: Watanabe (2008)

China Korea

Japan

118.5

92.9

27.3

50.3

44.5

87.7

China Korea

Japan

6.57

0.18

1.74

0.013

2.61

0.012

Source: Watanabe (2008)


To some extent, trade is almost synonymous to a country»s welfare. More specifically,

some research pointed out export as an engine of economic growth. From this stand point, it is

important to measure export sustainability to the economy, which in this section export among

the CJK become the main focus.

As already explained earlier, Japan, China and Korea are experiencing golden period in

doing export among them. Economic welfare is the most notable goal which links in this activity,

but is it sufficient to boost the economy in the long run? A pure market driven activity without

specific regional trade agreement might sometime create bias. It is clear that Japan, Korea and

China are lacking of such agreement among them (Urata and Kiyota, 2003) as described in the

table V.1.

To make an effective regionalism, Japan, China and Korea should support each other.

Therefore, intra regional cooperation within the CJK must take place by which can create

sustainable growth in East Asian region. The following sections serve to prove export sustainability

to economic growth, in the absence of trade arrangements, for the short and the long run.

Engle-Granger Cointegration and Error Correction Mechanism test are then employed for this

cause. This test employs time series quarterly data of GDP and for Japan, China and Korea

ranging from 1985 to 2004. The data is taken from CEIC database.

In doing Engle Granger Cointegration test, this paper divides the export relationship in to

three parts which are described in the following equations:

Table V.1Japan, China and Korea FTAs/EPAs

Countries Situation

China

Korea

Japan

Concluded

Under Negotiations

Under Considerations

Concluded

Under Negotiations


Concluded

Under Negotiations


Countries

Chile, ASEAN, Hong Kong, Macao

NZ, Australia, Pakistan, Singapore, GCC, SACU

Iceland, India, Japan-Korea-China, FTAAP, Switzerland

Chile, Singapore, EFTA, ASEAN, USA

India, Mexico, Canada, EU

FTAAP, China, Mercosur, NZ, South Africa, Japan-China-Korea,

Australia, GCC

Singapore, Mexico, Malaysia, Philippines, Chile, Thailand,

Brunei, Indonesia

India, Vietnam, Australia, Switzerland, Korea, GCC, ASEAN

FTAAP, Japan-China-Korea, South Africa

Source: Japanese Ministry of Economy, Trade and Industry, 2007


In these equations, JPGDP, CHGDP and KRGDP are Japan»s GDP, China»s GDP, and Korea»s

GDP respectively while Export JP, Export CH and Export KR are the variables of export destinations

to Japan, China and Korea. It would be possible to cointegrate Export and GDP since the trend

in export and GDP would offset to each other, creating a stationary residual. The residual is

called a cointegration parameter. In the data, if we find that the initial regression of the residual

(ut) gives stationarity it means that ut is stationary at order 0 (level) and it is notated as I(0). But

if ut is stationer in first difference, the variables of Export and GDP will be cointegrated in the

first difference which can be notated with I(1).

Table V.2Cointegration Parameters

Dependent Variable GDP (Japan) GDB (China) GDB (Korea)

Independent Variable

Export to Jepang na Stationer Stationer

Export to China Stationer na Stationer

Export to Korea Stationer Stationer na

From table V.2 we can see that, GDP and export relationship in the CJK yields stability in

the long run. It is proven by the stationarity of the error term in each of the cases. The

cointegration test that proves long run equilibrium describes that the model is not spurious.

(V.1)JPGDP = βo + β

1 .ExportCH + µ

t

(V.2)CHGDP = βo + β

1 .ExportJP + µ

t

ii. Korea and Japan Export Relationship

(V.3)KRGDP = βo + β

1 .ExportJP + µ

t

(V.4)JPGDP = βo + β

1 .ExportKR + µ

t

iii. China and Korea Export Relationship

(V.5)CHGDP = βo + β

1 .ExportKR + µ

t

(V.6)KRGDP = βo + β

1 .ExportCH + µ

t

i. China and Japan Export Relationship


Export is proven to be the engine of economic advancement in these countries. It approves

some previous research as the likes of Dorasami (1996), and Ekanayake (1999) of export and

economic growth relationship.

We have seen the long run relationship between Export and GDP. However, in order to

make it objective, we should also see the short run since it is still plausible to perceive

disequilibrium. Thus, could be noted as equilibrium error. This error then could be used to

relate the behavior of the short run Japanese GDP The technique to correct short-run

disequilibrium to its long run long run equilibrium is called Error Correction Mechanism (ECM).

The model of ECM is as follows:

(V.7)∆GDP_X = βo + β

1 .∆Export_Y + β

2.µ

t-1 + e

t

µt-1

is a cointegrated error lag 1, or could be noted mathematically as:

Ut-1

= GDPCountry Xt-1

- βo - β

1 ExportCountryY

t-1 (V.8)

In this equation, ∆GDPCountry X is the difference in GDP for Japan, Korea and China,

while ∆ExportCountry Y is the difference in export from country X to Country Y. As for

example, applies for the effect of Japan»s export to China on Japan»s GDP. From the above

model we can see that the long run relation between Export and GDP in Japan, China and

Korea would be balanced by the previous error. Below is the output for each country»s regressions:

i. Japani. Japani. Japani. Japani. Japan

In the short run, there is an equilibrium error for Japan»s Export to China with its relation

to Japan»s GDP. The coefficient of residual gives negative sign (-0.18), which means that Japan»s

Export to China is below the long run equilibrium. This will only lead to a rise of export for the

Tabel V.3Equilibrium Errors

Dependent Variable


Equilibrium error for Export to Japan na -1.0 9 *** -0.23 *

Equilibrium error for Export to China -0.18 *** na -0.48 ***

Equilibrium error for Export to Korea 0.017773 -1.33 *** na

Note: Statistical significance is indicated by *(10%), **(5%), and ***(1%)

GDP (Japan) GDP (China) GDP (Korea)


following periods. But it is important to note that the absolute value of the coefficient (adjustment

rate) is very small (0.18). This suggests that Japan»s Export to China is moving in a slow phase to

reach the long run equilibrium.

As for the relationship between Japan and Korea, the equilibrium error of the export

trend is not significant. These suggest that Japan»s GDP is adjusting to the change in Japan»s

export to Korea in the same period of time. In other words, Japan and Korea relationship in

terms of export has already reached steady state level.

ii. Chinaii. Chinaii. Chinaii. Chinaii. China

The residuals for the relationship between China»s GDP with China»s Export to Japan and

Korea are significant. These suggest that there is an equilibrium error in the short run. The

negative signs put the Export for a constant rise to reach the long run equilibrium. In China»s

case, the adjustment rate or the phase of acceleration for the long run equilibrium is very fast.

It can be seen through the absolute value of the equilibrium error coefficients which are 1.09

and 1.33 for China»s relationship to Korea and Japan respectively.

iii. Koreaiii. Koreaiii. Koreaiii. Koreaiii. Korea

Korea»s case is somewhat similar to China. The residuals for the relationship between

Korea»s GDP with Korea»s Export to Japan and China are significant. It yields similar explanation

with China»s case. However, the adjustment rate for the case of Korea is slower than China»s

but it is still faster than Japan»s. It gives the absolute value of 0.23 and 0.48 for Korea»s trade

relationship to Japan and China respectively.

From the ECM, we can conclude that North East Asian region is not moving at the

same phase to reach the long run equilibrium, which in this case Japan is the slowest one.

The insignificant value of acceleration rate for the case of Japan trade relationship with Korea

is also important point to note since it can be interpreted as an exhausted Korean market for

Japanese products (steady state condition). These facts are very crucial since it diminishes

Japan»s role as the sole leader in the north East Asia. Although whoever the leader is not to

important, but the stalled effect of a country»s economic growth in these region will only

serve as stumbling blocks in creating East Asian welfare. The rising growth of China and

Korea will soon meet its end mimicking the pattern of Japan if no serious action is site4.

Therefore, In order to strengthen regional welfare and accelerate the phase of adjusting,

economic integration must take place.


III. THE OPENNESS IN TRADE

Greater economic interdependence between Japan, China and Korea will act well as the

base of creating regionalism. In this sense, triangular trade agreements that dismantle trade

barriers will smooth the progress of improved trade flows among these countries by means of

greater market access. But unfortunately, this supporting environment only operates as fact in

a sheet. The process of regionalism in this area is proven to be difficult.

These countries may have aggressively reached other countries in making FTA»s and EPA»s

but none of which have been progressing among them (see table V.1). The reason of it will be

a subject for another research, while this section tries to focus on the effect of such agreement

to the economy. The lack of trade arrangements is being noted as the main factor that contributes

intra regional trade ineffectiveness in north East Asia. This hypothesis will be proved in the

following sections to come.

Export lead growth approach that has been done in the previous section with

cointegration and error correction model has actually provided the basis to measure openness

of a country, but in some ways this alone is not enough. It only works for confirming the

paradigm of trade as an engine of growth but it is not sufficient to measure a more robust

pattern of openness. Therefore, we then may have to address Dollar»s Relative Price Level (RPL

index).

This index is a measure of outward orientation of an economy which was explored by

Summers and Heston (1988). Using the US as the benchmark country, the index of country i»s

relative price level (RPL) is:

(V.9)RPLi

= 100 x Pi /P

us x 1/e

Where e is the exchange rate and Pi is the consumption price index for country i and P

us is the

consumption price index for US. Therefore, we can use the formula to measure inward- or

outward-orientation of a trade policy. With using the same analogy, this paper then customizes

the RPL index into this formula:

(V.10)RPLi

= 100 x Pi /P

tp x 1/e

Where Ptp is the consumption price index for the trading partner and e is the exchange rate (no.

of units of domestic currency per unit of trading partner currency). The customized RPL is then

become a powerful tool to analyze trade openness between the trading countries.


(V.11)∆GDP_X = βo + β

1 .∆RPL_Y + β

2.µ

t-1 + e

t

As already explained in the previous section, ECM provides the description of short run

shock. In this particular case, we examine the openness vis a vis trade liberalization trend in

north East Asia region. This test employs time series quarterly data of Exchange rate, CPI,

Export for CJK ranging from 2001 to 2005, the data is taken from CEIC data base. Below is the

equation:

From this particular test we can see that generally trade openness is affecting a country»s

GDP in a positive way. But in the short run, trade openness in the CJK is still below the equilibrium.

This suggests that trade openness is still finding its form in this area. Although we might not

see regionalism which liberalize trade in the short run, but the trend towards openness in trade

vis a vis regionalism is progressing in a respectful manner. We can see this through the adjustment

rate for the long run equilibrium (the coefficients of residuals) that yields an average of 1.1,

consequently we might see regionalism in North East Asia happen in the future.

IV. THE SPILLOVER EFFECT FROM JAPAN-KOREA-CHINA TRIANGULAR TRADETO ASEAN 4

As giants of Asia, the growth of Japan, Korea and China will most likely create positive

effect to the neighboring countries. Regionally speaking, the growth of North East Asia will

This equation mimics equation V.7, but the previous dependent variable is substituted

from export to RPL in order to suit the goal which is to measure the openness. ∆GDP Country

X is the difference in GDP from Japan, Korea and China, ∆RPL Country Y is the difference in

RPL from a country X to Country Y. ∆RPL Country Y measures the openness of trade from

of country X towards Y. Below is the outputs for each country:

Table V.4Cointegration Parameters

Equilibrium error for Openness to Japan na -1.23 *** -1.31 ***

Equilibrium error for Openness to China -1.15 *** na -0.97 ***

Equilibrium error for Openness to Korea -0.72 ** -1.24 *** na


Dependent Variable

Independent VariableGDP (Japan) GDP (China) GDP (Korea)


boost the East Asian growth as whole, in this sense we might want to exercise its effect to

ASEAN countries. To simplify things, this paper limits the effect to ASEAN4 since these countries

have the same economic characteristics. This paper employs static panel data model for this

purpose. The panel data is analyzed annually from 1989 to 2007 which consist of ASEAN 4»s

Export, Import, Consumption, Investment, Government expenditure, GDP, and GDP of Japan,

China, Korea. The data is taken from WDI online database. The following sections provide the

analysis.

A static panel data model can be specified as follows:

(V.12)

Where: λt and η

t are time and individual specific effects respectively, x it is a vector of the

explanatory variables, (i) is the time component of the panel, (N) is the cross-section

dimension (or the number of cross-section observations), and N x T is the total number of

observations. The idea is to run the models in order to have a consistent estimator for the

β coefficients, and the model (fixed or random) choice depends on the hypothesis assumed

for the relationship between the error-term (ε it ) and the regressors (x it ). The static panel

data analysis developed in the empirical section of the paper was based on two basic panel

models, the fixed (FE) and the random (RE) effect models. Since the time periods (1989-

2007) exceed the individual observations (Indonesia, Malaysia, Thailand, Philippines) therefore

FE is considered as the most appropriate method (Nachrowi and Usman, 2008). The model

is described as follows:

, t = 1,..., Ti = 1,...,NXYitititit

+++= εηλβ

(V.13)

Where:

Yit

= GDP growth of ASEAN 4 for time t and country i

Xit

= Independent Variables (ASEAN 4 consumption growth, investment growth, government

expenditure growth, export-import growth and Japan-China-Korea GDP growth for

time t)

Wit and Z

it are dummy variables which are defined as follows:

Wit = 1 for country i, where i = Indonesia, Malaysia, Philippines, Thailand

= 0 for others

itiTTiiNtNttititeZZZWWWXY ++++++++++++= δδδγγγβα .......

221122111


Zit

= 1 for Period t where t = 1989, 1990..., 2007

= 0 for others

The above structural equation is actually a simultaneous equation in which employs

causality relationship. To see the simultaneity, the above model can be decomposed into four

parts:

(V.14)

(V.15)

(V.16)

(V.17)

ttttttttKGDPCGDPJGDPXGICY .......

87654321ββββββββ +++++++=

tttYCC ..

3121βββ ++= −

tttYrI ..

321βββ ++=

ttttttKGDPCGDPJGDPCEXX .....

654321ββββββ +++++=

Equation V.14 describes the effects of ASEAN 4 consumption (Ct), investment (I

t),

government expenditure (Gt), export growth (X

t) and the North East Asian GDP growth (JGDP

t,

CGDPt, KGDP

t) on ASEAN4 GDP growth (Y

t). From the model, it is clear that consumption

growth, investment growth and export growth have their own determinants that simultaneously

form the structural equation. Consumption growth (Ct) is formed by last year»s consumption

growth (Ct-1

), and the present GDP growth (Yt), Investment (I

t) on the other hand is influenced

by the interest rate (rt) and the GDP growth (C

t). It is also expected that exchange rate (EX

t),

consumption growth (Ct) and trading partners economic growth (JGDP

t, CGDP

t, KGDP

t) have

some influences on export growth (Xt) for ASEAN 4.

From the structural equation, we can divide the variables into two, endogenous and

predetermined (exogenous). The first one is treated as stochastic while the latter as non

stochasti3. To see which simultaneous model that can satisfies the need, we have to address

the identification process. If K is the number of exogenous variables within the model, k is the

number of exogenous variables within the equation and M is the number of endogenous

variable within the model, so the criteria to state whether an equation is unidentified, just

identified, or over identified are describe as follows:

If K-k < M-1, so the equation is unidentified

If K-k = M-1, so the equation is exactly identified

If K-k > M-1, so the equation is over identified

Based form the above criteria, table V.5 summarize the order condition from the system:


1 2 1 3 1 4 5 6 7 8 9t t t t t t t t tY C Y r G EX JGDP CGDP KGDP− −= Π +Π +Π +Π +Π +Π +Π +Π +Π

10 11 1 12 1 13 14 15 16 17 18t t t t t t t t tC C Y r G EX JGDP CGDP KGDP− −= Π +Π +Π +Π +Π +Π +Π +Π +Π

19 20 1 21 1 22 23 24 25 26 27t t t t t t t t tI C Y r G EX JGDP CGDP KGDP− −= Π +Π +Π +Π +Π +Π +Π +Π +Π

28 29 1 30 1 31 32 33 34 35 36t t t t t t t t tX C Y r G EX JGDP CGDP KGDP− −= Π +Π +Π +Π +Π +Π +Π +Π +Π

Table V.5Order Condition

1 Yt 6 > 2 over identified

2 Ct 9 > 1 over identified

3 It 9 > 1 over identified

4 Xt 6 > 1 over identified

No Equation Criteria Conclusion

For the case of over identified, we might want to employ two stage least squares (2SLS)

approach as an elegant way to deal with such problem. 2SLS regression analysis, as suggested

by Angrist and Imbens (1995). Below is the detailed procedure of 2SLS.

In stage one, least square regression on the reduced form equation has to take place by

which it can yields Ct-1

, Yt-1

, rt, G

t, EX

t, JGDP

t, CGDP

t, KGDP

t as the instrumental variables,

therefore all equations from V.14 up to V.17 have to be transformed into reduced form equation

as the followings:

(V.18)

(V.19)

(V.20)

(V.21)

Note: Π is (β/(1- β))

From stage one we get as the fitted values with which we can run for the second stage.

In stage two, these fitted values are then plugged in to the main equation. The last step is to

run least squares on each of the above equations to get 2SLS estimation as described below in

Table V.6.

From the output above we can conclude that the North East Asian (Japan, Korea and

China) economic growth boost the ASEAN4 economic growth, it confirms the proposition of

this paper. Investment flows, in the form of FDI, has also operated as a dominant integrating

power in East Asia as whole. Although we cannot find legitimate determinant for FDI in the

output, but it is clear that FDI is trade related in nature. With its essentially open and outward-

looking economies, the region is highly dependent on foreign investment for its economic


growth. But still, the boosting power is not as much as in the spillover effect from the giant

countries of Japan, Korea and China. Japan, in terms of GDP growth, has the biggest influence

towards ASEAN4 followed by China and Korea at the second and third place. This fact is

described by the coefficient parameter that gives the value of 0.546, 0.311 and 0.250 for

Japan, China and Korea respectively.

The ranking of influence is presumably caused by the number FDI inflows to ASEAN from

these countries as described below in Table V.7. The only bias is on China and Korea, even

though the cumulative FDI from Korea to ASEAN4 was bigger than China»s, but it does not

seem to be reflected on the ranking of influence. As for this, it is assumed that the high

economic growth rate of China had been the major contributing factor (Urata, 2008) that

Table V.6Two Stage Least Squares Regression Output


YYYYY na 0.776 *** -0.087 Na

CCCCC 0.470 *** na Na -0.64 **IIIII 0.025 na Na NaXXXXX 0.072* na Na Na


Y C I XDependent Variable

Instrumental Variable

Y (Japan)Y (Japan)Y (Japan)Y (Japan)Y (Japan) 0.546 ** na Na 2.949***

Y (China)Y (China)Y (China)Y (China)Y (China) 0.311 ** na Na 1.112 ***

Y (Korea)Y (Korea)Y (Korea)Y (Korea)Y (Korea) 0.250 ** na Na -3.760

C (-1)C (-1)C (-1)C (-1)C (-1) na 0.01 Na Na

RRRRR na na 0.137 Na

Y (-1)Y (-1)Y (-1)Y (-1)Y (-1) na na Na Na

EXEXEXEXEX na na Na 0

GGGGG 0.122** na Na Na

Table V.7FDI flows to ASEAN 4 (US$ million)

Japan 288.06 8,096.02 4,761.11 3,055.68 16,200.87

Korea 331.88 235.58 98.51 238.13 904.1

China -36.78 50.16 120.72 4.07 138.17

Source: ASEAN secretariat

Host Country Indonesia Thailand Malaysia Philippines

Source Country

Total Cummulative1995-2003


overtook the influence of Korea»s cumulative FDI flow to ASEAN4. However, such factor is not

enough to surpass3 Japan»s influence to ASEAN4»s economic growth since Japan»s FDI

contribution to ASEAN4 outweighed China»s by more than one hundred folds.

The story goes hand in hand with the flying-geese hypothesis that was developed by

Japanese economist, Kaname Akamatsu (1935). This model has been frequently proposed to

examine the patterns and characteristics of East Asian economic integration. ≈The premise of

the flying-geese pattern suggests that a group of nations in this region are flying together in

layers with Japan at the front layer. The layers signify the different stages of economic

development achieved in various countries∆ (Xing, 2007). In the flying-geese model of regional

economic development, Japan as the leading goose leads the second-tier geese (China, Korea)

which, in their turn, are followed by the third-tier geese (ASEAN4).

Another important thing to note is the low significant value of exports within ASEAN4 in

terms of creating GDP growth. These are intriguing facts since export is considered as the main

determinant of GDP growth. It is suspected that the effect of rivalry between ASEAN4 members

and China is the main factor which creates insignificant value. This factor is supported by Holst

and Weiss (2004) that pointed out China»s emergence for creating short and medium term

direct and indirect competition between ASEAN and China. They argued that ASEAN and

China are experiencing intensified export competition in prominent third markets. This can

lead to painful domestic structural adjustments within the ASEAN in the short run. Then again

the mind set in viewing the economic opportunity or threat depends on whether China»s economy

is perceived as complementary or competitive vis-à-vis individual ASEAN economies and on

whether the latter economies are able to exploit their complementary opportunities and

overcome the competitive threats.

Chia (2006) argued that ≈the differences in resource and factor endowments, production

structures, and productivities lead to a complementary relationship, whereas similarities in these

areas lead to a competitive relationship.∆ In the long run, regionalism is expected to

accommodate welfare for East Asia. The growing significance of China market for ASEAN will

serve as the basis for regionalism. Thus, a unified East Asia could accelerate the momentum of

overall trade liberalization and boost regional economic growth.

3 From the ECM simulation as confirmed earlier, we found that China has taken over Japan»s role in East Asia. But this is true if weaddress the long run effect. This section only measures the present condition in the absence of the intertemporal problem.


V. THE FUTURE OF EAST ASIAN REGIONALISM (EAR)

The next task is to shape the future of EAR, but then will the future exist? In part C of this

paper, we measure the trend toward openness vis a vis regionalism by using ECM for the RPL

index in North East Asia (CJK). Since we include two sub regions, the best way to measure it is

by using test of convergence of the term of trade for CJK and ASEAN4. The notion of convergence

implies that differences between the series must follow a stationary process (Bernard & Durlauf,

1996; Oxley & Greasley, 1995). Thus, stochastic convergence implies that income differences

among countries cannot contain unit roots.

Following Bernard and Durlauf (1995), stochastic convergence occurs if the differential

log trade system, yt, follows a stationary process, where y

t = ASEAN4tot

t - CJKtot

t, where

ASEAN4tott is the logarithm term of trade of ASEAN4, and CJKtot

t is logarithm term of trade

of CJK, and both series are in the first difference (I(1)). Stochastic convergence is tested by

using the conventional augmented Dickey-Fuller (ADF) regression which shows a significance

result in proving stationarity for yt (see Table V.8). This indicates long-run convergence between

the two trading systems.

Table V.8ADF Test for Term of Trade

ADF Test Statistic -3.519465 1% Critical Value* -3.7204

5% Critical Value -2.9850


*MacKinnon critical values for rejection of hypothesis of a unit root.

A major drawback of the standard ADF unit root test procedure is that the power of the

test is quite low. To overcome this problem, the paper utilizes cointegration test as suggested

by Baharumshah et al. (2007). The following is the Engle Granger Cointegration:

Table V.9ADF Test for Cointegration Residual

ADF Test Statistic -5.623714 1% Critical Value * -3.7204



*MacKinnon critical values for rejection of hypothesis of a unit root.

Ut = ASEANtott − βο − β1CJKtot

t (V.22)


Open = α + βXit + γ1W1t + γ2W2t + γ3W3t + ... + γNWN + δ1Zi1 + δ2Zi2 + δ3Zi3 + ... + δtZiT + eit

(V.23)

The residual ( U

t ) gives stationary result (see Table V.9) which means that the two regions

have long run relationship (convergence). It is worth to say that with the test of convergence,

EAR will be there to stay in the long run. The robust finding surely creates optimistic view for

EAR. But knowing the future is not enough, we still need to find out the clear path to reach the

future. The next section serves to give the answer.

Feng and Genna (2003) argued that homogeneity of domestic institutions is needed to

go hand in hand with the regional integration process. Moreover, they pointed out inflation,

taxation and government regulation as representing factors for the economic institutions.

Another variable that might enhance integration is population as already identified by Tamura

(1995). He argued that large population is a catalyst for integration due to economic

agglomeration. Scholars like Milner and Kubota (2005) even pointed out democracy as an

important factor that could foster regionalism. Their empirical work on the developing countries

from 1970-1999 showed that regime change toward democracy was associated with trade

liberalization, and regionalization.

Given those works, this paper tries to combine the variables into one complete model

that can determine the formation of EAR. The formula as follows:

Where:,

Openit = Regionalism for time t and country i

Xit = Independent Variables (ASEAN4 + CJK»s rail ways, tax, democracy, governance,

industry, gross school enrolment rate, inflation and population)

Wit and Zit are dummy variables which are defined as follows:

Wit = 1 for country i, where i = Indonesia, Malaysia, Philippines, Thailand China, Japan,

Korea, otherwise 0.

Zit = 1 for Period t where t = 1998, 2000..., 2007, , otherwise 0.

The paper employs fixed effect model to estimate the variables. The followings are the

explanations for the variables used:

i) The paper use the proxy of trade openness (net export per GDP) for regionalism. The variable

of openness is used to represent regionalism since regionalism creates openness to some

sectors of economy. Openness here functions as dependent variable that is determined by

some independent variables.


ii) Railways as goods transported (million ton-km) is used to explain physical infrastructure

readiness. Pairing up with this variable is the gross school enrolment rate which serves as

the basic for human capital infrastructure. Sound infrastructure (both physical and human)

will provide steadiness and assuredness in making investment among members. In other

words, good infrastructure will only lead to a sustainable intra trade and investment that

serve as the basis of EAR.

iii) To measure democracy, the indices produced by Freedom House (2000) that is the index of

democracy called POLITY. Democratization is expected to open up new avenues of support

for freer trade vis-à-vis regionalism.

iv) Moving to the next variable is the taxation policy, the higher the rate the more it will diminish

the prospects of EAR

v) Other variable that also matters is governance which is measured by the six governance

indicators estimated by Kaufmann (2003). These indices describe various aspects of the

governance structures of a broad cross section of countries, including measures of Voice

and Accountability, Political stability, Government Effectiveness, Regulatory Quality, Rule of

Law, and Control of Corruption. In general, the Governance index provides explanatory

power to explain the capability and quality of governance from each member country. The

better indicator a country has the more it has the chance to capitalize regionalism.

vi) Macroeconomic variable which is represented by inflation creates ambiguous expectation.

High inflation might deter the formation of EAR since the very beginning but some scholars

prove the other way aroun4. One of argument that supporting the latter proposition is

given by Cohen (1997) who argued that the inflationary policy (high inflation) resulting

from the government action will tend to raise the obstacle to private investors which in turn

demand for greater integration. The loss of discretion in the fiscal and monetary policy will

then reduced the risk of uncertainty. vii) Large market together with the ongoing

industrialization process sums up the last aspects of EAR formation. The sheer size of the

East Asian population creates not only the potential demand for the goods traded in the

region but also the supply of labor force and the low absolute level of wages. In other

words, Lewis»s unlimited supply of labor will persist longer in East Asia. The process will lead

to an upward trend towards industrialization (value added as percentage from GDP) in the

region. The trend is very important since homogeneity in industrialization among countries

in the region will smooth the progress of EAR.

The results shows us that Economic and political factors such as Infrastructure (railways

and gross education), governance, taxation policy, industrialization and Democracy have

significant effect towards Regionalism (Openness) in East Asia while Inflation gives insignificant

role.



Table V.10.Factors Affecting Openness

LOG (RAILWAYS) 0.115860 2.059379**TAX -0.029831 -3.530943***DEMOCRACY -0.004282 -2.051852**GOVERNANCE 0.257508 3.860438***INDUSTRY 0.049930 4.861010***LOG (POPULATION) 0.863634 2.154852**GROSS EDUCATION 0.011445 2.217493**INFLATION -0.001545 -0.441719R-squared 0.99251Adjusted R-squared 0.98975

Dependent Variable : OPENNES

Independent VariableIndependent VariableIndependent VariableIndependent VariableIndependent Variable CoefficientCoefficientCoefficientCoefficientCoefficient t-Statistict-Statistict-Statistict-Statistict-Statistic

The signs of coefficient for railways, gross education, governance, and industrialization

are positive which mean the bigger the variable the more they create Openness. The negative

sign of the coefficient for tax describes the opposite relation between corporate tax rate and

the future prospect of EAR, the higher the rate the more it will the deteriorate the EAR. The

negative sign of democracy is against expectation but it is still rational since democracy is still

finding its form in East Asia. We have to define what democracy really means in order to

make it works. The insignificant role of inflation for EAR is expected due to the ambiguity

given.

VI. CONCLUSION

We have made an interim conclusion that export leads the overall growth in North East

Asia. However, it is important to note that Japan»s phase of adjustment towards long run

equilibrium is quite slow compared to the likes of Korea and China. This only yields as a stumbling

block in forming regionalism in East Asia. The hard task is about making these countries move

together in the same phase, which is why regionalism has to take place.

Since regionalism is an abstract term, the use of RPL index is essential. RPL index is a

proxy of outward orientation of a country or in other words it is a representation of regionalism.

Regionalism in this case goes hand in hand with openness in which it creates trade arrangements

that liberalize some sectors in the economy. The ECM simulation gives a clear picture of the

current form of openness which is below the equilibrium. It suggests that the trend towards


regionalism is still far behin4. It somewhat confirms the ineffectiveness of current triangular

trade in North East Asia. It is expected that regionalism can eliminates such bias in trade.

Moreover, since North East Asian countries has a big scale of economy, its economic

development will substantially affect the neighboring countries in East Asia specifically ASEAN4.

It is demonstrated by the large share of China-Japan-Korea growth that affects ASEAN4»s GDP.

In the short run, there is a rivalry competition between China and ASEAN. However, in

the long run regionalism is expected to accommodate export growth for East Asia as whole. In

a sense of creating integration in East Asia, there is a need to set up more formal institutional

mechanisms for trade. It is rational for such mutually dependent countries in the region to

institutionalize de facto integration through the establishment of regional arrangements (Kawai,

2005). The growing significance of China, Japan and Korea market for ASEAN4 will then serve

as the basis for a single East Asian Wide FTA. The next task is to shape the future of EAR, but

then will the future exist? Using the test of convergence, it is found that EAR will be there to

stay. The robust finding surely creates optimistic view for EAR. But knowing the future is not

enough, we still need to find out the clear path to reach the future. What are the paths then?

From a static panel data simulation it is found that sound physical infrastructure, good

governance, inflation, competitive taxation policy, sizeable market and the trend towards

industrialization are the main factors that serve as building blocks for EAR.

To wrap up, EAR will enable the region to cope with the future challenges of globalization

and remain internationally competitive. An integrated East Asia would lead to the advancement

in economies of scale, fuller development of production networks. Moreover, Chia (2007) stated

that EAR could help the less developed East Asian economies which would otherwise become

marginalized as they lack the attraction of sizeable market and lack negotiating resources.


Akamatsu, Kaname. (1935) «Wagakuni yomo kogyohin no susei [Trend of Japanese Trade in

Woolen Goods], Shogyo Keizai Ronso», Journal of Nagoya Higher Commercial School, Vol.

13, pp. 129-212.

Angrist, J. D, & Imbens, G. W. (1995) «Two-stage least squares estimation of average causal

effects in models with variable treatment intensity», Journal of the American Statistical

Association, Vol. 90, pp. 431-442.

Arellano, M. (1995) «On the testing of correlated effects with panel data», Journal of Econometrics,

Vol. 59, pp. 87ƒ97.

Bernard, A. 2., & Durlauf, S. N. (1995) «Convergence in international output». Journal of Applied

Econometrics, Vol. 10, pp. 97√108.

Chan, Sarah and Chun-Chien Kuo. (2005) «Trilateral Trade Relations among China, Japan and

South Korea:Challenges and Prospects of Regional Economic Integration», Journal of East

Asia, Vol. 22, pp. 33-50.

Chia, Siow Yue. (2006) «ASEAN-China Economic Competition and Free Trade Area», Asian

Economic Papers, Vol. 4, pp. 109-147

ƒƒƒƒƒƒƒƒƒƒ. (2007) ,Challenges and Configurations of a Region-wide FTA in East

Asia», FONDAD Conference.

Cohen, Benjamin J (1997) «The Political Economy of Currency Regions», in Helen Milner and

Edward Mansfield (eds) The Political Economy of Regionalism, New York: Columbia University

Press.....

Dollar, Davi4. (1992) «Outward Oriented Developing Economies Really Do Grow More Rapidly:

Evidence From 95 LDCs, 1976-85», Journal of Economic Development and Cultural Change,

Vol. 4, pp. 523-544.

Doraisami, Anita. (1996) Export Growth and Economic Growth: A Reexamination of Some

Time-Series Evidence of the Malaysian Experience «, Journal of Developing Areas, Vol. 30,

pp. 223-230.

Ekanayake, E.M. (1999) «Export and Economic Growth in Asian Developing Countries:

Cointegration and Error-Correction Models», Journal of Economic Development, Vol. 24,

pp. 43-56.

REFERENCES


Engle, R.F. and 3.W.J. Granger. (1987) «Cointegration and Error Correction: Representation,

Estimation and Testing», Econometrica, Vol. 55, pp. 251-76.

Feng, Yi and Gaspare M. Genna. (2003) Regional integration and domestic institutional

homogeneity: a comparative analysis of regional integration in the Americas, Pacific Asia

and Western Europe», Review of International Political Economy, Vol. 10, pp. 223-230.

Frankel, Jeffrey A. and David Romer. (1999) «Does Trade Cause Growth?, The American Economic

Review, Vol. 89, pp. 379-399.

Harrison, Ann. (1996) «Openness and Growth: A Time Series, Cross Country Analysis for

Developing Countries», Journal of Development Economics, Vol.48, pp. 419-447.

Harvie, Charles and Hyun Hoon Lee. (2002) «New Regionalism in East Asia: How Does It Relate

to the East Asian Economic Development Model», University of Wollongong Department of

Economics, Working Paper Series.

Holst, David Roland and John Weiss. (2004) «ASEAN and China: Export Rival or Partners in

Regional Growth?», Blackwell Publishing Lt4.

Kaufmann, Daniel, Aart Kraay and Massimo Mastruzzi. (2003) «Governance Matters III:

Governance Indicators for 1996-2002», World Bank Policy Research Department Working

Paper.

Kawai, Masahiro, (2005) East Asian Economic Regionalism: Progress and Challenges, Asian

Economics, Vol. 16, pp. 29-55.

Kawai, Masahiro and Ganeshan Wignaraja. (2007) «Regionalism as an Engine of Multilateralism:

A Case for a Single East Asian FTA», ADB Working Paper series on Regional Economic

Integration no.14.

Love, Jim and Ramesh Chandra. (2004) «An Index of Openness and its Relationship with Growth

in India», The Journal of Developing Areas, Vol. 38, pp. 37-54

Milner Helen V and Keiko Kubota. (2005) «Why the Move to Free Trade? Democracy and Trade

Policy in the Developing Countries,» International Organization, Vol. 59, pp. 107-143.

Nachrowi,Djalal. (2007) «Ekonometrika Untuk Analisa Ekonomi dan Keuangan [Econometrics

for Economic and financial analysis]», Faculty of Economics University of Indonesia.

Oxley, L., & Greasley, 4. (1995) «A time-series perspective on convergence: Australia, UK and

USA since 1870». The Economic Record, Vol. 71, pp. 259√270.

Summers, R. and A. Heston. (1988) «A New Set of International Comparisons of Real Product

and Price Levels: Estimates for 130 Countries, 1950-1985», Review of Income and Wealth,

Vol. 34, pp. 1-25.

Stubbs, Richar4. (2002) «ASEAN PlusThree: emerging East Asian Regionalism?», Asian Survey,

Vol. 42, pp. 440-455.


Tamura, Robert. (1995) «Regional economies and market integration», Journal of Economic

Dynamics and Control, Vol. 20, pp. 825-845.

Urata, Shujiro and Kozo Kiyota. (2003) «The Impacts of an East Asia Free Trade Agreement on

Foreign Trade in East Asia», NBER Working Paper Series 10173, National Bureau of Economic

Research, Cambridge.

Yoshida, Tadahiro. (2004) «East Asian Regionalism and Japan», IDE APEC STUDY CENTER Working

Paper Series.

Watanabe, Yorizumi. (2008) «Economic Partnership Agreement (EPA) of Japan and Economic

Integration in Northeast Asia», Academic presentation, Graduate School of Media and

Governance, Keio University.

Xing, LI. (2007) «East Asian Regional Integration: From Japan-led ≈Flying-geese∆ to China-

centred ≈Bamboo Capitalism∆», ∆», ∆», ∆», ∆», CCIS Research Series Working Paper No.3.

125

WRITING GUIDANCE

1. The paper should be original and should not violate any copyrights. The submitted paper

should have never been published or not being submitted to other publisher. The copyright

of the published paper is retained to the author.

2. The Bulletin of Monetary Economics and Banking provide a financial incentive between IDR

1,000,000 to IDR 3,000,000.

3. The paper should be sent in 2 formats (1) Microsoft Word (*.doc) and (b) portable digital file

(*.PDF). The 2 files should be sent to the following mail address:

[email protected] and Cc to: [email protected]

You may save your files in CD and send it to the following Editorial Address:

BULLETIN OF MONETARY ECONOMICS AND BANKING

Directorate of Economic Research and Monetary Policy-Bank Indonesia

Building Syafruddin Prawiranegara, 20th Floor

JI. M. H. Thamrin No.2 Central Jakarta, Indonesia

Ph. +62-21-3818202, Fax. +62-21- 3800394

4.4.4.4.4. To avoid missing fonts or other compatibility issue, any special characters or mathematicalTo avoid missing fonts or other compatibility issue, any special characters or mathematicalTo avoid missing fonts or other compatibility issue, any special characters or mathematicalTo avoid missing fonts or other compatibility issue, any special characters or mathematicalTo avoid missing fonts or other compatibility issue, any special characters or mathematical

expression (equations, symbol, matrix, etc.) must be written using Microsoft Equation.expression (equations, symbol, matrix, etc.) must be written using Microsoft Equation.expression (equations, symbol, matrix, etc.) must be written using Microsoft Equation.expression (equations, symbol, matrix, etc.) must be written using Microsoft Equation.expression (equations, symbol, matrix, etc.) must be written using Microsoft Equation.

5. The submitted paper should contain (i) an abstract of maximum one page A4, (ii) keywords

and (iii) JEL classification code. See the JEL code at http://www.aeaweb.org/journal/

jel_class_system.html.

6. The paper must contain the followings:

The background, the aim of the paper and its distinction to previous study

Theory and review of literatures

Methodology (quantitative methodology is preferred)

Result and analysis

Policy and further study implication

7. The citation should be in footnote and not in endnote.

8. The reference must obey the following rule:

126 Bulletin of Monetary Economics and Banking, July 2010

a. Book:

John E. HankeJohn E. HankeJohn E. HankeJohn E. HankeJohn E. Hanke and Arthur G. ReitschArthur G. ReitschArthur G. ReitschArthur G. ReitschArthur G. Reitsch, (1940), Business Forecasting, Prentice-Hall, New

Jersey.

b. Article in journal:

Rangazas, PeterRangazas, PeterRangazas, PeterRangazas, PeterRangazas, Peter. ≈Schooling and Economic Growth: A King-Rebelo Experiment with

Human Capital∆, Journal of Monetary Economics, October 2000, 46(2), page. 397-416.

c. Article in book edited by other people:

Frankel, Jeffrey AFrankel, Jeffrey AFrankel, Jeffrey AFrankel, Jeffrey AFrankel, Jeffrey A. and Rose, Andrew KRose, Andrew KRose, Andrew KRose, Andrew KRose, Andrew K. ≈Empirical Research on Nominal Exchange Rates∆,

in Gene Grossman and Kenneth Rogoff, eds.,∆Handbook of International Economics.

Amsterdam: North-Holland, 1995, page. 397-416.

d. Working papers:

Kremer, MichaelKremer, MichaelKremer, MichaelKremer, MichaelKremer, Michael and Chen, DanielChen, DanielChen, DanielChen, DanielChen, Daniel. ≈Income Distribution Dynamics with Endogenous

Fertility∆. National Bureau of Economic Research (Cambridge, MA) Working Paper

No.7530, 2000.

e. Mimeo or unpublished work:

Knowles, JohnKnowles, JohnKnowles, JohnKnowles, JohnKnowles, John. ≈Can Parental Decision Explain U.S. Income Inequality?∆, Mimeo,

University of Pennsylvania, 1999.

f. Article from web or other electronic form:

Summers, RobertSummers, RobertSummers, RobertSummers, RobertSummers, Robert and Heston, Alan WHeston, Alan WHeston, Alan WHeston, Alan WHeston, Alan W. ≈Penn World Table, Version 5.6∆

http://pwt.econ.unpenn.edu/, 1997.

g. Article in newspaper, magazine or equal periodicals:

Begley, SharonBegley, SharonBegley, SharonBegley, SharonBegley, Sharon. ≈Killed by Kindness∆, Newsweek, April 12, 1993, page. 50-56.

9. The paper should be submitted along with curriculum vitae complete with mail address and

phone number.

Documents

BULLETIN OF MONETARY ECONOMICS AND …. Dr. Iwan Jaya Azis Prof. Iftekhar Hasan Dr. M. Syamsuddin ... Bagus Arya Wirapati, Niken Astria Sakina Kusumawardhani Making East Asian Regionalism