My...MeMoOoOo

Exploratory Study on R9 Trip

2010-05-17T17:10:00.005+02:00

This survey is along the route No.9 on East West economic corridor which links Thailand, Myanmar, Laos and Vietnam together.

Exploratory Study on R3A Trip

2010-03-29T08:40:00.002+02:00

The study focuses on an investigation on the R3A economic potential including trade and investment. The exploratory areas cover LuangNamta and Boten in Myanmar, and Bohan, ChiangRung and Kunming in Yunnan (Southwestern China).

Exploratory Study on R3B Trip

2010-03-29T07:17:00.010+02:00

The study focuses on an investigation on the R3B economic potential including trade and investment. The exploratory areas cover Tachilek, MuengLa and ChiangThung in Myanmar.

Les Machines de l'île de Nantes

2008-06-10T17:29:00.006+02:00

The Machines of the Isle of Nantes

The Machines de l’Ile is a totally unprecedented artistic project. Born from the imaginations of François Delarozière and Pierre Orefice, it is a blend of the invented worlds of Jules Verne, the mechanical universe of Leonardo da Vinci, and the industrial history of Nantes, on the exceptional site of the former shipyards.

The Prototype Branch of the Heron Tree:

It is the preview of the major project of the Machines de l’Ile. The prototype branch was created during the first phase of the Machines de l’Ile in 2007 to verify the strength and safety of the construction, and to validate the methods used for growing plant life on the tree.

The Great Elephant:

Photo by N. Piyaareekul on June 7, 2008

This gigantic animal, 12 meters high and 8 meters wide, made from 45 tons of wood and steel, lives in the largest hangar. When the majestic elephant goes out for its walk, it is like architecture in motion coming out from a steel cathedral. The 49 passengers on board embark for an amazing journey on the Isle of Nantes (Ile de Nantes) which offers a view over the banks of the Loire river. It is a unique spectacle for everyone to enjoy.

source: http://www.lesmachines-nantes.fr/english/index.html

Jardin des plantes de Nantes

2008-06-10T14:00:00.006+02:00

Jardin des plantes de Nantais

Le Jardin des plantes de Nantes, autrement appelé jardin botanique, est un jardin public de la ville de Nantes, situé face à la gare SNCF. Il offre des collections de plantes rares, des carrés de plantes médicinales, de nombreuses variétés de camélias, de serres à cactées. Notamment, les serres constituent de précieuses collections, traitées de manière écologique, sans engrais ni pesticides.

The plant garden of Nantes, often called botanical garden, is a public garden in the city of Nantes, located opposite the railway station. It offers collections of rare plants, squares of medicinal plants, many varieties of camellias, greenhouses' cacti. In particular, the greenhouses are valuable collections, treated as environmental, without fertilizers or pesticides.

Photo by N. Piyaareekul on June 10, 2008

Grande Parade Nautique

2008-06-05T15:59:00.010+02:00

Les Rencontres du fleuve édition 2008

Pour sa 2ème édition, le festival itinérant qui allie arts et environnement animera 6 ports du département du 5 au 15 juin.

Varades, la Chapelle Basse-Mer, Nantes, Couëron, Cordemais et Paimbœuf vous invitent à rejoindre leurs rives pour vous conter l'histoire du dernier fleuve sauvage d'Europe. Autant de chances de valoriser le patrimoine maritime et de découvrir les richesses de la Loire. Musique, théâtre, cinéma et spectacles exceptionnels vous guideront dans ce voyage singulier.

GRANDE PARADE NAUTIQUE

Nantes - 7 et 8 juin - Chantiers navals à partir de 12h30

Au cœur du site historique des chantiers navals

Carrefour des cultures, confluence des eaux douces et salées, berceau des chantiers navals, Nantes est l’occasion de voir toute la diversité des bateaux qui en ont fait la richesse. Devant les nefs restaurées, les vikings de Thorvald établiront leurs tentes pendant que toutes les voiles carrées croiseront les voiles rouges auriques des anciens pêcheurs de l’Atlantique.

GAMS: Introduction

2008-06-03T17:27:00.008+02:00

Introduction to General Equilibrium Modeling with GAMS/MPSGE

MPSGE is a mathematical programming system for general equilibrium analysis which operates as a subsystem within GAMS. MPSGE simplifies the modeling process and makes AGE modeling accessible to any economist who is interested in the application of these models. In addition to solving specific modeling problems, the system serves a didactic role as a structured framework in which to think about general equilibrium systems.

This course is intended for students who want to use numerical modeling for simulation analysis of theoretical models. The course will provide an introduction to applied (numerical) general equilibrium (GE) models constructed using MPSGE with GAMS. We recommend, however, that students should not only be familiar with first year graduate courses in microeconomics and macroeconomics but also with GAMS programming.

Part 1: Introduction

Software installation
Rudiments of GAMS programming

Part 2: Getting started with applied general equilibrium

The conceptual framework
Economic principles underlying the formulation of numerical GE models
An introduction to the complementarity format

Part 3: Getting Started with MPSGE

An introduction to MPSGE: A non-algebraic language for GE modelling
A specialized modeling language for Arrow-Debreu equilibrium modelling: motivation, simple example of model building

Part 4: General Equilibrium Modeling under MPSGE: Part 1

The basic MPSGE of standard Arrow-Debreu models with perfect competition
Incorporation of activity analysis
Government policy : tax policy analysis (e.g., equal yield tax reforms)
Different households (equity issues)

Part 5: General Equilibrium Modeling under MPSGE: Part 2

Open economy models with tariffs and quotas
Single-country models
Multi-regional trade models with biltateral trade flows

Part 6: Application

Open economy analysis: exercises with trade liberalization and integration policies

GAMS: Definition

2008-06-03T17:12:00.005+02:00

The General Algebraic Modeling System (GAMS)

GAMS is a high level modeling language for formulating models with concise algebraic statements that are easily read by modelers. The GAMS language is formally similar to commonly used programming languages. It is therefore familiar to anyone with programming experience.

Using GAMS, data are entered only once in familiar list and table form. Models are described in concise algebraic statements which are easy for both humans and machines to read. Whole sets of closely related constraints are entered in one statement. GAMS automatically generates each constraint equation, and lets the user make exceptions in cases where generality is not desired. Statements in models can be reused without having to change the algebra when other instances of the same or related problems arise. The location and type of errors are pinpointed before a solution is attempted. GAMS handles dynamic models involving time sequences, lags and leads and treatment of temporal endpoints.

Structure of GAMS programs:

GAMS programs consist of one or more statements that define data structures, initial values,data modification,and symbolic relationships (equations). While there is no fixed order in which statements have to be arranged, the order in which data modifications are carried out is important. Symbols must be declared as to type before they are used, and must have values assigned before they are used. Each statement is followed by semicolon except the last statement, where a semicolon is optional.

A GAMS program can be organized in three parts:

DATA part with

set declarations and definition;
parameter declaration and definition;
assignments;
displays. variable declarations;

MODEL part with

equation declarations;
equation definitions;
model definitions.

SOLUTION part

solve statement;
display statements.

The two most common ways of organizing GAMS programs result by change above order inter MODEL part and DATA part.

Clasifications of GAMS statements

Each statement in GAMS is classified into one of two groups:

declaration and definition statements which describe the class of symbol. Often initial values are provided in declaration, and then it may be called a definition. The specification of symbolic relationships for an equation is a definition.
execution statements are instructions to carry out actions such as data transformation, model solution, and report generation.

Data types and definition

sets; parameters; variables; equations; models.

Set definitions

Sets are fundamental building blocks in any GAMS model. There are some more advanced set concepts, such as assignments to sets as well as lag and lead operations. GAMS language have simple sets, dinamic sets with multiple indices, ordered and unordered sets. There are set operations : union, intersection, complement, difference.

Tables

Tabular data can be declared and initialized in GAMS using table statement. For 2 si higher dimensional parameters this provides a more concise and easier method of data entry than the list based approach, since each label appears only once 9at least in small tables).

Functions

GAMS provides commonly used standard functions such as exponentiation, and logarithmic, and trigonometric functions.

References

GAMS - A Users Guide Tutorial by Richard E. Rosenthal, 2008
A GAMS Tutorial by Richard E. Rosenthal

Related topics:
Introduction

SE: Philosophy of the "Sufficiency Economy"

2008-06-03T17:06:00.004+02:00

Philosophy of the "Sufficiency Economy"
by the King of Thailand, His Majesty King Bhumibol Adulyadej

"Sufficiency Economy" is a philosophy that stresses the middle path as the overriding principle for appropriate conduct by the populace at all levels. This applies to conduct at the level of the individual, families, and communities, as well as to the choice of a balanced development strategy for the nation so as to modernise in line with the forces of globalisation while shielding against inevitable shocks and excesses that arise. "Sufficiency" means moderation and due consideration in all modes of conduct, as well as the need for sufficient protection from internal and external shocks. To achieve this, the application of knowledge with prudence is essential. In particular, great care is needed in the utilisation of untested theories and methodologies for planning and implementation. At the same time, it is essential to strengthen the moral fibre of the nation, so that everyone, particularly public officials, theorists and businessmen, adheres first and foremost to the principles of honesty and integrity. In addition, a balanced approach combining patience, perseverance, diligence, wisdom and prudence is indispensable to cope appropriately with critical challenges arising from extensive and rapid socioeconomic, environmental, and cultural changes occurring as a result of globalisation.

Related topics:
Gross National Happiness

GMS: Rationale

2008-06-03T17:05:00.004+02:00

The Greater Mekong Subregion (GMS) comprises Cambodia, the People's Republic of China, Lao People's Democratic Republic, Myanmar, Thailand, and Viet Nam. Since 1992, the countries of the Greater Mekong Subregion (GMS) have embarked on a program of economic cooperation (the GMS Program) that aims to promote development through closer economic linkages. The GMS Program, with support from ADB and other donors, helps the implementation of high priority subregional projects in transport, energy, telecommunications, environment, human resource development, tourism, trade, private sector investment, and agriculture. The program has contributed to the development of infrastructure to enable the development and sharing of the resource base, and promote the freer flow of goods and people in the subregion. It has also led to the international recognition of the subregion as a growth area.

Related topics:
Cross-border Investment between Thailand and Southern China

GMS: Cross-Border Investment between Thailand and Southern China

2008-06-03T16:59:00.006+02:00

I make a contribution to the evolution of the economies of Thailand and Southern China and give my thoughts on the term cross-border investment which is the important theme nowadays.

The economies of Thailand and Southern China have achieved rapid economy growth over the recent years. The consecutive successes in economic development in them should be lauded as the "Sibling relationship Miracle". Indeed, Thailand and Southern China also joined this sibling relationship miracle a bit later. The program of economic cooperation of Greater Mekong Subregion (GMS) was a formal starter and a catalyst of such economic development.

The one catalytic role of GMS program has been made through the multinational enterprise (MNE) promotion. The notable expansion of cross-border investment into the region was driven by the MNEs, and investments. It, in turn, induces trade in manufacturing goods.

As known that such cross-border investment stimulated trade particularly intra-industry trade in manufacturing products, this region's engagement in foreign trade further stimulated FDI activities. The term of cross-border trade and investment nexuses captures the important dynamics of the economic development in the region. Such multinational activities are supported by the efforts to promote cross-border trade and investment under the GMS framework as well as increasing number of cooperative agreements.

The purposes of this study are to investigate determinants of cross-border investment between Thailand and Southern China, to examine supply-side impediments that discourage cross-border investment in the region, and to provide problem-solving strategies from the viewpoints of investors.

Related topics:
Rationale

Le journal du net

2008-06-03T14:16:00.004+02:00

To be continued.

DOCF: Abstract

2008-06-03T13:50:00.003+02:00

Impacts of Third-Country Effects and Regional Integration on FDI : An Application to ASEAN
July 2008

This work studies two distinctive issues that concern the theoretical and empirical analyses of foreign direct investment (FDI) in the Association of Southeast Asian Nations (ASEAN) economies. In the first part we extend the theoretical framework proposed by Baltagi et al. (2005) in the context of determinants of FDI and host-country effects of FDI. In order to obtain the theoretical hypotheses to analyze empirical evidence, the comparative-static analysis and numerical simulation are conducted. In the second part we empirically investigate the effects of changes in the determinants of FDI in the ASEAN using panel data techniques. To receive consistent results, different methods which are commonly used for recent estimation are applied for this study. This unique research topics on FDI is meant to serve as a guide to understand the patterns and impacts of FDI which still need further exploration because of the lack of robust results.

Key words: Foreign direct investment, Regional integration, Third-country effects, ASEAN region, Panel data analysis, Time-series analysis

JEL classification: F15, F21, R12, C22, C23

SE: Gross National Happiness

2008-06-03T13:40:00.006+02:00

Concept of the "Gross National Happiness*"

The ideology of Gross National Happiness (GNH) is regarded as the Bhutanese Phylosophy of development. The concept of GNH was first pronounced by His Majesty Jigme Singye Wangchuck, the King of Bhutan in his speeches to the people soon after acceding to the throne in 1972. The four pillars of GNH are the promotion of equitable and sustainable socio-economic development, preservation and promotion of cultural values, conservation of the natural environment, and establishment of good governance. GNH is a qualitative indicator (in contrast to gross national products, GNP) that attempts to quantify well-being and happiness. In other words, the well-being is more relevant and important than consumption.

Note: * Available keynote address concerning the philosophy of GNH by the prime minister of Bhutan at http://www.bhutanstudies.org.bt/index.htm.

Related topics:
Philosophy of the "Sufficiency Economy"

IE: Course description

2008-06-03T13:30:00.003+02:00

Principles of international trade; transaction cost; balance of payment. Adjustments; foreign exchange markets; international trade policy and economic organizations. A study of issues, theories and policies regarding international trade and finances, international movements of capital and labor, trade and development, and external debt and foreign aid.

Referee's comments

2008-06-02T22:20:00.002+02:00

There are 10 important referee's comments in which researchers should be aware before submitting the paper to the editor.

1. Significance of Themes

Is this a topic that needs addressing? Is the area investigated by the paper: timely? important? in need of addressing because it has been neglected? intrinsically interesting? filling a gap in current knowledge? (The paper does not have to be all of these things to be significant; it is sufficient to measure it against one of these forms of significance.)
By addressing these themes, does this paper make a useful contribution? Is it itself significant?

2. Relevance of Themes

Are these themes relevant to this publication? If not, is there a more appropriate place for publication?

3. Clarity of Thematic Focus

Are the author’s themes clearly stated?
Does the paper follow through by addressing these themes, consistently and cogently?

4. Relationship to Literature

Does the paper demonstrate an adequate understanding of the current literature in the field?
Does it connect with the literature in a way which might be useful to the development of our understanding in the area it addresses?

5. Research Design and Data

Has the research, or equivalent intellectual work upon which the paper is based, been well designed?
Does the paper demonstrate adequate use of evidence, informational input or other intellectual raw materials in support of its case?

6. Data Analysis and Use of Data

Has the interpretative potential of the data been adequately realised?
Has the data been used effectively to advance the themes that the paper sets out to address?

7. Use of Theory

Does the paper use theory in meaningful way?
Does it develop or employ theoretical concepts in such a way as to make plausible generalizations?

8. Critical Qualities

Does the paper demonstrate a critical self-awareness of the author’s own perspectives and interests?
Does it show awareness of the possibility of alternative or competing perspectives: such as other cultural, social, political, theoretical or intellectual perspectives?
Does it show an awareness of the practical implications of the ideas it is advancing?

9. Clarity of Conclusions

Are the conclusions of the paper clearly stated?
Cohesiveness of paper: do the conclusions adequately tie together the other elements of the paper (such as theory, data and critical perspectives)?

10. Quality of Communication

Does the paper clearly express its case, measured against the technical language of the field and the reading capacities of an academic, tertiary student and professional readership?
What is the standard of the writing, including spelling and grammar? If you will be recommending publication with revisions, please make specific suggestions or list errors.

EOF: Abstract

2008-06-02T22:00:00.002+02:00

Productivity Spillover Effects of FDI in ASEAN
May 2008

Abstract

The purpose of this paper is to shed light on the effects of inward foreign direct investment on productivity in the ASEAN economies. The main novelty is the analysis of the spillover effects of inward FDI that may occur from the entry of multinational enterprises and the regional economic integration. In doing so, the causality between FDI and total factor productivity growth was taken into account. The causality analysis was made in five ASEAN economies using country-specific historical data from 1970 to 2004. We provided a variety of analytical testing, including unit root testing, cointegration testing, and Granger causality testing. The results from the Granger causality testing proposed by Toda-Yamamoto (1995) showed that there was strong evidence that FDI inflows caused the TFP growth in Indonesia, Malaysia, Philippines, Singapore and Thailand. That means the change in FDI inflows induced the productivity spillovers to domestic firms.

JEL codes: C12, D24, F15, F21

Key Words: Foreign direct investment, Total factor productivity, ASEAN region, Granger causality

Outline:
Abstract Introduction Productivity spillover Empirical analysis Conclusion Bibliography

EOF: Introduction

2008-06-02T15:00:00.002+02:00

Foreign direct investment (FDI) plays a key role in the economic growth of the Association of South East Asian Nations (ASEAN). The FDI inflows to the ASEAN have grown dramatically in the last decade, increasing about 86% from 1995 to 2005. FDI is seen as an important channel for obtaining employment opportunities, human capital development, higher wages, technology transfer, and infrastructure improvement. In other words, FDI is often seen as a vehicle for an increase in productivity and efficiency, and consequently as a driver for economic growth. Particularly, spillover effects are identified as an important channel through an increase in productivity of domestic firms. The entry of MNEs not only improves the productivity of firms that are recipients of FDI, but also spills over into the surrounding economy. The existing theoretical and empirical literatures indicate that the spillover effect of FDI will occur when the entry of MNEs makes the higher productivity growth of domestic firms in a host country. Interestingly, there seems to be a widely used mechanism for governments through which FDI promotes growth in a host country. One of the strategies which were used in attracting FDI was performed on the ASEAN Investment Area (AIA) framework agreement adopted in 1998. Subsequently, the ASEAN governments have attempted to follow the AIA framework agreement which was undertaken with a view to improve productivity, efficiency and competitiveness in the ASEAN industries. Meanwhile the ASEAN policy makers have pursued the implementation of the AIA framework agreement by hoping that FDI would play a key role in accelerating the growth of production. It will finally achieve an end-goal of ASEAN vision 2020. Although FDI can foster development in the aggregate, more attention should be focused on the productivity growth from the entry of MNEs, notably effects on productivity spillovers. Empirical studies on whether the FDI in ASEAN economies is the source of productivity spillover effects should be observed.

An issue of interest is whether FDI can contribute to the objective of increasing spillovers. More recently, the relationship between FDI and productivity spillovers has been extensively studied both theoretically and empirically. It is theoretically argued that FDI inflows have a positive relationship with horizontal and vertical spillover effects. On the other hand, there are a number of empirical studies of the relationship between FDI and the presence of productivity spillover in both developed and developing host countries. Most researches were to investigate the effects of FDI on the productivity of domestic firms based on the firm-specific data using a rigorous econometric framework. They found more empirical evidence in favor of vertical spillovers rather than horizontal spillovers. And yet a limited number of empirical results came up with a clear answer to the question whether domestic firms benefit from foreign investors through the positive spillovers. Under the perspective of spillovers, the lack of positive spillover results is clearly counterintuitive. This makes us wonder whether the estimated model, measures and data used so far by the economic literature to detect the spillover effects are appropriate or not. To date, there are many attempts to answer this question by improving the fits of the model, the methodology and the samples. Another important issue to be concerned is the limitation of empirical studies on the productivity spillover effects of FDI in developing countries. The main reason is due to limited availability of data on productivity at the firm level. Nevertheless, to avoid this problem, the causal link between FDI and productivity was taken into account instead. This makes it possible to examine if the productivity of specific economies responds to FDI. It is well recognized that FDI is crucial for productivity growth. Therefore, the direction of causality between FDI and productivity growth is also crucial because it has significantly implications for the spillovers.

Surprisingly little attention has been paid in the literature to the linkage between FDI and productivity spillovers. So far, some attention has been on the relationship between FDI and economic growth. Explicit testing for causality between FDI and productivity is extremely rare. Several studies analyzed the causality between FDI and economic growth for a sample of the Asian countries while there is only a work of Ng (2006) that examined the causality relationship between FDI and productivity. He employed both standard Granger causality test and the test of Toda and Yamamoto (1995) covering a sample of eight East Asian economies over the period 1970-2000. In addition, he claimed that the productivity database was compiled from data envelopment analysis of the United Nations Industrial Development Organization and seemed to be an advantage in the estimation. He found an evidence of causality between FDI and total factor productivity growth in only two countries of Singapore and Taiwan. Due to a limited number of studies, the contribution to the causal relationship is still very useful, especially in the sample of the developing countries. The direction of causality, in fact, can help the policy makers to take the most appropriate decisions in FDI matters: for example, the findings can suggest how much the entry of MNEs should be allowed.

The aim of this study is to investigate the causality between the ratio of FDI to GDP and total productivity growth (TFP) for explaining productivity spillovers within the ASEAN host countries. The analysis is based on a theoretical framework and an estimation methodology that has been developed recently. We follow the causality procedure proposed by Granger (1969) and developed by Toda and Yamamoto (1995). Note that we use the causality test because of the limitation of firm-specific data of the ASEAN countries. However, the country-specific data used in testing can account for a possibility of spillover effects of FDI that occur outside the industry. Even though this estimation seems to partly reexamine the study of Ng (2006) by using the updated data, we make a different contribution to pre-testing of series of variables before applying the causality test. This also results in minimizing the distortion of the causality test.

Outline:
Abstract Introduction Productivity spillover Empirical analysis Conclusion Bibliography

EOF: Productivity Spillovers from FDI

2008-06-02T14:50:00.003+02:00

FDI is likely to have direct and indirect effects on host economies. Increasing capital formation, employment and trade is the direct effects of FDI, whereas receiving a beneficial transfer of know-how and technology, and management skills from home country responds to indirect effects of FDI. The main indirect effects of entry of MNEs would be productivity spillovers as an increase in productivity of host country’s local firms. To date, there exist a large number of studies that concentrate on the indirect effect on productivity and determine whether host country tends to enjoy spillovers from FDI. Following the existing literature, there are two main channels through which multinational firms can improve the productivity of domestic firms via spillovers: horizontal and vertical spillovers.

Horizontal spillovers seem to be technological externalities associated with specific knowledge such as a superior production technique, know-how and management strategy. The entry of multinational firms leads to an increase in the productivity of domestic firms in the same industry. As a result, the horizontal spillover effect is so-called “intra-industry” spillover. There are three important potential channels through which spillovers can occur: demonstration effects, labor turnover and competition effects.[1] Empirically, there is an abundance of evidence regarding the relationship between FDI and the presence of spillover on productivity. These studies attempt to quantify the effect of FDI on the productivity of firms in a host country that is a recipient of this FDI flows. There is a presumption that multinational firms provide positive spillovers in the form of technology upgrading to domestic firms in intra industry. In the recent review of the empirical literature, Görg and Greenaway (2004) concluded that the results from empirical studies using firm- and industry-level cross-section and panel datasets were mixed.[2] A number of studies provide evidence toward positive spillovers that the entry of multinationals enhances productivity of local firms or host country sectors. Interestingly, several recent empirical studies argue that spillovers from FDI may not be significant or even negative. The utilization of cross-sections versus panel data techniques is an important problem in the research design that might lead to different results. However, there are many studies using panel data that suffer from specification problems even though panel data studies have big advantage over studying in cross-section data. Obviously, there are only eight studies employing panel data that find some evidence of positive results while there are several studies finding negative effects of the foreign presence on domestic firms.

On the other hand, vertical spillovers are recognized to be pecuniary externalities from FDI via backward and forward linkages to input market transactions. They may occur when multinational firms enter in transactions between local suppliers and customers, and provide them with technology transfer and know-how so as to improve the quality of intermediate goods. The entry of multinationals can raise demand for local output as backward linkage to intermediate goods suppliers. Therefore through backward linkage mechanism, productivity levels of domestic firms entirely improve. Also, domestic producers who purchase intermediate goods from multinational suppliers may benefit from supply of more sophisticated inputs as forward linkages. The kind of vertical spillovers takes place in the interaction between foreign and domestic firms but not in the same industry; therefore it is also called “inter-industry” spillover. There are very few theoretical works on vertical spillover effect associated with FDI, with demand-side effect on one side and supply-side effect on the other.[3] Their theoretical models deal with the effects of backward linkages between foreign and domestic firms at upstream and downstream sector. Both theoretical literatures have a strong theoretical support for the idea that FDI can play a crucial role in an improvement of productivity by generating backward linkages in the host economy.

On the empirical side, much attention is focused on the research to provide evidence that spillover effect is likely to occur on significant forward and backward linkages between local and foreign firms. A number of recent studies are related to sample differences in data. These studies generally find evidence of positive vertical spillover; and the positive impact on local supplier is likely to be greater when multinationals transfer more advanced technology that improves supplier's productivity. For example, Blalock and Gertler (2003) look separately for both horizontal and vertical spillovers in Indonesia on the semi-parametric estimation method of Olley and Pakes[4]. Using a panel dataset of Indonesian manufacturing, they found evidence of positive vertical spillover presence through backward linkages but also the absence of horizontal spillovers. More recent studies using the same estimation technique performed in Blalock and Gertler (2003) found similar results such as Smarzynska (2003) in Lithuania, López-Córdova (2003) in Mexico, Blyde et al. (2004) in Venezuela and Javorcik and Spatareanu (2006) in Romania. All reported the evidence of positive vertical spillover to local suppliers, conditional on the existence of backward linkages. In contrast, Schoors and Tol (2002) and Sasidharan (2006) found negative vertical spillover effects in Hungary and India respectively. These studies are more conclusive in attributing the presence of significant spillovers, although they do not accrue in all cases. It is important to understand that in this context multinational activities relate to upstream production, not just to downstream production. Consequently, backward and forward linkages between upstream and downstream industry provide a measure for productivity spillover effect of FDI. An expansion of demand for intermediate goods in upstream industry depends on the presence of multinationals. In particular, it implies that the higher use of intermediate goods (due to the quality of goods through technology transfer from MNEs) leads to expand the intermediate sector. This will reduce the price of intermediate goods and finally benefit firms in downstream industry.

In sum, the existing theoretical and empirical existing literature support the prediction that the entry of MNEs gives rise to productivity spillovers. Consequently, it implies that the host-country effects of FDI on productivity spillovers are positive.

[1] See Görg and Strobl (2000, 2002) for a concise description of different channels through horizontal spillovers.

[2] See Görg and Greenaway (2004) for a comprehensive literature review on the empirical studies.

[3] See Markusen and Venables (1999) and Lin and Saggi (2005) for the demand- and supply-side effects of multinational entry on host economy, respectively.

[4] Blalock (2003) suggested that the semi-parametric estimation taken by Olley and Pakes (1996) was a notable method in examining the correlation between firm productivity and foreign presence.

Outline:
Abstract Introduction Productivity spillover Empirical analysis Conclusion Bibliography

EOF: Empirical Analysis

2008-06-02T14:40:00.003+02:00

As specified earlier, the focus in this paper is to examine the productivity spillover effects of FDI. We use the causality methodology to test the direction of the relationship between the ratio of FDI to GDP and TFP growth for the ASEAN countries. Prior to test for causality it is essential to determine the time-series properties of variables employed in estimation by testing for unit root and cointegration. These properties help to choose the best form in which causality tests will be carried out, for example, standard Granger causality test, Toda-Yamamoto augmented Granger causality test and Error Correction Model (ECM) causality test.

Data used in this study are annual time series data on five ASEAN countries: Indonesia, Malaysia, Philippines, Singapore and Thailand. All data cover the period 1970-2004. Data on FDI and GDP are obtained from the UNCTAD foreign direct investment database and the World Development Indicators database published by the World Bank, respectively. Data on TFP growth is estimated by using the Solow growth accounting method, as suggested by Sarel (1997). All values derived there are available on raw data from the World Bank’s World Development Indicators.

Testing for Unit Root:

Testing for unit root is used to determine the data univariate properties and the degree of integration of the series, the TFP and FDI. There are various methods to test for unit root: the Augmented Dickey-Fuller (ADF) test, the Phillips-Perron (PP) test, the Kwiatkowsky-Phillips-Shin (KPSS) test, the Elliot-Rothenberg-Stock (ERS) Point Optimal test, the Dickey-Fuller generalized least square (DF-GLS) test, and the Ng-Perron (NP) test. All these tests are implemented for the level form of the TFP growth and the FDI taking as a percentage of GDP. The first-differenced form of the FDI time-series data are also applied to check for the presence of one order of integration. The test statistic results for a model with a constant and deterministic time trend are reported in Table 1.

Table 1: Unit Root Tests for the TFP and FDI

Notes:

a) The superscripts *, ** and † indicate statistical significance at 1%, 5% and 10% level, respectively. The superscript £ indicates that the data is integrated of order zero I(0) in the first difference at 10% level of significance.

b) The lag length in the ADF and DF-GLS test is chosen by the Akaike Information Criterion (AIC). The PP and KPSS test bandwidth selection is based on Newey and West (1994) using the spectral density at frequency zero with a Bartlett kernel. The lag length in the ERS point optimal and NP tests is chosen using the spectral GLS-detrended based on the AIC and the spectral GLS-detrended based on the modified AIC, respectively.

According to the ADF tests, the level form of the TFP growth seems to be stationary in most countries, since the null hypothesis of a unit root is rejected at the one percent level of significance. At the ten percent level of significance, the unit root hypothesis would also be rejected for Philippines. A similar result is obtained by the PP test, where the unit root assumption is rejected at the one percent level, except for Philippines and Thailand which are rejected at the ten percent level. Additionally, it is noticed that the PP results are in line with the ones reported by Ng (2006) for the period 1970-2000. The KPSS test that considers the null hypothesis of stationarity against the alternative unit root hypothesis confirms the acceptance of stationary null hypothesis at the one percent level of significance for all of selected countries. The results given by the newly developed ERS and DF-GLS test are in accordance with the previous ones which favor stationarity of the levels for all countries we consider. The ERS point optimal test rejects non-stationarity at the five percent level of significance for the whole country. The DF-GLS test mostly rejects at the one percent level of significance, with the exception of Philippines, where the null is rejected at the five percent level of significance. It is important to note that both ERS and DF-GLS tests have the best overall performance in terms of small-sample size and power, dominating the traditional ADF, PP and KPSS test. The problem here is that the test critical values are calculated for 50 observations and may not be accurate for a sample size with a time dimension of 35 observations. Consequently, the results obtained with these tests may be questionable. Turning to the results of the NP test, the statistic test more or less confirms that the TFP time-series data are stationary at the ten percent level for most ASEAN countries, with one exception. The exception is Philippines where the non-stationary null is not rejected, overwhelmingly indicating that the first difference is stationary at the ten percent level.

Concerning the FDI-to-GDP ratios, at least two traditional unit root tests out of three (the ADF, PP and KPSS tests) indicate that this series would be non-stationary in the levels for most countries. While almost all of the newly developed tests (the ERS, DF-GLS and NP tests) provide consistent results indicating that this series are non-stationary in the levels for every country of interest. These results may not be reliable though. As for this reason, the unit root tests on the first difference of the FDI series are also applied to check for the possibility of stationarity.

For the level form, the ADF test of the FDI series is found to be stationary at the one percent level of significance for Thailand and at the five percent level for Malaysia and Singapore, except for Indonesia and Philippines where the ADF statistics cannot reject the null hypothesis. Therefore, to ensure that these series for Indonesia and Philippines are meaningful for further analysis, it needs to apply the test on the first-differenced form. After first differencing the FDI-to-GDP ratios, the null hypothesis of unit root in the ADF test is rejected at the one percent level of significance only for Indonesia, that is, it is integrated of order one. The result of the ADF test is the weakest for Philippines. The PP test results are a little bit contradictory which appear to be stationary in the level form at the one percent level for Indonesia, Philippines and Singapore, with two exceptions. The exceptions are Malaysia and Thailand where the PP statistics cannot reject the null hypothesis in the levels but they can reject in the first difference at the one percent level. In the place of this, the results of the FDI series reported by Ng (2006) were found that the null is rejected only in Singapore. Interestingly, the KPSS test provides the results suggesting that the series are stationary in the level form under the null hypothesis.

Turning to the newly developed ERS test, the level form of the FDI series seems to be non-stationary in all of countries we consider, with one exception. The exception is Thailand where the null of a unit root is rejected at the one percent level of significance. For the first difference, the test statistics can reject the null hypothesis of the presence of unit root at the ten percent level only for Malaysia. It is assumed to be integrated of order one. Similar results are obtained by the DF-GLS test, where the unit root assumption is rejected in the level form at the one, five and ten percent level for Singapore, Malaysia and Thailand, respectively. Moreover, the DF-GLS test statistics cannot reject the presence of unit root for the first difference time series of the FDI-to-GDP ratios for Indonesia and Philippines. However, the results obtained with the ERS and DF-GLS tests may be questionable as a result of a limitation of the critical values calculation. Finally, the results given by the NP test are similar to the previous ones, indicating non-stationarity in the levels for most countries, with the exception of Philippines where the test can reject the unit root null hypothesis. For the first difference, the NP test rejects the presence of unit root at the five percent level only for Malaysia. The test statistics of first order integration are the weakest for Indonesia, Singapore and Thailand which may result in the inclusion of too many autoregressive terms in the test equation.

In sum, the unit root tests absolutely give definite results for the stationarity of the TFP series for Indonesia, Malaysia, Singapore and Thailand, with the notable exception of Philippines. On the other hand, the tests do not give clear-cut results for the stationarity of the FDI series for the whole countries. Both traditional and newly developed unit root tests give conflicting results for the FDI series. For the sake of simplicity, we will assume that the TFP series for Philippines and the FDI series for all countries contain a unit root. That means these series are non-stationarity in the levels but stationarity in the first differences, in other words, they are the first order integrated variables, namely variables of I(1). This assumption can confirm that two series of the TFP and the FDI for Indonesia, Malaysia, Singapore and Thailand are not cointegrated because they are not integrated of the same order. For Philippines where the TFP and FDI series are integrated of order one, the cointegration test has to be established to determine the existence of the long run equilibrium relationships among theses variables.

Testing for Cointegration:

The next step is to test for the presence of cointegrating relationships in non-stationary time series. The cointegration process is used to investigate the long-run relationship between the TFP growth and the FDI, and ensure that the regression is not spurious. The lack of cointegration implies that the variables have no link in the long-run. In testing for cointegration, the necessary condition for all the variables is that they are integrated of order one I(1) in the levels and should be stationary in their first differences. According to the results of unit root tests, only for Philippines, all the series tested are not stationary in the levels but are stationary after being differenced once. Hence, the cointegrating properties of these variables are examined by using the methodology developed in Johansen (1988). In the Johansen maximum likelihood cointegration test, the trace test and eigenvalue test are used to determine the number of cointegrating vectors. Their statistics imply a stationary long-run equilibrium relationship between the variables. The optimal lag length of the VAR model chosen by the standard information criteria[1] is eight. Table 2 reports the results of Johansen cointegration tests among the TFP and FDI series which indicate that there is at least one cointegration vector for the VAR model.

Table 2: Johansen Cointegration Test Results for the TFP and FDI

Notes:

a) Cointegration test is based on a linear deterministic trend assumption.

b) The term r is the number of cointegration vectors under the null hypothesis.

c) Both trace and maximum eigenvalue tests imply significance at 1% level.

The cointegration test results suggest that both trace and maximum eigenvalue test statistics can reject the null hypothesis that the TFP and the FDI are not cointegrated (r=0) at the one percent level of significance. The null hypothesis of at most one cointegrating vector (r≤1) cannot be rejected, indicating one cointegrating vector. The results support the hypothesis of cointegration between the TFP growth and the FDI. The existence of cointegration between the variables confirms that there is a long run relationship between the variables for Philippines.

Testing for Causality:

In an attempt to examine the relationship between TFP and FDI in the selected ASEAN countries, we adopt a causality testing framework which consists of three stages, starting with a unit root and cointegration testing. Lastly, we undertake Granger causality testing that examines the significance of the causal relationship shared among two variables. Both direct and indirect approaches are performed. The direct approach is to estimate the causality between the FDI and the TFP using the Toda-Yamamoto procedure. In this way, it is not necessary to consider the properties of the variables. In contrast, before using the indirect approach, testing for the presence of unit root and cointegration has to be conducted. It is possible that there is a high risk of making wrong inferences about causality due to the incorrect identification of the order of integration of the series or the number of cointegration vectors among the variables. And even though the alternative tests improve the size and power of the Granger causality test, they can lead themselves to difficult application. This is a very important shortcoming that leads to keep in mind the more robust Toda-Yamamoto procedure. The reason is that it allows for the variables in the VAR to be non-stationary or even cointegrated. Hence, a summary of the causality results here is based on the findings of the Toda-Yamamoto causality test, whereas the alternative Granger causality tests are considered as complementary to each other.

We begin with the direct approach proposed by Toda and Yamamoto (1995). The test equations for the Toda-Yamamoto Granger causality would be as follows:

where ln TFP and ln FDI are the logarithm of total factor productivity and of foreign direct investment, respectively. p is the number of lags: p=k+d_max where k is the optimal lag order, and d is the maximal order of integration of the series entered into the model. γ is a constant, t is the time period, and u₁ and u₂ are the error terms that are assumed to be white noise. Equations (1) and (2) are tested for the null hypothesis of no Granger causality (β_1i=0 and β_2i=0, respectively) against the alternative hypothesis using the standard Wald test. Prior to applying the Toda-Yamamoto procedure for Granger causality, it is necessary to establish the optimal lag length of the system (k) and the maximum order of integration of variables (d_max). The maximum order of integration of the series is assumed to be one, which is tested by a mean of the ADF as reported in Table1. To select the appropriate lag structure of the VAR model, measures such as the AIC, SIC, HQ, FPE and LR are used. We then estimate the VAR of the TFP growth and the ratio of FDI to for each country for GDP by using a number of k+d_max lags. The results of the Toda-Yamamoto causality test are reported in Table 3 for all countries.

Table 3: Toda-Yamamoto Granger Causality Test Results for TFP and FDI

Notes: The p-value statistics indicate a statistic significance at 5% level.

The results show that we can reject the null hypothesis that “FDI does not Granger cause TFP” at five percent level of significance in all the sample economies. It means that there is strong evidence of causal relationship running from foreign direct investment to productivity in Indonesia, Malaysia, Philippines, Singapore and Thailand. This is different from the findings of Ng (2006) that only

Singapore

has causality running from FDI towards TFP growth. Moreover, we can reject the null hypothesis that “TFP does not Granger cause FDI” at five percent level of significance, with one exception of Singapore. In other words, these findings indicate that there is strong evidence of a bi-directional causality between the FDI and TFP variables for Indonesia, Malaysia, Philippines and Thailand. That is, the FDI inflows drive productivity growth, whereas the TFP appears to have any impact on FDI there. Meanwhile we find that there is an evidence of a unidirectional causality from the FDI to the TFP in the case of the Singaporean economy.

As specified earlier, the existence of Granger causality from the FDI to the TFP growth is indicative of the productivity spillover effects of foreign direct investments. The results obtained in Table 3 seem to support the productivity spillover hypothesis. This hypothesis not only means that the country is more able to take advantage of foreign direct investment, it also means that the entry of MNEs will lead to the higher improvement of productivity and efficiency of domestic firms in the host country. Of course, demand for intermediate goods in upstream industry will be higher due to the better quality of goods from the horizontal spillover effects. Then, it will be more profitable to downstream industry. By using the country-specific data for testing causality, it is possible to capture only the spillover effects of FDI that occur outside the industry. However, intuitively, an evidence of causality from FDI on productivity tends to make an interesting dilemma regarding the productivity spillover effect of FDI. From an evidence of the Toda-Yamamoto Granger causality, we can conclude that the changes in FDI inflows contribute to productivity spillovers on all the selected ASEAN countries.

An indirect approach offers an alternative way to analyze causal relationships among time series in the VAR model. In this way, the time-series properties of the data are determined to apply what a Granger causality test should be carried out. We conduct Granger test for causality from the FDI to each country’s TFP using two methods: the Granger causality in first difference and the Granger causality on the error correction model. Both methods should be called an “improved standard Granger causality test”. In the first method, the results from the unit-root tests are utilized to ensure that the orders of integration of variables are different and no-cointegrated. Then, the Granger causality test in first difference is implemented. On the contrary, in the second method, the results obtained from the cointegration tests are taken into account. The Granger causality on the ECM will be applied to detect the direction of causality when the variables in the system are cointegrated. Turning to the properties of the data determined earlier, we can categorize the properties in the two cases: (1) One of the series is I(0) and the other one is I(1). This is true for Indonesia, Malaysia, Singapore and Thailand. (2) The TFP and FDI series are I(1) and both are cointegrated. This is option for Philippines. According to the properties of the time series, an indirect Granger causality approach could be applied using two alternative methods as given earlier.

In the first one, we examine the model for Indonesia, Malaysia, Singapore and Thailand whose variables are of different integration orders in levels, and therefore are not cointegrated. Of course, the Granger causality test in first differences is conducted. The results from the causality test on the relationship between the FDI and the TFP are shown in Table 4.

Table 4: Granger Causality Test Results for TFP and ∆FDI

Notes: The p-value statistics indicate a statistic significance at 5% level.

The results in Table 4 are quite different from the Toda-Yamamoto causality test, except for Singapore. In case of Singapore, the null hypothesis that “FDI does not Granger cause TFP” can be rejected at the five percent level of significance but we cannot reject the null hypothesis that “TFP does not Granger cause FDI”. This indicates that there is an evidence of unidirectional causality running from the FDI to the TFP. In case of both Indonesia and Malaysia, there is only strong evidence that TFP causes FDI. For Thailand, there is no any evidence of causality between the FDI and the TFP. That is, FDI does not Granger cause TFP, whereas TFP does not Granger cause FDI. These findings suggest that with the exception of Singapore, there is no evidence that the FDI helps to increase the TFP in a sample of ASEAN countries.

In another case, Granger causality test is performed on the error correction model to detect the direction of causality among variables which are assumed to be cointegrated. The general form of the ECM derived from the Johansen-Juselius methodology can be written as:

where ∆ is the first-difference operator, EC_t-1 is the error correction term obtained from the long run cointegrating relationship, and μ₁ and μ₂are error-correction coefficients. The changes of the lagged independent variable are expected to capture the short run dynamics of the model, whereas the coefficient on the error-correction term indicates the speed of an adjustment through the long-run relationship among variables. The Granger causality from the FDI to TFP in the presence of cointegration is evaluated in equation (3) by testing the null hypothesis that μ₁and all β_1i are equal to zero, using the standard Wald test. By rejecting the null, FDI is said to Granger-cause TFP. Likewise, we can test if TFP causes FDI by replacing for FDI and vice versa in equation (4). As regards our cointegration results, it seems appropriate to employ the ECM for testing causality in the data series of Philippines. The empirical results of the estimated ECM are presented in Table 5.

Table 5: Estimation Results of the ECM for TFP and FDI

Notes: ** denotes statistically significant at 5% level of significance.

The estimates from the ECM shown in Table 5 confirm that there is no evidence of short-run and long-run causality test running from the FDI to the TFP in Philippines. Not only the F-statistics for lagged values of FDI but the t-statistics of the estimated coefficient on lagged value of error-correction term are statistically insignificant. Hence, FDI does not Granger-cause TFP in Philippines. On the other hand, the results provide some evidence of unidirectional causality that TFP causes TFP in the long run. However, there is no evidence of causation running from the TFP to the FDI in the short run. Thus, we can conclude that there is no a significant short-run relationship between the FDI and the TFP as obtained by the Toda-Yamamoto causality approach.

It might be noted that both alternative tests were improved due to the shortcoming of the standard Granger causality test. One of its shortcomings is that the results are only valid for stationary variables. Therefore, using the improved Granger causality tests tends to make the causality inferences. However, it is also possible that incorrect inferences will occur due to the sensitivity of stationary and cointegration tests. Therefore, to minimize the risks of wrongly identifying the presence of unit root and cointegration, we hold the causality results obtained from the Toda-Yamamoto Granger causality test in this paper.

[1] The number of the optimal lag length is determined using the standard information criteria, namely the Akaike Information Criterion (AIC), the Schwarz Bayesian Information Criterion (SIC), the Hannan-Quinn Information Criterion (HQ), the Final Prediction Error (FPE) and the Likelihood Ratio (LR). The best chosen one will be selected among these criteria using the maximum chosen lag.

Outline:
Abstract Introduction Productivity spillover Empirical analysis Conclusion Bibliography

EOF: Conclusion

2008-06-02T14:30:00.002+02:00

In this paper, we investigate the productivity spillover effects of FDI using the country-level data for five ASEAN countries during 1970-2004. Empirically, it is to examine the causal relationship between the ratio of FDI to GDP and TFP growth for explaining productivity spillovers from the FDI. This study differs from quite a few recent literature which mainly used the firm-level variables in the model regressed by exploiting the firm-specific data. However, by using the country-specific data, it is possible to capture the spillover effects of the FDI outside the industry. In doing so, various recently developed time-series techniques such as unit-root testing, cointegration testing, and Granger causality testing are introduced. The empirical results from the Granger causality test proposed by Toda and Yamamoto (1995) suggest that there is the bilateral causal relationship between the FDI and the TFP growth found in four economies: Indonesia, Malaysia, Philippines and Thailand. Meanwhile, for Singapore, there is a strong evidence of the unidirectional causality from the FDI to the TFP growth. In other words, the results from the Toda-Yamamoto Granger causality test confirm that there are positive productivity spillover effects from inward FDI in all the selected ASEAN countries. More importantly, the results of the studies provide a link between the theoretical issue on the FDI inflows and the presence of productivity spillovers. The entry of MNEs plays an important role in the nature of productivity spillover effects, not only horizontal spillover but also vertical spillovers. Even our results cannot restrict to the pattern of spillover effects due to the limitations of the firm-specific data, there is clear evidence in the results of productivity spillover effects, notable through FDI.

In addition, our results could suggest the possible policy implications. That is, inward investment under the AIA framework agreement tends to enhance the productivity spillovers. This implies that the ASEAN governments should keep in mind about the implementation of the AIA framework agreement which is one of the most favorable mechanisms for an end-goal of ASEAN vision 2020. The success of the AIA would advance the interest of foreign investors, and consequently, renders the higher productivity in local industries. Moreover, the policy makers would be also wise to consider attracting investments from such countries, focusing on the high-tech technology sectors. Taken together, it is the inward FDI to the ASEAN countries that generates the greatest productivity effects of FDI. Another interesting implication of this analysis is that, apart from the direct effect of the FDI on the productivity, the ASEAN governments can outline the scope for the policies and measures that help both domestic investors and producers get higher returns from the dispersion of the productivity spillovers.

For future research, two potential extensions of this empirical analysis would be interesting. First, moving to higher frequency data, if available, could be worthwhile. This step might be more reliable ways of determining the direction of causality among the FDI and the TFP. Second, further consideration should be given to the productivity spillover effects of the FDI within industries which could generate valuable insights. Using data at the firm level and rigorous econometric techniques such as cross section and panel data techniques will help to explain the importance of the source country sector in determining the productivity effects from the FDI, which may be ignored by policy makers when allocating the FDI incentives. It is expected that this study will support the explanations about the performance of the investment policies in the ASEAN.

Outline:
Abstract Introduction Productivity spillover Empirical analysis Conclusion Bibliography

EOF: Bibliography

2008-06-02T14:20:00.002+02:00

Blalock,G. - Gertler,P. (2003), ‘Technology from Foreign Direct Investment and Welfare Gains through the Supply Chain’, Tech. rep., Cornell University and University of California.

Blyde,J. - Kugler,M. - Stein,E. (2004), ‘Exporting Vs. Outsourcing By MNC Subsidiaries: Which Determines FDI Spillovers?’, Discussion Papers in Economics and Econometrics 0411, University of Southampton.

Görg,H. - Greenaway,D. (2004), Much Ado about Nothing? Do Domestic Firms Really Benefit from Foreign Direct Investment?’, World Bank Research Observer, 19: 171-197.

Görg,H. - Strobl,E. (2002), ‘Multinational Companies and Productivity Spillovers: A Meta-Analysis with a Test for Publication Bias’, Research Paper 2000/17, Centre for Research on Globalisation and Labour Markets, University of Nottingham.

Görg,H. - Strobl,E. (2002), ‘Spillovers from foreign firms through worker mobility: An empirical investigation’, Research Paper 2002/13, Leverhulme Centre for Research on Globalisation and Economic Policy.

Granger,C.W.J. (1969), ‘Investigating causal relations by econometric models and cross-spectral methods’, Econometrica, 37(3): 424-438.

Javorcik,B.S. - Spatareanu,M. (2006), ‘To Share or Not To Share: Does Local Participation Matter for Spillovers from Foreign Direct Investment?’, Newark Working Paper 2006-001, Rutgers University.

Johansen,S. (1988), ‘Statistical analysis of cointegrating vectors’, Journal of Economic Dynamics and Control, 12:231-254.

Lin,P. - Saggi,K. (2005), ‘Does Foreign Direct Investment Promote Development?’, Multinational Firms and Backward Linkages: A Critical Survey and a Simple Model, Institute for International Economics, Washington D.C.

López-Córdova,J.E. (2003), ‘NAFTA and Mexicos Manufacturing Productivity in Mexico’, Tech. Rep. 1, Inter-American Development Bank, Washington D.C.

Markusen,J.R. - Venables,A.J. (1999), ‘Foreign Direct Investment as a Catalyst for

Industrial Development’, European Economic Review, 43:335-356.

Ng,T.H. (2006), ‘Foreign direct investment and productivity: Evidence from the east asian economies’, Staff Working Paper 03/2006, United Nations Industrial Development Organization.

Sarel,M. (1997), ‘Growth and Productivity in ASEAN Countries’, IMF Working Paper WP/97/97, International Monetary Fund.

Sasidharan,S. (2006), ‘Foreign Direct Investment and Technology Spillovers: Evidence from The Indian Manufacturing Sector’, Working Paper Series 2006-010, United Nations University.

Schoors,K. - Tol,B.V.D. (2002), ‘Foreign Direct Investment Spillovers Within And Between Sectors: Evidence From Hungarian Data’, Working Paper 2002/157, Ghent University, Belgium.

Smarzynska,B.K. (2003), ‘Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers Through Backward Linkages’, William Davidson Working Paper 548, The William Davidson Institute.

Solow,R. (1956), ‘A contribution to the theory of economic growth’, Quarterly Journal of Economics, 70:65-94.

Toda,H.Y. - Yamamoto,T. (1995), ‘Statistical inference in vector autoregressions with posssibly integrated processes’, Journal of Econometrics, 66: 225-250.

Outline:
Abstract Introduction Productivity spillover Empirical analysis Conclusion Bibliography

DOF: Abstract

2008-06-01T13:00:00.003+02:00

FDI in ASEAN Countries: An Empirical Investigation
August 2005

Abstract

Theories of multinationals, in particular the knowledge-capital (KC) model combined by the horizontal and vertical model, have recently been used to explain the role of foreign direct investment (FDI) in developing countries. Nevertheless they did not concern whether the determinants associated with horizontal and vertical model affect the investment decision of multinational enterprises (MNEs) in ASEAN. As a result, this paper aims to examine MNEs activities by using panel data of ASEAN countries from 1983-2003. Results of econometric tests give strong support to the horizontal model in favor of the KC model.

JEL Classification: F23, F21, F12, F15

Keywords: Multinational enterprises, Foreign direct investment, Knowledge-capital, ASEAN

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOF: Introduction

2008-06-01T12:50:00.005+02:00

Foreign direct investment (FDI) is recognized to contribute to benefits of economic and social development. In addition, it is also a channel through which countries’ different characteristics confront one another. The developing country governments attempt to promote investment and create a sound investment climate that attracts foreign investors to their countries. As a result, FDI inflows are regarded as vital complements to development efforts. Likewise, the Association of Southeast Asian Nations (ASEAN) takes into account to conduct investment cooperation agreements for ASEAN to function as an attractive investment destination and to contribute special conditions for multinational enterprises (MNEs) in order to stimulate the surge of FDI into this region.

Over the last decade, FDI inflow to ASEAN countries fluctuated tremendously; however, the upward FDI inflow trend for ASEAN was demonstrated in several years. That may be because the strength of ASEAN cooperation reinforces this region to be a source of competitiveness in production costs and advantage in access to a customer base. One of foremost FDI source belongs to United States (US); it was valued at US$ 2,920 in 2003 with the share of 14% of total FDI inflow. Moreover, US and ASEAN built on the solid framework of US engagement in ASEAN since 2004 in order to establish a regional environment that supports trade and investment inflows and to boost US to access to ASEAN markets.

The purpose of this paper is to examine how ASEAN country’s characteristics explain an investment behavior of US MNEs. The consideration underlying the theory of MNEs sheds particularly light on the investment mode of MNEs. In recent year, it is well-known that the Knowledge-capital (KC) model is used to analyze substantially a motivation for FDI of MNEs. In fact, the horizontal model where the access to a host-country market is driven horizontal FDI, and the vertical model where differences in factor endowments between two countries are driven vertical FDI are combined in the KC model. Therefore, the specification underlying the KC model can explain the MNEs activity explicitly. That is the reason why the KC model described in Markusen et al (1996) and Markusen (1997) is utilized in the conceptual framework for our paper.

The recent studies have examined the roles of country’s characteristics on MNEs’ decision by applying the KC model. The results gave both support and reject to the KC model. Carr et al (1998) gave a strong support to the KC model, in contrast to Markusen and Maskus (1999) and Blonigen et al (2002). The contradict results related to the negative relationship between affiliate sales and skill differences variables. That means larger differences in factor endowments between host and home country decrease MNEs activity; however, these results were not as predicted. In order to ensure the strength of the KC model, Davies (2002) and Braconier et al (2002, 2003) sought to contribute to better informed decision in MNEs location by gathering statistics and improving variables and measures on their specification. The evidences gave strong support to the KC model; the skill measure can explain the vertical MNEs in favor of the KC model. At the rate, their studies paid attention to the motivation for FDI of overall MNEs, therefore, there is a doubt how change in MNEs activity of developed country on developing country’s characteristics is.

In order to address the above question, our empirical specification defines variables of interests and signs of prediction followed in Carr et al (1998) and Markusen and Maskus (1999). We use a panel country-level data set for the period 1983-2003 in analyzing how ASEAN country’s characteristics contribute to an investment environment are attractive more to US MNEs activity.

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOF: Empirical Specification

2008-06-01T12:40:00.002+02:00

According to our objective, it is to examine how ASEAN member countries’ characteristics explain US multinational enterprises (MNEs) activity. Hence, a good starting point is a consideration in hypothesis and country-level variables for our empirical specification which takes into account the results from the Knowledge-capital (KC) model suggested by the existing literature to be able to explain MNEs activity.

The access to a host-country market is acknowledged to be a motivation in horizontal foreign direct investment (FDI). In addition, a similarity in country’s size and relative endowments and a high trade cost are essential variables behind horizontal MNEs’ decision. That is because they contribute to higher profits from accessible capability to customers and trade-cost saving from substituting exports with locating production facilities.

In contrast, vertical FDI is driven by differences in relative factor endowments between both countries. The greater skill differences attract the larger FDI. That is, the unskilled-labor abundant host country encourages MNEs to fragment production facilities where unskilled labor is cheap. Moreover, vertical FDI is more attractive in smaller lower trade costs which decrease the total marginal costs for shipping back to home country.

The results from the theoretical model, namely the KC model bring us to two main hypotheses.

Hypothesis 1 Countries with similar size and relative factor endowments, high trade costs and low investment costs, induce a larger horizontal FDI.

Hypothesis 2 Countries with different relative factor endowments, and low trade costs and investment costs, induce an increasing vertical FDI.

In addition, the theoretical model and empirical studies reviewed in previous section defined reliable country-level variables that were key factors in explaining MNEs activities. These main explanatory variables consisted of sum of real GDP in home and host country, difference in real GDP in both countries, difference in skilled-labor abundance in two countries, investment cost in host country, trade cost to home and host country, and distance.

Therefore, in order to estimate empirically MNEs activity by home country i in host country j at time t, the above explanatory variables are chosen into the empirical specification by applying the gravity equation approach on the KC model. In addition, by following directly the basic KC specification in Carr et al (1998), which is also used by Markusen and Maskus (1999), Blonigen et al (2002), Braconier et al (2002, 2003) and Davies (2002), the basic empirical estimation is given as follows:

The subscripts point to the variation in such regressors over time (t), across either home county (i) or host country (j). The dependent variable – a measure of the MNEs activity from a home county (i) to a host country (j) – is measured in term of affiliate sales and direct investment between any two countries. First, RSALESij,t denotes the real total sales of country i’s affiliates in country j. Second, FDIij,t follows directly Davies (2002) denotes the stock of direct investment of country i to country j.

The first two explanatory variables are posited to capture the motivation in the horizontal investment. The first term, SUMGDPij,t denotes the sum of two countries’ real GDP. It is expected to have a positive sign, that is, larger joint market size tends to increase the FDI. The second term, GDPDIFSQij,t denotes the squared differences in real GDP between home and host country. It is expected to be negative; the more similarities in country size tend to encourage the FDI into host country.

The third explanatory variable, a key factor in explaining the vertical multinational production – an opposite of the horizontal model, is SKDIFFij,t that denotes the skilled-labor abundance in home country relative to host country. It is expected to be positive; two countries with different relative endowments contribute to the vertical MNEs that fragment the production process, and locate headquarter services in skilled-labor abundant country and production in skilled-labor scarce country.

The rest of explanatory variables are control variables. First, INVCj,t denotes an index of the investment cost in country j, in other words, it regards as an influence of investment barriers on MNEs’ decision. This regressor is expected to be negative sign; obviously, the higher investment costs reduce FDI. Second, TCi,t and TCj,t denote trade cost in exporting to home and host country respectively. The first term explains how the vertical FDI locates plant abroad in order to ship back to home country, shifts in the level of trade barriers. The second term explains how trade barriers in exporting to host country, encourage the horizontal FDI. These explanatory variables are expected to be positive and negative respectively. That is, the increasing trade barriers in host country foster an incentive for horizontal FDI in its country; whereas, the increasing trade barriers in home country reduce an incentive for vertical FDI. Finally, DISTij denotes geographical distance between home and host country, including as a proxy for transportation and trade costs. Due to the distance is a composition of both investment and trade cost; therefore, the expected sign of distance is ambiguous.

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOF: Data

2008-06-01T12:30:00.002+02:00

The panel country-level data for the estimation are collected from different sources between home country and host country over the period 1983-2003. Such observations constitute a relation between a home country (US) and a host country (Malaysia, Philippines, Singapore and Thailand), therefore, our sample consists of 84 location decisions of US MNEs into ASEAN member countries from 1983 to 2003.

The primary variables – US affiliate sales and US direct investment stocks- are gathered from the Bureau of Economic Analysis (BEA) database, US department of commerce. The real GDP data are collected from National Accounts main aggregate database of United Nations Statistics division. These data sources are used to figure out annual data on the sum of GDP and the GDP differences. See http://unstats.un.org/unsd/default.htm

Due to the total employments by occupation only in professional, technical, administrative and managerial workers are measured as a proxy of skill labor. Therefore, annual data on country’s total employment by occupation from the International Labor Organization (ILO) database are collected and calculated for the skilled-labor abundance (see http://laborsta.ilo.org/). The difference between skill labor of home and host country is measured by the difference of the ratio of country’s skill labor to country’s total employment. The skill labor is the sum of the numbers of workers in categories 0/1 (professional, technical and kindred workers) and 2 (administrative workers). These figures have a range between 0 (skilled labor scarce) and 1 (skilled labor abundant). The missing annual figures are taken to equal the period average figures for each country.

The investment cost data are obtained from the index of freedom reported annually by the Heritage Foundation (see http://www.heritage.org/research/). Investment cost measure is constructed on the composite score of index of economic freedom accumulated by the Heritage Foundation. The overall index of economic freedom includes the measures of governmental, institutional, financial, and economic factors, is defined in the range between zero and 100. The higher the score is, the greater the level of government interference in the economy and the less economic freedom are.

The trade cost measure is based on the trade openness measure that defines as the sum of country’s exports and imports divided by country’s GDP. Therefore, the export, import and GDP data in such countries are essential data sets to construct the trade cost database. The export and import data are collected annually from World Trade Organization database.

Finally, the distance is measured by distance of each country’s capital city from Washington DC in 1,000 kilometers. The longitude and latitude between capital cities are available at web page http://export911.com/convert/distaCalc.htm and are calculated their distance on global distance calculator version 1.

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOF: Empirical Results

2008-06-01T12:20:00.002+02:00

The results of specification in (22) based on the gravity equation are shown in the following table using the Ordinary Least Square (OLS) and Fixed Effect (FE) techniques in the estimation. The results will bring us to evidence that US multinational enterprises (MNEs) activity into ASEAN member countries belongs to both horizontal and vertical feature. The expected signs in specification are based on Carr et al (1998) and Markusen and Maskus (1999).

Column (1) shows the coefficient estimates for our specification, using affiliate sales (RSALES) as a dependent variable. The coefficient on sum of GDPs (SUMGDP) and squared GDP differences (GDPDIFSQ) are significant at one percent level and five percent level respectively with the expected sign. That is, SUMGDP and GDPDIFSQ are expected to be positive and negative respectively for horizontal model in favor of the KC model, and to be absolutely unrelated to vertical model. Obviously, there is greater horizontal FDI between similar-sized countries.

The coefficient for variable of interest, skill differences (SKDIFF), is significant at the one percent level with the wrong sign. In fact, SKDIFF should be positive to support vertical model and indicates that the vertical foreign direct investment (FDI) is particularly driven by larger skill differences. Surprisingly, the result for differences in skilled-labor endowments is not as expected, however, it is similar to Markusen and Maskus (1999b) which found a negative relation between relative skill endowments and affiliate exports back to US, and Markusen and Maskus (1999a) that gave weak roles for skilled-labor abundance on affiliate sales. In order to better improvement, we apply absolute values on skill differences corresponding to Blonignen et al (2002) in our specification. The results in estimation of two specifications are identical.

Turning to the rest of explanatory variables, the coefficient estimates of investment cost (INVC) and trade cost back to the home country (TCI) are significant at one percent level and have negative and positive sign respectively which are as anticipated, except for trade cost to host country (TCJ) that is insignificant despite the correct sign. It is said that MNEs’ decision is responsive dramatically to moderate investment costs and the strength of trade barrier in one country, whereas, greater trade openness to export back of own country attracts MNEs activity.

The coefficient estimate of distance between home and host country (DIST) is significant at one percent level with positive sign. However, as discussed earlier, distance is as a proxy for both investment cost and trade cost, its sign is ambiguous in theoretically. This finding here is contrast virtually with results in Carr et al (1998) and Markusen and Maskus (1999).

For comparison, we test our specification using fixed effect estimation. The results are shown as in column (2). The signs are as hypothesized except for INVC. The coefficients of variables of country’s size and trade cost remain strongly significant while the coefficients for skill differences and investment cost are insignificant with correct and wrong sign respectively.

Altogether, the equations in column (1) and (2) have strongly good fit, with an adjusted R-squared at 0.94 and 0.96 respectively. According to hypothesis, the results for our specification confirm that US MNEs activity increase when ASEAN member country is similar in size and factor endowments relative to US country. It is clear that the result of skill differences contradicts the KC model. Therefore, it is important to note that the results for our specification are strong support in the implications of the horizontal model in favor of the KC model, contrary to the vertical model.

With respect to Davies (2002), we employ direct investment stock (FDI) as a dependent variable in our specification. The results are similar to a previous specification that exploits affiliate sales as dependent variable. The results are shown as column (3). The coefficients of sum of GDPs and squared GDP differences have positive and negative sign as expected, and are significant at ten percent level and insignificant respectively. However these signs ensure that the similarities in country’s size encourage the horizontal FDI.

The skill differences (SKDIFF) is significant at the one percent level with the wrong sign. The results for all of two specifications are similar, that is, they do not support the prediction of the KC model with respect to skill differences between two countries. It is doubtful why there is a negative sign in skill differences in developing-country observations, in other words, why differences in factor endowments between developing country and developed countries discourage the vertical FDI.

The remaining coefficient estimates of investment cost and trade cost are significant at one percent level with the expected signs. Both of their findings are not surprising due to production facilities that contain fair investment costs and extremely trade barrier, encourage MNEs activity.

The coefficient on distant remain strongly significant at one percent level with positive sign. Its reason can explain as aforementioned. The equation in column (3) has strongly good fit, with an adjusted R-squared at 0.92. The results give strong support in horizontal model in favor of the KC model as well as previous results. It implies that the more MNEs activity takes place, the more similar country’s size and relative endowments are. That is, US MNEs activity occurs directly to ASEAN member country where there is similar in size and relative factor endowments.

In sum, the results for our specifications using both affiliate sales and direct investment as dependent variable support our first hypothesis. As a result of the opposite coefficient of skill difference contradicts absolutely to the KC model in vertical composition.

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOF: Conclusion

2008-06-01T12:19:00.002+02:00

The purpose of this study is to examine why ASEAN member country’s characteristics as the determinants of US multinational enterprises (MNEs) activity. The knowledge-capital (KC) model as discussed in this paper makes evident testable implications. Our empirical specification follows directly Carr et al (1998) and Markusen and Maskus (1999) and evidences US MNEs activity between countries as a function of characteristics of ASEAN countries, in particular sum of GDPs, squared GDP differences, relative endowment differences, investment cost, trade cost and distance. At any rate, this evidence is as anticipated following the theoretical framework of KC model.

The results for specification indicate that model fits well and gives strong support to the horizontal MNEs feature. The estimates in column (1) suggest correct signs and strongly statistical significance for sum of GDPs, squared GDP differences, investment cost, trade cost to host country and distance, whereas, trade cost to home country has correct sign with weakly statistical significance. Surprisingly, skill differences are highly significant with wrong sign. The possible explanation is that our specification attempts to make an improvement by using panel data set from developing host countries e.g. Malaysia, Philippines, Singapore and Thailand rather than included developed host countries.

In summary, it is sufficiently clear that the motivation of US MNEs to ASEAN countries increases according to their joint market size and their similarity in size and relative factor endowments. It is important to note that this finding accepts the horizontal model in favor of the KC model and rejects the vertical model.

However, this outcome raises our concerns to question that remains to be answered why differences in factor endowments between developing country and developed country e.g. ASEAN and US country respectively discourage the vertical MNEs activity. One of our concerns associates with a solid framework in fostering foreign investment of developing countries, dealing with competitiveness in production costs in principle. Therefore, this is regarded as an important pace in way that better improves the conceptual framework and econometric specification related to MNEs activity in the further study.

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOF: Bibliography

2008-06-01T12:18:00.002+02:00

ASEAN Secretariat. (2004). ASEAN Statistical Yearbook 2004.

Blonigen, B.A., Davies, R.B. and Head, K. (2002). Estimating the Knowledge-Capital Model of the Multinational Enterprise: Comment. National Bureau of Economic Research Working Paper No. 8929.

Braconier, H., Norbäck, P-J. and Urban, D. (2002). Vertical FDI Revisited. The Research Institute of Industrial Economics Working Paper No. 579, Sweden.

Braconier, H., Norbäck, P-J. and Urban, D. (2003). Reconciling the Evidence on the Knowledge Capital Model. The Research Institute of Industrial Economics Working Paper No. 590, Sweden.

Carr, D.L., Markusen, J.R. and Maskus, K.E. (1998a). Estimating the Knowledge-Capital Model of the Multinational Enterprise. National Bureau of Economic Research Working Paper No. 6773.

Carr, D.L., Markusen, J.R. and Maskus, K.E. (2002). Competition for Multinational Investment in Developing Countries: Human Capital, Infrastructure and Market Size. Discussion Paper No 2002-14, Centre for Economic and Business Research, Denmark.

Davies, R. B. (2002). Hunting High and Low for Vertical FDI. August. University of Oregon. Mimeo.

Markusen, J.R. (1997). Trade Versus Investment Liberalization. National Bureau of Economic Research Working Paper No. 6231.

Markusen, J.R. and Maskus, K.E. (1999a). Multinational Firms: Reconciling Theory and Evidence. National Bureau of Economic Research Working Paper No. 7163.

Markusen, J.R. and Maskus, K.E. (1999b). Discriminating among Alternative Theories of the Multinational Enterprise. National Bureau of Economic Research Working Paper No. 7164.

Markusen, J.R., Venables, A.J., Konan, D.E. and Zhang, K.H. (1996). A Unified Treatment of Horizontal Direct Investment, Vertical Direct Investment, and the Pattern of Trade in Goods and Services. National Bureau of Economic Research Working Paper No. 5696.

UNCTAD (2004). World Investment Report: The Shift towards Services.

Outline:
Abstract Introduction Empirical specification Data Empirical results Conclusion Bibliography

DOT: Abstract

2008-05-31T17:43:00.003+02:00

ASEAN economic integration and trade flows: evidence from gravity model
September 2004

This paper applies the gravity trade model to analyze the ASEAN's trade with its major trading partners and Intra-ASEAN's trade using the panel data estimation technique. The major issue in this analysis is to explore how determinants influence on trade flows of models. A panel data analysis is used to capture the relationships between the relevant variables over time and to disentangle the time invariant country-specific effects. Then we find that the fixed effect model is preferable to the random effect gravity model. Our empirical results show that ASEAN's trade to 20 trading partner countries is positively determined by the size of the economies, per capita GDP differentials of the countries involved, regional integration arrangement (RIA) of ASEAN and NAFTA. But distance and RIA of EU are found significant factors in influencing ASEAN's trade negatively. Furthermore, the results show that variables, namely, GDP, per capita GDP, and the membership of ASEAN influence the higher Intra-ASEAN trade, while distance affects Intra-ASEAN trade negatively.

Key words: Gravity model, Panel data analysis, ASEAN's trade, Intra-ASEAN's trade

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

DOT: Introduction

2008-05-31T16:39:00.001+02:00

The issue of regional economic cooperation has increased an interest in international economics in recent years, especially the formation of free trade area (FTA) and other special regional trading arrangements (RIAs). Among the Southeast Asian countries, the Association of Southeast Asian Nations free trade area (AFTA) was created in 1992 to eliminate tariff barriers with a view to integrate the ASEAN economies into a single production base and create a regional market. Consequently, ASEAN member countries have made significant progress in the lowering of Intra-regional tariffs through the Common Effective Preferential Tariff (CEPT) scheme for AFTA. The reduction in tariff among ASEAN members may lead member economies to greater competitiveness and open up larger markets. In addition, the effect of RIAs may increase Intra-ASEAN trade by diverting trade away from non-member countries. The CEPT scheme has been proving itself in the successful implementation during a decade. That was, ASEAN trade grew continuously from 1993 to mid-1997 (before the financial and economic crisis in 1997-1998) and regained upward trend reaching its peak in 2000, then declining in 2001 as a result of the economic slowdown in the United States and Europe and the recession in Japan. Therefore the effects of RIAs on trade flows are generally ambiguous analytically. One possibility is that, in globalization with policy changes and technical progress, we confront obviously trade interventions and market imperfections. For better understanding, the empirical examination of the effects of RIAs on trade flows is essential to perform particularly on the RIAs of ASEAN that is a group of developing countries with high risk of economic crisis.

The gravity equation has been frequently and successfully used to further understanding of the determinants of bilateral trade flows across countries and, subsequently, to analyze commercial policy measures. Sanso (1993) noted that there were 3 main topics on gravity equation: the great usefulness of its basic formulation as an instrument for bilateral trade flows modeling, its adequacy to each particular situation by adding proper variables and, finally, the strong conviction that the loglinear form was the correct specification for it. The concept in the form of a gravity model often use a size of economy, geography, demography and concurrent attributes to explain trade flows between countries. Empirically, trade flows have been found to be higher between countries with larger economies. Country geographic size and population are factors expected to reduce trade orientation.

In this paper, we will apply the gravity model to quantify the trade effects on RIAs of ASEAN by using the panel data estimation technique. The main objective in this analysis is to explore how determinants influence on trade flow of models. There are 2 gravity models in our work: (1) trade flow between ASEAN member countries and trading partner countries e.g., EU, NAFTA, China, Japan, South Korea, Australia and New Zealand; and (2) trade flow among ASEAN member countries. Both models are empirically tested whether variables are relevant in explaining the volume and direction of international trade. These variables comprise of GDP of a pair of countries, PCI differential, distance and the formation of regional integration.

In the empirical application, a panel data methodology is used to capture the relationships between the relevant variables over time and to disentangle the time invariant country-specific effects. In addition, a panel data analysis can ignore some doubts on the usual interpretation of integration dummies when the pooling time series or cross-section analysis is the methodology applied.

However there are 2 gauges in our approach. The first gauge, we attempt to investigate the role that GDP of a pair of countries, PCI differential, and distance play as explaining bilateral trade flows in a panel data framework. The second gauge, we estimate the effect of trade flows on RIAs (EU, NAFTA and ASEAN) that is a special interest in world trade.

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

DOT: Revealed Comparative Advantage

2008-05-31T13:53:00.004+02:00

ASEAN's Revealed Comparative Advantage

The Association of Southeast Asian Nations (ASEAN) was established on 8 August 1967 in Bangkok by the 5 original member countries, namely, Indonesia, Malaysia, Philippines, Singapore and Thailand. Afterwards Brunei Darussalem joined on 8 January 1984, Vietnam on 28 July 1985, Laos and Myanmar on 23 July 1997 and Cambodia on 30 April 1999. However, in order to expand the share intra-ASEAN trade, the Preferential Trading Arrangements (PTAs) was launched in 1977, which accorded tariff preferences for trade among ASEAN economies. Later the ASEAN Free Trade Area (AFTA) was established in January 1992 to eliminate tariff barriers among the Southeast Asian countries with a view to integrate the ASEAN economies into a single production base and create a regional market. Hence the Agreement on the Common Effective Preferential Tariff (CEPT) Scheme for the ASEAN Free Trade Area occurred to require tariff rates levied on a wide range of products traded within the region be reduced to no more than 5%. Quantitative restrictions and other non-tariff barriers are to be eliminated simultaneously. Although the original schedule was realized by 2008, the target of a free trade area in ASEAN was continuously moved forward. That is, all import duties will be eliminated by 2010 for the 6 original members of ASEAN and by 2015 for the new members.[1]

As a result of the successful implementation of the CEPT scheme, trade among ASEAN countries grew from US$ 429,948 million in 1993 to US$ 687,773 million in 2001 which represents an average annual increase of 7% (see annex 1). Moreover before the financial and economic crisis struck in mid-1997, ASEAN trade was increasing by 16%. However 2 years later, ASEAN's total trade had upward trend reaching its peak in 2000 at US$ 756,520 million. Until, as a result of the economic slowdown in the United States (US) and European Union (EU) including the recession in Japan, ASEAN trade value declined in 2001 to US$ 687,773 million. Figure 2-1 shows the fluctuation of ASEAN trade value between 1993 and 2001.

Figure 2-1 Total ASEAN trade, 1993-2001

While trade with traditional industries markets remained robust, share of intra-ASEAN trade remained low with 22% of total ASEAN trade[2] in 2001 comparing with 19% in 1993 when AFTA was launched (see annex 2 and 3). However, this is significantly higher than the rate of increase of total ASEAN trade, which grew only 16% during the same period. The United States, the European Union and Japan are ASEAN’s largest export markets. Japan, followed by the U.S. and EU, are the largest sources of ASEAN imports.

Figure 2-2 Intra- and Extra-ASEAN trade, 1993-2001

In order to measure a relative export performance or a comparative advantage of ASEAN-5[3] members, the revealed comparative advantage[4] (RCA) indexes were calculated, at 1-digit and 2-digit standard international trade classification (SITC) level.

The revealed comparative advantage (RCA) index of a country's given product category is defined as the quotient of 2 ratios. The numerator is the share of the product category's export in the country's total exports, while the denominator is the share of the world's total exports of the product category in the world's total exports of all products. An index greater than unity in a particular product category reveals that the country has a comparative advantage in the production of that product category, whereas an index smaller than unity reveals that the country has a comparative disadvantage. An increasing in the magnitude of RCA value indicates that the country is gaining advantage in that product relative to the world market.

Annex 4 and 5 present RCA indexes of ASEAN export products with an RCA value from 1998 to 2001 by 1-digit, and 2-digit SITC respectively, depending on data availability. In annex 4, product category that shows the comparative advantage for ASEAN-5 is animal and vegetable oils, fats, and waxes (SITC 4); machinery and transport equipment (SITC 7); and mineral fuels, lubricants and related materials (SITC 3). As annex 5 indicates clearly in the comparative advantage vis-a-vis 2-digit SITC level from 1998 to 2001. ASEAN's comparative advantage is most pronounced in some of primary products: food and live animals chiefly for food (SITC 0); crude materials, inedible, except fuels (SITC 2); mineral fuels, lubricants and related materials (SITC 3); animal and vegetable oils, fats, and waxes (SITC 4); manufactured goods classified chiefly by material (SITC 6); machinery and transport equipment (SITC 7); and miscellaneous manufactured articles (SITC 8). In 2-digit SITC, ASEAN has:

The high comparative advantage with RCA over than 2 in all years are crude rubber; fixed vegetable oils and fats; animal and vegetable oils and fats; office machines and automatic data processing equipment; electrical machinery, apparatus and other appliances; cork and wood manufactures; fish, crustaceans and mollusc; and gas, natural and manufactured.
The mild comparative advantage with RCA between 1 and 2 in most years are telecommunications and sound recording apparatus; travel goods, handbags, briefcases, purses, sheaths; articles of apparel and clothing accessories; coffee, tea, cocoa, spices; photographic apparatus, optical goods, watches; furniture and parts; coal, coke and briquettes; sugar, sugar preparations and honey; and footwear.

Meanwhile, annex 6 presents RCA indexes of ASEAN-5 members for export products with an RCA value of greater one from 1998 to 2001 by 2-digit SITC, depending on data availability. We can conclude the advantage of ASEAN-5 by using an indicator averaged from RCA value between 1998 and 2001 and referring to 4 product categories[5] i.e., natural resource intensive products, unskilled labor intensive product, technology intensive product and human capital intensive product as follows.

Singapore has a significant comparative advantage in technology intensive products such as computers, electrical machinery, telecommunications equipment, and photographic equipment and it has a mild comparative advantage in natural resource intensive products such as tobacco, and petroleum products throughout the period. In addition, it is apparent that there is a declining trend in the RCA index of natural resource intensive products.

Malaysia has a substantial comparative advantage in natural resource intensive products e.g., vegetable oils and fats, crude rubber, wood, and petroleum; and in technology intensive products e.g., computers, electrical machinery, furniture, telecommunications equipment. Nevertheless most of RCA indices in resource intensive products have declined, while some RCA indices in technology intensive products has fluctuated throughout the period.

Thailand has a significant comparative advantage in resource intensive products such as crude rubber, rice, cereal, fish, and sugar. Moreover it has a mild comparative advantage in resource intensive products like manufacturing products such as leather and wood manufactures; in unskilled labor intensive products e.g., textile, furniture and footwear; and in technology intensive products e.g., office equipment, telecommunications equipment, electrical machinery and photographic equipment. The trend of RCA indices have increased in natural resource intensive products and have decreased in both unskilled intensive products and technology intensive products.

Indonesia has a high comparative advantage in resource intensive products such as crude rubber, wood, vegetable oils and fats, petroleum, ores, fish and cocoa; and in unskilled labor intensive products such as footwear, textile, furniture, and clothing. The change of RCA indices in resource intensive products have fluctuated all the period. As the trend of RCA indices in unskilled labor intensive products have slightly increased from 1998.

Philippines has a pattern of export competitiveness similar to Thailand and Indonesia. It is high competitive in natural resource intensive products e.g., leather and vegetable oils; in unskilled labor intensive products e.g. clothing; and in technology intensive products e.g., computer equipment, and electrical machinery. It is obvious that there is a increasing trend in the RCA indices.

In sum, from the RCA analysis, it is apparent that ASEAN-5 has comparative advantage in various goods: both natural resource intensive goods, unskilled labor intensive goods and technology intensive goods.

Annex 4 Revealed Comparative Advantage (RCA) Indices, 1-digit SITC

Source: Calculation based on COMMTRADE, United Nations Statistics Division, http://unstats.un.org/unsd/snaama/SelectionBasicFast.asp n.e.s. = not elsewhere specified

Annex 5 Revealed Comparative Advantage (RCA) Indices Greater One, 2-digit SITC

[1] See more detailed in section: AFTA on www.aseansec.org.
[2] Total ASEAN trade equals intra-ASEAN trade plus extra-ASEAN trade.
[3] ASEAN-5 includes Indonesia, Malaysia, Philippines, Singapore and Thailand.
[4] The concept of Revealed Comparative Advantage (RCA) is grounded in conventional trade theory that the original RCA index formulated by Balassa (1965). The revealed comparative advantage (RCA) is an indicator for measuring the exported ability of a country i.e., the gaining market share in the international trade of various products: , where i, j, w and x stand for product, country, world and exports, respectively. RCA is based on observed trade patterns; it measures a country's exports of a commodity relative to its total exports and to the corresponding export performance of a set of countries. If RCA > 1, then implies that the country has comparative advantage in that product.
[5] The commodity exports are aggregated into 4 product categories: (1) natural resource intensive products include food and live animals (chiefly for food), beverage and tobacco, crude materials, mineral fuels, animal and vegetable oils, manufactured goods classified by material such as leather, wood manufactures, pearls, precious stones, pig iron (2) unskilled labor intensive products include textile and textile products, manufactured goods like furniture, clothing, footwear, toys (3) technology intensive products include chemicals, machinery and transport equipment, telecommunication equipment, office machines, computers, aircraft, scientific instruments and (4) human capital intensive products include dyes and paints, steel, paper, non-electrical parts, tools, watches and clocks, printed matters, musical instruments.

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

DOT: Empirical Specification

2008-05-31T12:50:00.002+02:00

Indeed, the regional trading agreements (RTAs) has been a crucial role for the application of the gravity model in trade effects. In addition, we can conclude broadly that trade from country i to country j relates to their economic sizes (GDP or GNP), their populations, direct geographical distances and a set of dummies incorporating some kind of institutional characteristics common to specific flows. Therefore, in order to analyze the determinants influenced on ASEAN trade flow that accounts for a main objective in this paper, the typical gravity model specification is assumed as follows:

a. The gravity model from ASEAN countries' trade (i) to partner countries (j)

(4.2.3a)

where TRADEij denotes volume of trade from countries i to j , GDPi and GDPj denote gross domestic products of i and j , DISTij denotes distance between i and j , PCI_Dij denotes per capita income differentials of i and j , ASEAN denotes regional dummy variable that takes a value of 1 when the trading country i belongs to the preference trade agreement (AFTA) and 0 otherwise as follows: 1982-1991: zero for all countries 1992-2002: one for ASEAN member countries; zero otherwise , EU and NAFTA denote regional dummy variables that take a value of 1 when the trading partner country j belongs to the preference trade agreement (EU and NAFTA respectively) and 0 otherwise as follows: EU Integration:- 1987-1994: one for the EC-12; zero otherwise 1995-2002: one for the EU-15; zero otherwise North American Integration (NAFTA):- 1987-1993: zero for all countries 1994-2002: one for US, Canada and Mexico; zero otherwise, uij denotes error term and t denotes time period

The dependent variable is trade between pairs of countries in a given year. The first 3 independent variables are standard gravity terms that trade expected to increase with the size of the domestic economy and to decrease with distance. Thus the coefficients β1 and β2 should be positive while coefficient β3 is negative. The β4 would be positive if the H-O hypothesis holds and negative of the Linder hypothesis holds. The last 3 independent variables in equation 4.2.3a are intercept dummy variables intended to test for the effects of membership of regional groupings in ASEAN, EU and NAFTA respectively. Positive and significant coefficients on the regional dummies are taken as evidence of a RTA effect.

b. The gravity model of Intra-ASEAN trade

(4.2.3b)

where TRADE denotes volume of trade among ASEAN countries, GDP denotes gross domestic products of ASEAN countries, DIST denotes distance between ASEAN countries, PCI denotes per capita income of ASEAN countries, ASEAN denotes regional dummy variable that takes a value of 1 if both countries i and j belongs the same preference trade agreement (AFTA) and and 0 otherwise as follows: 1982-1991: zero for all countries 1992-2002: one for ASEAN member countries; zero otherwise.

The coefficients β1 and β3 should be positive while coefficient β2 is negative. The last one independent variable in equation 4.2.3b is intercept dummy variables intended to test for the effect of membership of regional grouping in ASEAN. Positive and significant coefficient on the regional dummy is taken as evidence of a RTA effect.

In empirical model (4.2.3a) we consider a sample of 25 countries. The countries are chosen on the basis of importance of trading partnership with ASEAN member countries and availability of required data. Five countries of ASEAN - Indonesia, Malaysia, Philippines, Singapore and Thailand - are included. From the NAFTA, 3 countries - US, Canada and Mexico - are considered. Twelve countries are taken from the EU. There are France, Benelux (Belgium and Luxembourg), Germany, Italy, Netherlands, UK, Denmark, Sweden, Austria, Spain, Greece and Portugal. Five Oceanic countries - Japan, China, Republic of Korea, Australia and New Zealand - are also included in our sample for the analysis of ASEAN's trade. As the empirical model (4.2.3b) we consider a sample of 5 ASEAN member countries.

The data were collected for the period of 1987 to 2002 (16 years). Our data in equation 4.2.3a consists, therefore, of a panel data of 100 trading pairs, with 1,600 observations; while the data in equation 4.2.3b are 20 trading pairs with 320 observations. All observations are annual. Data on trade (export plus import), GDP and GDP per capita are obtained from CD-ROM CHELEM database of Centre d'etudes prospectives et d'informations internationales. Data on distance (in miles) between each commercial city of ASEAN countries and other commercial cities of country j are obtained from calculation[1] by global distance calculator version 1. GDP and GDP per capita are purchasing power parity[2] in constant 1995 US dollar. Total trade, GDP, PCI are measured in million US dollar.

[1] The distance values are calculated on the latitudes and longitudes between commercial cities which obtained from web site, namely, www.export911.com/convert/distaCalc.htm and estimated on global distance calculator version 1 which obtained from http://mhpnet.com.
[2] The purchasing power parity (PPP) is the value converted into a rate of exchange that allows a standard comparison of price levels between countries. One PPP dollar has the same purchasing power in the domestic economy that the U.S. dollar has over U.S. economy.
[3] Egger (2000) and Zarzoso (2003).

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

DOT: Empirical Results

2008-05-31T12:45:00.014+02:00

This section is proposed to investigate the determinants of trade flows directly by using a gravity model which contains many determinants as shown in previously mentioned theories. That is, we model firstly ASEAN countries'[1] trade to 20 partner countries and secondly Intra-ASEAN countries' trade over a 16 years period (1987 to 2002). Regressions are run on trading pairs of annual trade flows that yield 1,600 observations for equation 4.2.3a and 320 observations for 4.2.3b. The trade is the dependent variable, while the independent variables (aside from a constant) are the GDP of the trading and partner countries, per capita GDP differential, and the distance between them. In addition, we attempt to estimate the effects of regionalism on trade flows from ASEAN countries. Hence bilateral trade between the trading and partner countries is added as binary variables to represent 3 regional groupings: ASEAN, EU and NAFTA. The results exhibited in the following are calculated by software namely STATA version 6.0.

As mentioned earlier, we estimate equation (4.2.3a) for aggregate trade flows from ASEAN countries to partner countries, and equation (4.2.3b) for aggregate trade flows among ASEAN countries using several methodologies. Firstly, for testing an individual effect, we use ordinary least square (OLS, aij= a). Besides, we apply the regression to cross-section means (between estimation). Secondly, for choosing the most appropriate model, we apply the Hausman specification. Finally, the FEM and REM are utilised for analyzing our gravity equations. In this section, we present the results and their explanation step by step in each equation as follow.

Gravity equation for ASEAN countries' trade to partner countries:

In the first step, we must test an individual effect by performing an F-test to check for the pool ability of the data. The restricted model is the pooled model given by equation (4.2.3), with the restrictive assumption of a single intercept (aij= a) and with the same parameters over time and across trading partners, as shown in Table 5-3. The unrestricted model, however, is the same behavioral equation but allows the intercept to vary across trading partners.

Table 5-3 shows the above test in 6 cases: whole ASEAN member countries across trading partners and individual ASEAN member countries (Indonesia, Malaysia, Philippines, Singapore and Thailand) across trading partners. The results from the F-test in all cases report that we cannot accept the null hypothesis of equality of individual effects. This indicates that the OLS results are biased and we have to select a model with individual effects. The between estimates exploit the between dimension of the data (differences between individuals), but ignore any information within individuals. In addition, the results in table 5-4 show that the coefficient estimates (GDP of trading and partner country, and PCI differential) for the ASEAN gravity model are similar to the pooling of data in table 5-3. Actually, this phenomenon entirely appears in individual ASEAN member's trade flow models. However OLS regression of ASEAN model shows that all variables except the NAFTA dummy variables present the expected sign at 1% level of statistical significance, while between regression of ASEAN model points that all variables except distance present the expected sign at level of statistical significance.

Table 5-3 Ordinary Least Square (OLS) results for gravity equation

Notes: 1. T-statistics are in parentheses: * denotes significance at the 1% level, ** denotes Significance at the 5% level and *** denotes significance at the 10% level.

Table 5-4 Between Regression (Regression on group means) results for gravity equation

The second step, in order to discriminate between the two models we test for the null hypothesis that the explanatory variables and the individual effects are uncorrelated using a Hausman test. The fixed effects estimates are consistent under both the null and alternative hypothesis whereas the random effects estimates are only consistent and efficient under the null hypothesis. Therefore REM will be preferred if the null hypothesis hold, otherwise FEM will be preferred. Tables 5-5 and 5-6 report respectively the estimated results of the FEM and REM and are employed in this discussion. Moreover, the estimates of the country-pair individual effects are omitted in space consideration.

The results in the Hausman test are shown in Table 5-5, and we have to reject the null hypothesis in all models of ASEAN countries' trade to partner countries that leads us to select the fixed effects estimates because the random effects estimates are inconsistent. Generally, in FEM, the intercept terms aij and bij are considered to be country specific, and the slope coefficients are considered to be the same for all countries. The intercept terms in REMs are considered to be random variables, instead of fixed country specific variables, but the slope coefficients are still considered to be the same for all countries.

Table 5-5 Regression results for the fixed effect model

Firstly, ASEAN trade flow model has R2 as 0.762, and F-test is 137.16. The coefficient of GDP of partner countries is positive and highly significant as expected. This implies that ASEAN countries tend to trade more with larger economies of partner countries. Our estimate suggests that a 1% increase in GDP of country j can increase ASEAN's trade with these countries by as much as 2.25%. While the coefficient of GDP of trading countries presents insignificantly.

The coefficient of per capita GDP differential between ASEAN and country j is also significant at 1% level and has positive sign. The coefficient value is 1.589 which implies that bilateral trade with country j increases as the per capita GDP differential increases but more than proportionately. The possible explanation for the positive sign is that the H-O effect (difference in factor endowment) dominates the Linder effect.

The distance variable (see table 5-3) is significant even at 1% level and has anticipated negative sign which indicates that ASEAN tends to trade more with its neighboring countries. The coefficient value is -1.08 which points that when distance between ASEAN countries and country j increase 1%, the bilateral trade between them decreases by 1.08%.

The regional integration dummies for ASEAN and NAFTA become statistically significant and have the correct sign, as dummy of EU has still the wrong sign but consistently significant. The interpretation of the coefficient on the regional integration dummy variables is also relevant for our analysis. Since our model is estimated in natural logs, all dummy variables are given a value of one in natural logs when the correspondent condition is satisfied and a value of zero otherwise. That is, the coefficient value of 0.055 in ASEAN dummy suggests that trade from ASEAN to partner countries is around 5.65% {[exp(0.055)-1]*100} more than expected from the gravity. While the coefficient value of 0.347 in NAFTA dummy indicates that trade from ASEAN to NAFTA members is about 41.48% {[exp(0.347)-1]*100} above what could be expected from the gravity model. On the other hand, exports from ASEAN to EU members is 15.6% {[exp(0.145)-1]*100} lower than expected levels.

Secondly, Indonesia trade flow model has R2 as 0.749, and F-test is 50.7. The variables: GDP in j countries, per capita GDP differential and distance are found to be highly significant at 1% level, while GDP in i countries is significant at 5% level. The positive coefficients of GDP in i and j countries imply that Indonesia's trade depend on them. The estimated results point that 1% increase in GDP of country i leads to 0.88% trade to j countries. Similarly, 1% increase in GDP of country j leads to 1.56% trade to j countries. The PCI differential has positive sign which supports the H-O model. With 1% increase of this variable, trade of Indonesia increase by 1.28%.

The distance variable is significant even at 1% level and has expected negative sign which indicates that Indonesia trade more with its neighboring countries. The estimated coefficient is -2.21 which implies that Indonesia's trade increase 2.21% with 1% decrease in distance between Indonesia and the partner countries. Surprisingly, all dummy variables are found to be insignificant. However there are only dummies of ASEAN and NAFTA have the correct sign.

Thirdly, Malaysia trade flow model has R2 as 0.864, and F-test is 96.13. The coefficient of GDP of partner countries and PCI differential are positive and highly significant as expected. The estimated results show that 1% increase of GDP of country j has an effect of 2.70% increase of its trade. The PCI differential has positive sign which supports the H-O model. With 1% increase of this variable, trade of Malaysia increase by 2.35%. However the coefficient of GDP of country i presents insignificantly.

The distance variable is found to be significant at 1% level and has expected negative sign. The estimated coefficient is -1.46 which implies that, with 1% decrease in distance between Malaysia and the trading countries, Malaysia's trade increase 1.46%. The NAFTA and EU dummy variables display to be significant, as NAFTA has the correct sign, EU has the wrong sign. The coefficient of NAFTA dummy is 0.59 which suggests that Malaysia's trade with NAFTA member countries is 0.8 times higher [exp(0.59)-1]. Nevertheless dummy of ASEAN still appears to be insignificant with positive sign.

Fourthly, Philippines trade flow model has R2 as 0.769, and F-test is 86.48. The coefficient of GDP of partner countries and PCI differential are positive and highly significant as expected, while coefficient of GDP of trading countries is positive at 10% level of significant. The positive coefficients of GDP in i and j countries, and PCI differential imply that Philippines' trade depend on them. The estimated results point that 1% increase in GDP of country i directs to 0.27% trade to j countries. Likewise, 1% increase in GDP of country j directs to 2.49% trade to j countries. The PCI differential has positive sign which supports the H-O model. With 1% increase of this variable, trade of Philippines increase by 1.04%. The distance variable has the expected negative sign at 1% significant. The estimated coefficient is -1.55 which implies that with 1% decrease in distance between Philippines and the partner countries, Philippines' trade increase 1.55%. The NAFTA dummy variable shows to be significant with the correct sign. The trade from ASEAN to NAFTA is 0.95 times [exp(0.669)-1] extremely higher than expected levels. However the dummies of ASEAN and EU show to be insignificant with positive sign and negative sign respectively.

Fifthly, Singapore trade flow model has R2 as 0.721, and F-test is 85.97. Interestingly, trade of Singapore are also positively responsive with the GDP of country j, but negatively responsive with the GDP of country i. The coefficient of GDP of country j and i are 3.56 and -1.21 respectively. The PCI differential has positive sign which supports the H-O model. With 1% increase of this variable, trade of Singapore increase by 3.22%.

The distance variable is found to be significant at 1% level and has expected negative sign. The estimated coefficient is -2.03 which implies that, with 1% decrease in distance between Singapore and the trading countries, its trade increase 2.03%. The EU dummy variable displays to be significant with a wrong sign. While the dummies of NAFTA and ASEAN appear to be insignificant with positive sign.

Finally, Thailand trade flow model has R2 as 0.833, and F-test is 86.2. Almost all variables except GDP of country i and ASEAN dummy are found to be highly significant at 1% level. The GDP variables of Thailand and country j have positive influences on Thailand's trade. The estimated results show that with 1% increase of GDP of country j has an effect of 1.96% increase of its trade. With per capita GDP differential has positive sign which supports the H-O model. With 1% increase of this variable, trade of Thailand increase by 1.46%.

Actually, the distance variable is found to be significant at 1% level and has expected negative sign. The estimated coefficient is -1.40 which implies that Thailand's trade increase 1.4% with 1% decrease in distance between Thailand and partner countries. The NAFTA and EU dummy variables show to be significant, as NAFTA has the correct sign, EU has the wrong sign. The coefficient of NAFTA dummy is 0.19 which suggests that Thailand's trade with NAFTA member countries is 20.9% higher [exp(0.19)-1]. At the same time dummy of ASEAN tends to be insignificant with negative sign.

In sum, from the empirical evidences of the 6 models, we observe that GDP, per capita GDP, distance and regional integration are the crucial factors for enhancing ASEAN's trade. The GDP of partner country, PCI differentials, and distance are found largely significant in all models. The GDP of trading country is only accounted for a determining factor of Indonesia, Philippines and Singapore trade flow model. Per capita GDP differential, which supports the H-O effect or Linder effect, is found as the major determinant of trade in all models. With regard to the distance effect, all models support that transportation costs are inversely related to the ASEAN trade. In terms of dummy of regional integration, almost all models are significant in NAFTA dummy in an anticipated way, as other dummies are not. However the goodness of fit of all models is entirely high.

Moreover the results of the determinants influence on individual ASEAN trade flow model indicate that GDP of partner countries, PCI differentials, and distance are significant at 1% level with expected sign in all models. The GDP of trading country is significant in almost all models with the various directions. The GDP of Indonesia, Philippines and Thailand has positive influences on their trade, while GDP of Malaysia, and Singapore has negative influences on its trade. The NAFTA dummy is significant at 1% level with expected sign in Malaysia, Philippines and Thailand trade model, while it is insignificant in the remained models. The EU dummy is statistically significant with unexpected sign in Malaysia, Singapore and Thailand trade model, while it is insignificant in the remained models. The ASEAN dummy is insignificant in all models, that is, individual ASEAN trade to partner countries is not depended on the membership of RIA.

Gravity equation for Intra-ASEAN countries' trade :

In this section we forecast the equation (4.2.3b) for aggregate trade flows among ASEAN countries. In the first step, we examine an individual effect by performing an F-test to check for the pool ability of the data as shown in Table 5-6. The results from the F-test indicate that we reject the null hypothesis of equality of individual effects; that is, the OLS results are biased and we have to select a model with individual effects. In addition, the results of between regression show that the coefficient estimates (GDP, per capita GDP and distance) are similar to the pooling of data in OLS. There are only 3 variables: Per capita GDP, distance and ASEAN dummy in OLS regression show the expected sign at 1% level of statistical significance, while there is only one variable in between regression i.e., distance shows the expected sign at 1% level of statistical significance.

Table 5-6 Regression results for OLS, between, and fixed effects model

The second step, in order to select an appropriate model among 2 models (FEM and REM) we test for the null hypothesis using a Hausman test. The results of the Hausman test shown in Table 5-6, we have to reject the null that leads us to select the fixed effects estimates because the random effects estimates are inconsistent.

Intra-ASEAN model has R2 as 0.809, and F-test is 42.99. The coefficient of GDP of ASEAN countries and per capita GDP are positive and highly significant as expected. This implies that ASEAN countries tend to trade more with their larger economies. The estimated results point that 1% increase in GDP of ASEAN countries leads to 0.90% trade among themselves. Likewise, 1% increase in per capita GDP leads to 1.70% trade among ASEAN countries.

The distance variable (see in OLS column) is significant even at 1% level and has anticipated negative sign which indicates that Intra-ASEAN trade is more with a common trading group. The coefficient value is -1.31 which points that when distance between ASEAN countries increase 1%, the bilateral trade between them decreases by 1.31%.

The regional integration dummy for ASEAN becomes statistically significant and has the correct sign. The coefficient value of 0.18 in ASEAN dummy suggests that ASEAN trade between themselves about 19.72% {[exp(0.18)-1]*100} above what could be expected from the gravity model.

In sum, from the empirical results, we observe that GDP, per capita GDP, distance and ASEAN regional integration are the significant factors for enhancing Intra-ASEAN trade. In addition, the goodness of fit of all models is entirely high.

[1] The ASEAN-5: Indonesia, Malaysia, the Philippines, Singapore, and Thailand (original ASEAN members).

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

DOT: Conclusion

2008-05-31T12:42:00.002+02:00

This paper explores a theoretical justification for using the gravity model in the analysis of bilateral trade and applies the gravity model to analyze the ASEAN's trade with its major trading partners using the panel data estimation technique. We have established the application of the gravity model in applied research of bilateral trade which is theoretically justified. There are wide ranges of applied research where the gravity model is used to examine the bilateral trade patterns and trade relationships. Our gravity equations were estimated on trade between ASEAN countries and 20 trading partners: 12 EU member countries, 3 NAFTA member countries and 5 oceanic countries between 1987 and 2002; and among ASEAN-5 countries between 1987 and 2002. In doing so, we aim to analyze which are the determinants of ASEAN and trading partners trade flows and of Intra-ASEAN trade flows.

In the first model, we have estimated the gravity model of ASEAN and partner countries with all ASEAN trade and individual ASEAN trade. Our results show that trader income has a positive effect in bilateral trade flows particularly in Indonesia, Philippines and Singapore model. It indicates that larger economies lead to more trade. While the trading partner income, as expected, has a positive influence in bilateral trade flows of all models, indicating that bigger countries trade more than smaller countries. In addition, we investigated that per capita GDP differentials play a significant role to explain bilateral trade flow in a panel data framework. However, per capita income differential in all models supports the H-O hypothesis than the Linder hypothesis. It may be the case that per capita income differential is the proper representation of the factor endowment differential. Then trade between countries will be larger with the larger difference in factor endowments.

With reference to the distance estimate, our findings support the hypothesis of the importance of this variable since it is all statistically significant and presents the expected sign. That is, transportation cost is found to be a significant negative factor in influencing the ASEAN trade. This implies that ASEAN would do better if an ASEAN trade more with its neighbors. This is also evident from the country specific effects. When testing regional integration effects, the empirical results in ASEAN model show that ASEAN dummy variable is significant at level of statistically significant with expected sign as well as NAFTA dummy variable. We can suggest that belonging to two of the three preferential arrangements fosters trade, that is, regional integration of ASEAN and NAFTA can attract the greater ASEAN's trade to partner countries. Meanwhile EU dummy variable becomes a negative sign and is statistically significant, suggesting a contradictory result obtained from the regional integration effect. However this study does not consider the difference between trade creation and trade diversion, thus the results have to be interpreted with caution.

In the last model, we have forecasted the gravity model of Intra-ASEAN trade. Our result shows that ASEAN income has positive effects in Intra-ASEAN trade flows. It indicates that larger economies of ASEAN lead to more trade within themselves. With regard to per capita GDP, it plays a significant role to explain trade flow among ASEAN members in a panel data framework with supporting the H-O hypothesis. Then trade among ASEAN countries will be larger with the larger difference in factor endowments. With the distance effect, our findings support the hypothesis of the importance of this variable since it is all statistically significant and presents the expected sign. This implies that ASEAN would do better if there were more trade with a common trading group or Intra-ASEAN. In estimating an ASEAN regional integration effect, we find that it presents a positive sign with statistically significant. That means the membership of ASEAN can foster the higher ASEAN trade.

In summary, there are 4 general aspects to be taken into consideration for ASEAN trade to partner countries and for Intra-ASEAN trade as follows. Firstly, trade relations in almost all models demonstrate that ASEAN's trade figure not only depends upon GDP of trading and partner countries but also relates to per capita GDP differentials as well. Secondly, the value of trade with ASEAN neighboring countries is relatively prominent. Thus the privilege of geography can be used to expand ASEAN's trade to its neighboring countries. Thirdly, ASEAN's trade relations with regional countries are remarkable. Consequently, the economic integration influences on the expansion of trade to regional markets especially NAFTA market. Finally, the ASEAN regional integration is an essential instrument in the higher ASEAN's trade. Thus, the enlargement in the economic cooperation in particular Free Trade Area (FTA) to the potential countries can foster the higher value of trade in ASEAN countries.

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

DOT: Bibliography

2008-05-31T12:22:00.003+02:00

ASEAN Secretariat. (2003). AFTA.
ASEAN Secretariat. (2003). ASEAN Statistical Yearbook 2003.
Chelem. (2003). Harmonised Accounts on Trade and The World Economy: International Trade Database and GDP Database. Centre d'etude prospective et d'informations internationales.
Egger, P. (2000). A note on the proper econometric specification of the gravity equation. Economics Letters, 66 (2000), 25-31.
Sanso, M., Rogelio C. and Fernando S. (1993). Bilateral Trade Flows, The Gravity Equation, and Functional Form. The Review of Economics and Statistics, 75, No.2 (May 1993), 266-275.
United Nations Statistics Division. (2003). National Accounts Main Aggregates: COMMTRADE.
Zarzoso, I.M. and Felicitas N.L. (2003). Augmented gravity model: An empirical application to Mercosur-European Union trade flows. Journal of Applied Economics. Vol. VI, No.2 (Nov.2003), 291-316.

Outline:
Abstract Introduction RCA Empirical Specification Empirical Results Conclusion Bibliography

FTA: Introduction

2008-05-30T11:10:00.002+02:00

ASEAN-China free trade area: evidence from GTAP model
January 2004

This study is an attempt to better understand the ASEAN-China free trade area with respect to the GTAP model, based on the study of Chirathivat (2002).

The ASEAN[1]-China economic cooperation seriously occurred in November 2002 on the Framework Agreement on Comprehensive Economic Cooperation between ASEAN and China. In this framework, the establishment of ASEAN-China Free Trade Area (FTA) is one of principal objectives implemented on an accelerated basis. ASEAN-China FTA will be engaged within 10 years with special and differential treatment for the older ASEAN members and flexibility for the newer members namely Cambodia, Lao PDR, Myanmar and Vietnam.

The remained measures for comprehensive economic cooperation between ASEAN and China consist of progressive liberalization of trade in services with substantial sectoral coverage; establishment of an open and competitive investment regime that facilitates and promotes investment within the ASEAN-China FTA; establishment of effective trade and investment facilitation measures including simplification of customs procedures and development of mutual recognition arrangements; and establishment of appropriate mechanisms for the purposes of effective implementation of this agreement.

ASEAN Secretariat (2001) noted that the establishment of a FTA between ASEAN and China would create an economic region with 1.7 billion consumers, a regional GDP of about US$2 trillion and total trade estimated at US$1.23 trillion. Besides they believed that the removal of trade barriers between ASEAN and China would lower costs, increase intra-regional trade and increase economic efficiency. The establishment of ASEAN-China FTA will create a sense of community among ASEAN members in East Asia and allow both ASEAN and China to have a larger voice in international trade affairs on issues of common interest.

The fact that the study about FTA is attempts to measure trade creation and trade diversion in order to interest in welfare gain or loss. Venables (2000) illustrated that empirical work on trade creation and trade diversion has taken 2 main forms[2] as follows: (1) The econometric studies which seek to quantify the changes in trade flows attributable to membership of a regional integration agreements (RIAs), and (2) The computer simulation studies of the full general equilibrium effects on RIAs membership.

Past computable general equilibrium (CGE) analyses of the macroeconomic effects concerned on ASEAN-China FTA have focussed solely on the measuring the impacts of ASEAN Free Trade Area (AFTA) in Adams et al (1995). This paper indicates that ASEAN would be better off from AFTA, and the trade volume within ASEAN would be magnified. In addition if the cooperative market opening toward the other region is combined with AFTA, the positive effect will be increased remarkably. However, before opening the market, ASEAN must consider the cost of trade liberalization, that is, the additional burden on the government budget and trade accounts and the resulting inflation. Thus to lessen the cost, the combination of domestic policy and foreign policy must be considered and adjusted appropriately.

During the last few years, most CGE analyses of the macroeconomic effects related to ASEAN and China have concentrated entirely on the evaluation the impact of China's liberalization. Shantong et al (2002) analyzed the impact of China’s WTO accession using a two-region Chinese CGE model discovered that China would gain from significantly in terms of economic efficiency. When China fully implements its commitment on market access, in 2010, its real GDP and welfare measured in Hicks equivalent variation (EV) would be increased continuously. The large gains in real GDP mainly results from the enhanced efficiency of resource allocation brought about by an increased specialisation in accordance with China’s comparative advantage. Wang’s (2002) application of CGE methods with world trade increases substantially as a result of China’s accession. The simulation results showed that the main winner from China’s WTO accession is China itself. The developing countries with an endowment structure similar to China, like those in South America and Southeast Asia, may experience keener competition in labor-intensive exports and lower prices for their products.

In particular, Chirathivat (2002) attempts to measure the impacts of the ASEAN-China FTA on welfare, trade balance and other important macroeconomic variables with a linked computable general equilibrium (CGE) model system namely the Global Trade Analysis Project (GTAP) model. This paper points to the fact that there would certainly be trade gains for both ASEAN and China in a FTA. Trade creation will easily offset trade diversion with ASEAN getting a sight trade diversion while that same trade diversion would not be so obvious for China. As a result of this study, China's strong growth, it seems that China would require more input imports and ASEAN could provide as an alternative source of inputs for natural-resource based and immediate inputs in an FTA.

Special attention in the mentioned papers, we obviously mention that CGE model is a valuable tool for trade policy evaluation and it has a significant composition in demonstrating the impacts of trade liberalization. However for better understanding in CGE model, we must actually recognize in the theories underlying CGE model including concern on the model's theoretical structure precisely.

Therefore Chirathivat's article was raised for studying in this paper due to the reason that the it showed the effects of such tariff and non-tariff elimination by using CGE model, paying particular attention to the effects within ASEAN-China FTA. The purpose of this study is to illustrate precisely in theories underlying on computable general equilibrium (CGE) model, besides to extend obviously in the model's structure namely GTAP model.

[1] ASEAN (Association of Southeast Asian Nations) created in 1967 by the Bangkok Declaration, ASEAN began as a political/diplomatic group, but has recently placed a much stronger emphasis on economics. A milestone was the creation of the ASEAN Free-Trade Area (AFTA) in January 1992. Original members were Indonesia, Malaysia, Thailand, the Philippines and Singapore; Brunei joined after independence from Great Britain in 1984; Vietnam joined in 1995; and Laos and Myanmar (Burma) were accepted in 1997. Cambodia finally joined in 1999; hence, ASEAN now is comprised of all Southeast Asian countries.
[2] In addition Venables (2000) mentioned that the first empirical approach based on the gravity model estimates bilateral trade between countries. The estimated effect indicates whether countries in a RIA trade more or less than the rest of the world otherwise be expected. The second empirical approach based on computable equilibrium modeling involves in constructing a full computer model of the economies under study and use of the model to simulate the effects of the policy changes associated with the RIA. Such a model typically contains a great deal of microeconomic detail, so can be used to predict changes in production in each sector, and changes in factor prices and real incomes. The strength of these models is that they have sufficient micro-economic structure for the effects of a policy change to be traced out in detail, and its real income effects to be calculated. They are also often used for prediction – to estimate the likely effects of a policy change before it is implemented.

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: Theoretical framework

2008-05-30T11:08:00.010+02:00

The purpose of this section is to bring the main theoretical framework addressing welfare effects on regional integration agreements (RIAs) that are accompanying with countries formed in the objective of reducing barriers to trade between the member countries e.g. Free Trade Area (FTA). The fact that a studying in welfare effect on RIAs must keep in mind trade creation and trade diversion as a result of tariff barrier and non-tariff barrier. Thus we first consider in the concepts of them simultaneously the effects on trade diversion and trade creation in RIAs, then proceed in free trade area theory, and finally briefly describe in general equilibrium theory.

1. Trade creation, trade diversion and welfare effect

The concepts of Jacob Viner’s trade creation and trade diversion lead to a better understanding in analysis not only the theory preferential trading arrangements, but also the general theory of second-best welfare economics that may be welfare-improving or welfare-reducing, depending on the specific features. Pomfret (1997) noted that the distinction between trade creation and trade diversion was crucial for illuminating this point as well as the clarification of trade diversion. The fact that the resource misallocation associated with trade diversion is the fundamental cost of preferential trading agreements which has to be weighted against any benefits from lower tariffs on some imports.

Venables (2001) and Srivivasan et al (1993) mentioned the Viner’s trade creation and trade diversion effects on RIAs. The former effect, trade creation in which trade between countries expands in accordance with international comparative advantage, is the substitution of imports of lower-cost goods produced by a country’s partner for its own domestic products. That is a reduction in tariffs with all trading patterns raises welfare. The latter effect, trade diversion in which trade between countries expands as a result of the preferential treatment given to imports from within region as compared to those from the rest of the world, is the shift in imports from the least-cost exporter to the more expensive product from the nation’s partners whose exports benefit from the preferential market access. That is a preferential reduction affecting only patterns in the regional integration agreements (RIAs) may reduce welfare.

For better understanding, Panagariya A. (1999) showed the model under perfect competition, trade diversion is associated with a welfare loss and trade creation with a welfare gain. In figure 2-1a supposes country A eliminates the tariff on country B but still retains it on country C, given PC+t > PB, thus A imports goods from B rather than C at price PB. This change substitutes the less efficient B for the more efficient C which Viner's terminology called "trade diverting".Another case, supposing the initial non-discriminatory tariff in A is t'. Afterwards A removes tariff on B that leads to switch in trade from the higher-cost suppliers in A to lower-cost suppliers in B, in Viner’s terminology called “trade creating”. Viner said an increase and decrease welfare depend on the relative magnitudes of trade creation and trade diversion.

Meanwhile Panagariya A. (1999) mentioned that James E. Meade (1995) pointed out Viner’s analysis[1], the relative magnitudes of trade creation and trade diversion, are insufficient to determine the welfare effects in two reasons. First, benefits of preferential liberalization depend on not only the extent of trade creation, but also the magnitude in which costs are reduced on each unit of newly created trade. In terms of figure 2-1a, trade creation, area f+g, equal to 0Q0*PBPC while trade diversion, area e, equal to 0Q0*PAPB. In this case we cannot infer the gain or loss from 0Q0. Second, formalized by Franz Gehrels (1956-57) and Richard Lipsey (1957) demonstrates the demand curve in country A is a downward-sloped demand curve see in figure 2-1b. Assume t is the initial non-discriminatory tariff and country A imports OQ0 from C. If A removes the tariff on B, then the least-cost producer is an expansion of imports from OQ0 to OQ1. The result is a loss of area e on the original imports but a gain of area h on new imports and there is a redistribution of tariff revenue to consumers in A in area f. In principle, area h can be larger than area e, that a wholly trade diverting, A can lead to an improvement in welfare.

Figure 2-1a Trade creation and Trade Diversion

Figure 2-1b A welfare gain under Trade diversion

In figure 2-1a, trade diversion, country A loses tariff revenue e+f, e paid for the higher cost production in country B and f become A’s consumer surplus. The net loss to A and the world is area e equal to 0Q0*PBPC. Trade creation, country A gains tariff in consumer’s surplus or welfare gain is area f+g equal to 0Q0*PAPB.

Although Srinivasan et al[2] noted that the changes in trade patterns are not reliable predictors in welfare effect on RIAs but mentioned to P. Wonnacott and R. Wonnacott (1976) and C. Kowalczyk (1992), a country to enter a RIA believe that the effects of its partners removing their trade barriers to its exports are sufficiently large to offset any loss from the concessions that it makes to them including demonstrated in M. Kemp and H. Wan (1976), while maintaining the same volume of trade with the rest of the world, any group of countries could benefit from trade liberalization within the group. Consequently, aggregate world welfare can rise from increased intra-regional trade thus countries can gain from forming preferential trading arrangements, and such benefits need not be at the expense of the rest of the world.

Nevertheless tariff barriers affected trade creation, trade diversion and welfare, as well non-tariff barriers (NTB) can lead to conclusions supporting the more favorable assessment of RIAs. Pompret (1997) mentioned that conclusion in comparison between tariff and non-tariff barrier at best will have the negative effects on a tariff and at worst will be welfare-reducing. For example of cost-increasing measures, if NTB is not purely cost-increasing, then moving from a non-discriminatory NTB to a situation in which some preferred suppliers are exempted from the NTB could still lead to welfare-reducing trade diversion. In summary, discriminatory reduction of a NTB is more likely to be global-welfare-increasing than is discriminatory tariff reduction, because of the extra source of gain from the reduction of unproductive behavior.

2. Free Trade Area (FTA) Theory

The essential features of a free trade area (FTA)[3] are (1) the member countries retain the power to fix their own separate tariff rates on imports from the rest of the world and (2) the area is equipped with rules of origin[4], designed to confine intra-area free trade to products originating in, or mainly produced in, the area. Although the member countries in FTA may be accompanied by trade creation and trade diversion but there are the effects of FTA which occur in the irrelevance of rules of origin and induce the indirect trade deflection. Consequently in FTA, third-country suppliers have an incentive to route their trade through the low-tariff FTA.

Pompret (1998)[5] supposed that the country A’s tariff on a good was lower than the country B’s tariff, then the third-country suppliers of the B market would use country A as the point of entry into FTA. Rules of origin were intended to ensure that goods entering B’s duty-free from country A were truly of A, and not third-country goods trying to evade the B’s tariff. As long as the good is also produced in the low-tariff FTA member, rules of origin cannot affect B’s output and consumption. In figure 2-2 the price in A (PA) is equal to the world price (PW) plus the A’s tariff, and free trade with A will drive the A’s price down to the same level. The impact is the same whether foreign goods are trans-shipped to B via A or A’s supplies are deflected from the home market to the B’s market, as long as A’s output exceeds B’s import demand, ab, at the intra-FTA price.

With rules of origin, ab will be met out of A’s output 0b, so that the A’s tariff revenue increases from bc(PA-PW) to ac(PA-PW). The net welfare effect on B is ambiguous because the ultimate source of added imports is the global low-cost supplier, not the preferred partner. A’s increased tariff revenue (shaded area) must exceed the part of B’s lost tariff revenue that is not offset by increased consumer surplus (stippled area), the FTA as a whole must benefit from the displacement of high-cost internal suppliers by external imports. There is no trade diversion, so the global welfare effect is positive thus rules of origin can be welfare improving.

Figure 2-2 Direct and indirect trade deflection

3. General equilibrium theory

Due to the fact that general equilibrium theory known as Walrasian equilibrium describes the efficient allocation or welfare among the economic agents. Indeed, it is Pareto efficiency (optimum) or the efficient allocation in the economy i.e., the situation that is impossible to make anyone better off without making someone worse-off. To understand the theoretical concepts[6], we show the general equilibrium in 2 cases: general equilibrium in consumption and general equilibrium with firms.

General equilibrium in consumption

General equilibrium with firms

[1] In addition Pomfret (1997, p.183) mentioned that James E. Meade (1995) pointed out the Viner’s analysis is most suitable where demand elasticities are zero and supply elasticities are infinite.
[2] See in Anderson K. and Richard B. (1993), ch.3.
[3] See in Peter R. (1980), ch.2.
[4] The purpose of rules of origin is to limit trade deflection, that is the redirection of imports through the country with the lowest tariff for the purpose of exploiting the tariff differential (Peter R., 1980, p.28).
[5] Mentioned to Pompret (1998), p.186-187.
[6] See in David.M.K. (1990).

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: Theoretical structure and calibration

2008-05-30T11:07:00.008+02:00

Model’s theoretical Structure and Calibration

This study bases mainly on the work of Chirathivat (2002) that in GTAP model is a multi-region and multisector model with 45 regions and the 50 production sectors. First, we consider the basic theoretical assumptions, second proceed in theoretical model linked in GTAP model structure, and finally display empirical results.

1. The theoretical assumption

The basic theoretical assumptions made in GTAP are as follows.

(a) Regional Household: Regional household behavior is governed by an aggregate utility function whose Cobb-Douglas preferences specified over composite private expenditures, composite public expenditures and savings[1].

(b) Private Demand: Private expenditures are governed by a Constant Difference of Elasticity (CDE) function which Hertel (1997) showed that it was first proposed by Hanoch (1975). It lies midway between the nonhomothetic Constant Elasticity of Substitution (CES) function on the one hand, and the fully flexible functional forms on the other which can also shown the CES and the Cobb-Douglas in case of CDE function.

(c) Production: Production is assumed to be described by a multi-level Leontief-type production function shown in figure 3-1. The upper nest is a Leontief-type production function involving value added and intermediate inputs. The technical coefficients of this top-level nest are generated from the Social Accounting Matrix (SAM[2]) constructed for each region. Value added is produced through a CES function of the five primary factors of production. Each intermediate input is in turn produced using domestic and imported components known as the Armington Assumption[3] with the technical process described by a CES function. Finally, imported components are a mix of imports from the other regions in the global model with the technical process again described by a CES function.

Figure 3-1 Production structure

(d) Macro closure : The macro closure of a CGE model with macro adjustment behavior that the macro balances in each region will be composed of government deficit or surplus, aggregate saving and investment and balance of trade.

Finally, the equilibrium of the model is defined as a set of prices and quantities for goods and factors in all regions such that (a) demand equals supply for all goods and factors; (b) each industry earns zero profit; and (c) gross investment equals aggregate saving in each region. The model is neoclassic in nature as prices in each region's product and factor markets are assumed to be flexible and arable land for agriculture in each region is assumed to have a fixed amount.

2. The model’s theoretical structure

For an easier understanding in model’s theoretical structure, we will explain the basic theory relies on the aforementioned assumptions: household expenditure, production and trade, simultaneously the calibration in each condition. However this structure will be applied for exploitation in GTAP model. Suppose the open-economy illustrated in the model has 2 goods and 2 factors, the theoretical structure[4] is shown respectively as follows.

(a) Household Expenditure

(b) Private Expenditure

(c) Production

(d) Trade function

[1] Hertel (1997) mentioned that savings is included in the utility function derives from the work of Howe (1975), who showed that the intertemporal, extended linear expenditure system (ELES) could be derived from an equivalent, atemporal maximization problem. In addition Howe’s result, linking in a well-defined intertemporal maximization problem, is applied for a Stone-Geary utility function in GTAP model where all subsistence shares are equal to zero.
[2] SAM is the entries records financial flows between agents, for example in simple open economy model, production sectors, consumers and external account are shown as columns while goods and factors are shown as rows. A negative entry in the SAM in the cell in row i, column j indicates that sector j demands good i. A positive entry indicates that sector j produces good i if j is a production sector, or that consumer j has an endowment of factor i. Income-expenditure relationships dictates that all columns must sum to zero, and supply = demand relationships mean that all rows must sum to zero (Reed, 1996 : p.6).
[3] That is firms decide on the sourcing of their imports; then, based on the resulting composite import price, they determine the optimal mix of imported and domestic goods (Hertel, 1997 : p.40-41). [4] See in Reed et al. (2004).

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: GTAP simulation

2008-05-30T11:06:00.005+02:00

The GTAP simulation involved complete removal of all ad valorem import tariffs and tariff equivalent of bilateral non-tariff barriers on source import in each country. The model discussed in the previous section has been contained in the GTAP database by calibrating the model to each country’s benchmark equilibrium data set of 1995. In addition, there are a smaller 10 region and 10-sector aggregation of the larger 45 regions and 50-sector GTAP model.

The 10 regions used in our simulation are Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, China, USA, EU, Japan and the rest of the world. The GTAP database does not included all ASEAN countries, and hence Brunei Darussalam, Cambodia, Laos and Myanmar have not been included.

The 10 production sectors are cereals and crops, food, vegetable oils and fats, other agricultural products, textiles and apparel, petrochemicals, electronics and machinery, motor vehicles, other manufactures and services. The particular aggregation employed highlights important sectors of interest to the ASEAN countries. The mapping from the 45 country and 50-sector grouping of GTAP to our 10x10 structure appears in simulation.

GTAP model will appraise the effects on macroeconomic variables computed under model structure and assumptions previous mentioned. The macro effects are explained in terms of rental price of capital, real wage rates, capital and real GDP, terms of trade, real domestic final demand, real domestic saving and trade volumes. The results are appeared in equations shown as follows[1]:

Equations of aggregate indices of prices received and paid for products sold and purchased by each region. The difference between them measures the percentage change in each region's terms of trade.
Equations of the regional equivalent variation and worldwide equivalent variation.
Equations of the percentage in the region-specific consumer price index.
Equations of the regional gross domestic product (GDP) with a value index, which accounts for changes in prices and quantities, and a price index, which accounts for changes in prices only.
Equation of the quantity index for domestic product derived from the difference between value index and price index.
Equations of the percentage change in export and import values: (1) by commodity and region, (2) by region for all traded commodities, and (3) by commodity for all regions in the world.
Equations of the percentage change in the value of total world trade.
Equations of the percentage change in the value of total world output by commodity.
Equations of the associated price indices.
Equations of The aggregate trade and aggregate output.
Equations of the change in trade balance by commodity and by region, in which it is a value-based concept refers to the changes in the current account for each region. However these equations do not play a role in determining the equilibrium solution but can compute after the fact.

[1] See in details in Hertel (1997), p.61-64.

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: Empirical Results

2008-05-30T11:05:00.002+02:00

The results[1] are reported for simulation of elimination of tariff and non-tariff barriers in macroeconomic impacts and sectoral impacts as follows.

1. Macroeconomic impacts

China's macroeconomic impact

The results from GTAP simulation show that ASEAN-China FTA will lead to a decline in price level due to the reduction of China's import tariff rates. In addition, FTA will results in higher demands for China exports due to the reduction of ASEAN's tariff rates and greater competitiveness of China products in the world market. Higher export demands lead to greater demands for primary inputs such as labor and land. As the results, average wage rate will increase by 0.61% while land rent will increase by 0.23%. In addition, the import tariff cut will result in a decline the GDP deflator by 0.2%

Tariff elimination between ASEAN and China will boost the total China's exports by 2.4%. The trade creation effect will lead to a sharp rise in China's exports to ASEAN by 23%. In addition, the exports to other countries will slightly expand due to cost reduction from import tariff cut which strengthens the competitiveness of Chinese products in the world market. However ASEAN-China trade agreement will also create trade diversion also. China will increase its import value from ASEAN by 53.3% while decrease its import value from U.S.A. by 2.6%, Japan by 1.4% and the EU by 1.7%.

The FTA has impact not only on external demand but also internal demand. Higher export income and lower commodity prices will increase private consumption in China by 0.4%. Private investment is expected to increase by 0.7%, caused mainly by greater export opportunities. Overall, the welfare of China will improve by 1,787 million US$.

ASEAN's macroeconomic impact

ASEAN-China FTA will mainly benefit ASEAN through an increasing market access in China's huge market. A rise in ASEAN's exports, such as rice, sugar, vegetable oil, textiles and clothing increases the demand for primary factors of products. Wage and rental price of land will rise by 1% and 3.6% respectively. This leads to an increase of 0.6% in the GDP deflator.

The FTA results in a significant trade creation. Namely, there will be 53.3% surge in ASEAN's exports to China while ASEAN's imports from China will rise by 23%. This leads to an increase in the total exports of 1%. Due to cheaper products from China and expansion of overall economy, ASEAN's total imports from China and the rest of the world will increase by 1.3%. As a result, trade balance may decline by $177.7 millions. However FTA will create some trade diversion effects. The intra-trade within ASEAN members will significantly decline. While the exports of ASEAN to U.S.A. and Japan will drop 0.8% and 1.4% respectively.

The FTA expands not only ASEAN's external sector but also internal sector. Gross domestic investment in ASEAN will rise by 0.8%. While the private consumption will increase by 0.4%. As a result, the real GDP will increase by 0.4%. This leads to increase in welfare of ASEAN by 2,986 million US$.

2. Sectoral impacts

China's sectoral impact

The GTAP results show both positive and negative FTA impact on each sector. On the positive side, some sectors such as electrical appliances, textiles, motor vehicles & parts, and drinks & tobacco are expected to gain from lower input costs to lower China's tariffs. On negative side, some sectors may experience lower output resulting from import substitutions.

ASEAN's sectoral impact

According to the GTAP results of sectoral impact, FTA creates both positive and negative impacts on productive sectors. On the positive side, some sectors such as rice, sugar, vegetable oil, textile, chemical, rubber & plastic products and vehicles & parts may benefit from more exports to China. On negative side, due to lower ASEAN's trade barriers, imports from China cause a decline in ASEAN's output of vegetable and fruits.

[1] See empirical results in tables in Chirativat (2002).

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: Critical Analysis

2008-05-30T11:04:00.002+02:00

In the previous sections, the study of Chirathivat (2002) was exploited not only to study ASEAN-China economic integration; free trade area; theoretical framework, methodology, and interpretation aspects, but also to be a starting point of various new ideas. Therefore in this section we pay attention to present different ideas from Chirathivat (2002) in order to satisfy the following questions:

Is it sufficient to conclude that the trading between ASEAN and China induces the trade creation and welfare effect?

Indeed, economic cooperation framework between ASEAN and China focus mainly on the establishment of an ASEAN-China Free Trade Area; tariff and non-tariff reduction. Thus more intra-regional trade activities will induce more intra-regional financial activities. Many theories link importance between trade and finance for example; more trade requires more hedging through financial markets, eventually financial structure will be developed. This in turn facilitates trade. Whether the greater financial flows e.g., a foreign direct investment (FDI) lead to the greater trade. Whether monetary and financial cooperation e.g., fixed exchange rate reduces costs associated with trade.

From the aforementioned importance, studying about economic integration e.g. ASEAN-China FTA should analyze in terms of financial integration simultaneously trade integration. Laurenceson (2003) indicated that China and ASEAN are already highly integrated with respect to goods and services markets. In contrast, barriers to financial integration remain significant. There are two main implications of these findings. Firstly, they indicate that the financial sector and international capital flows must be explicitly addressed in computable general equilibrium (CGE) models if they are to produce relevant simulations and policy guidance in the future. Thus, the relevance of previous simulations conducted using static CGE models that focus almost solely on trade are highly questionable. Secondly, given that goods and services markets already appear quite integrated, the impact of future liberalization mandated through the WTO and the China-ASEAN FTA will likely be most pronounced in financial markets.

Static computable general equilibrium method, is it adequate to apply in analyzing welfare effects in era of globalization?

The fact that the CGE model used in Chirathivat (2002) accounts for evaluation in static effects. Static effects of regional integration occurred whenever reductions in barriers to trade through inherent changes in price can indicate the impacts from regional integration. However the phenomenon of globalization has served to arouse economists to look at the dynamic effects[1] of regional integration e.g., technology transfer, economies of scale, FDI and X-efficiency (intellectual and organizational “idea” transfers). Notwithstanding static analysis is not good enough for representative of actual impacts in studying regional integration, dynamic analysis becomes to play a key role in analyzing economic integration. However it is worth noting that several recent articles have made important progress in dynamic effects e.g. Shantong et al (2002) and Silva et al (1996).

Shantong et al (2002) applied a two-region recursive dynamic Chinese CGE model in evaluating the impact on China's economy from its accession to the WTO. The dynamics of the model originate from the accumulation of productive factors and productivity changes. The analysis shows that gains China would derive from a more efficient allocation of resources and from the elimination of MFA quota. In addition, Silva et al (1996) estimated economies of scale and imperfect competition effects of trade liberalization on the Australian economy within CGE model. Simulation results show that tariff reforms caused import-competing industries such as textiles, clothing and footwear (TCF) to shrink. On the other hand, exporting industries such as agriculture and mining expanded.

Is the information analysis of the former ASEAN suitable to represent the current ASEAN?

Due to the fact that 10 regions solely used in simulation is Indonesia, Malaysia, Philippines, Singapore, Thailand, Vietnam, China, U.S.A., EU, Japan and the rest of the world. The GTAP database does not include all ASEAN countries, and hence, Brunei Darassalam, Cambodia, Laos and Myanmar. Therefore the conclusion that FTA between ASEAN and China is feasible would have been existent if the simulation included 4 countries and the results showed that ASEAN gained from the FTA.

Finally, what do we realize in a success of ASEAN-China economic integration apart from trade gain?

The key success of ASEAN-China economic cooperation creation must recognize in factors caused competitiveness between them. Economic research forum (1996) mentioned that East Asia’s recipe for competitiveness consists of export orientation and not import substitution; macroeconomic stability and not inflationary pressures; high rates of domestic resource mobilization and private investment; rapid accumulation of human capital and no discrimination in favor of special groups; catching up via technology acquisition and not reliance on turnkey transfers; promoting global competitiveness via proactive industrial policy; and building consensus for a national vision and not top-down decision-making. Hence we can conclude that static effects of ASEAN-China economic cooperation induced trade gain may not show a significant success of economic integration between ASEAN and China in globalization.

[1] Dynamic effects are thought to make the gains from trade substantially larger than in the static model caused by regional integration that should grow over time, e.g., technology transfer, “X” efficiency, economies of scale, and changes in investment flows.

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: Conclusion

2008-05-30T11:03:00.003+02:00

In this paper, we have attempted to provide useful insights in understanding the theoretical framework in studying the impacts on ASEAN-China FTA from the tariff and non-tariff elimination and demonstrate that Computable General Equilibrium (CGE) model can be a valuable tool for trade policy evaluation. Chirathivat's article is advanced for studying in the aforementioned objectives.

The understanding in concepts of trade creation and trade diversion is a starting point in getting along the welfare effect occurs in decision-making to participate in regional integration agreements e.g., free trade area. Trade creation is the displacement of inefficient domestic production with more efficient partner country production through formal regional integration. On the other hand trade diversion is the displacement of more efficient non-partner imports with less efficient partner country imports due to discrimination inherent in a preferential trading arrangement. In addition, general equilibrium theory is a significant theory underlying on CGE model, which explain the efficient allocation or welfare in the economy by making anyone better off without making someone worse-off.

In Chirathivat's work, it modeled in a multi-country computable general equilibrium namely Global Trade Analysis Project (GTAP) in order to estimate of impacts on reducing rate of trade protection to zero which is an ultimate goal to eliminate all tariff and non-tariff measures. The fact that GTAP model can provide a clear focus on issues of efficiency and of distribution by the analysis of simultaneous changes in various policy-instruments. Therefore GTAP model accounts for a favored tool in analysis trade policy issues. Based on the mentioned purpose, we can briefly conclude the results under GTAP model that there would certainly be trade gains for both ASEAN and China in a FTA. Trade creation will easily offset trade diversion overall with some ASEAN imports diverted from current trade partners to China. However, with China's strong growth, imports from ASEAN could be absorbed to expand exports without causing significant trade diversion. Thus China could look to ASEAN as an additional source of inputs for natural resource based and intermediate products.

During the past decade, the acceleration of the process of globalization and the rise of regional trading agreements and China's WTO accession are a global economic force and the growing interdependence between ASEAN and China. The framework agreement on comprehensive economic cooperation signed on November 2002 will carry on ASEAN-China relations forward and establish the growth and stability in East Asia. Based on the above assessments, we can reach the following conclusions in importance of ASEAN-China FTA. First, ASEAN-China FTA will enhance intra-regional trade, increase economic efficiency and lower cost that is a momentum of the fast growing economic linkage between ASEAN and China and the challenges posed by globalization.

Second, Both ASEAN and China will raise foreign direct investment (FDI) in each other due to a closer economic relationship established. Moreover ASEAN-China economic cooperation will significantly create a credibility of foreign investment eroded in financial crisis in 1997.

Third, China can create stability in regional currency by holding China's currency which lessen the risk of a financial crisis in the region sparked off by currency fluctuations.

Finally, the relationship between ASEAN and China will lead to create a sense of common community and to support an economic growth and stability. Besides both ASEAN and china will collaborate in international trade affairs on issues of common interest.

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

FTA: Bibliography

2008-05-30T11:01:00.001+02:00

Adams, F.G. and Innwon P. (1995). Measuring the impact of AFTA: an application of a linked CGE system. Journal of Policy Modeling, 17(1), 325-365.
Adams, P.D. (2003). Interpretation of Macroeconomic Results from a CGE model such as GTAP. Central of Policy Studies, Monash University.
Anderson, K. and Richard B. (1993). Regional Integration and the Global Trading System. Harvester Wheatsheaf, Great Britain.
ASEAN Secretariat. (2001). Forging Closer ASEAN-China Economic Relations in the Twenty-first Century. A report submitted by ASEAN-China Expert Group on Economic Cooperation, October 2001.
ASEAN Secretariat. (2003). Framework Agreement on comprehensive economic cooperation between the Associated of South East Asian Nations and The People's Republic of China. Phnom Penh Meeting, 5 November 2002.
Chirathivat, S. (2002). ASEAN-China Free Trade Area: background, implication and future development. Journal of Asian Economics, 13(2002), 671-686.
Economic Research Forum (1996). The race for competitiveness and high growth. Economic Research Forum For The Arab Countries, Iran & Turkey FORUM, Vol. 3, No.2, Jun 1996.
Hertel, T.W. (1997). Global Trade Analysis Modeling and Applications. Cambridge University Press, the United States of America.
Kreps, D.M. (1990). A course in microeconomic theory. Harvester Wheatsheaf, Great Britain.
Laurencesor, J. (2003). Economic Integration between ASEAN and China. Discussion Paper No.329, School of Economics, The University of Queensland, Australia.
Panagariya, A. (1999). Preferential Trade Liberalization: The traditional theory and New Developments. Department of Economics, University of Maryland.
Pomfret, R. (1998). The Economics of Regional Trading Arrangements. Clarendon Press, Oxford, New York.
Reed, G and Adams B. (2004). Applied General Equilibrium Analysis. AGE lecture Notes 2003-2004, School of Economics, University of Notthingham.
Reed, G. (1996). The Use of CGE Modelling in the Analysis of Trade Policy Reform. Centre for Research in Economic Development and International Trade (CREDIT), University of Nottingham, UK, Paper presented at the Cairo University Conference on Implications of the Uruguay Round on the Arab countries, January 1996.
Robson, R. (1980). The Economics of International Integration 4^th ed. Routledge, London and New York.
Shantong, L. and Zhai F. (2002). China’s WTO accession and implications for its regional economics. Economic internationale, 92(2002), 67-102.
Silva, K.S. and Mark H. (1996). Economies of scale and imperfect competition in applied general equilibrium model of the Australian economy. Preliminary working paper no.OP-84 March 1996, Centre of Policy Studies and the Impact Project, Monash University.
Venables, A.J. (2000). International trade: Regional Economic Integration. Paper prepared for the “International encyclopedia of social and behavioral sciences.
Venables, A.J. (2001). Regionalism and Economic Development. Paper prepared for the Inter-American development Bank conference and volume “Trade and Regional Integration in the development agenda”, ed. A. Estevadeodal and R. Devlin.
Wang, Z. (2002). The impact of China’s WTO accession patterns of world trade. Journal of Policy Modeling, 25 (2003), 1-41.

Outline:
Introduction Theoretical Framework Theoretical Structure and Calibration
GTAP Simulation Empirical Results Conclusion Critical Analysis Bibliography

Logos often seen in Nantes

2008-05-29T19:56:00.000+02:00

The logos are often seen when you visit to Nantes, France.

La poste is the post office in France which was officially granted the postal service in France in 1999. It is active in three primary areas: Mail service (Le Courrier), Parcels and Express (Le Colis-Express) and Banking (La Banque Postale).

EDF (L'électricité de France) is the electricity generation and distribution company in France, established on 1946. Until now, EDF is one of the world's largest producers of electricity.

France Telecom is the main telecommunication company in France, established on 1988. It is one of the largest in the world.

Neuf Telecom is a French wireline telecom services provider and a mobile virtual network operator, legally established in 2005.

SNCF (La Société nationale des chemins de fer français) is a French National Railway Company. Its operations include almost all of France's railway system, including the TGV (Train à Grande Vitesse, "High-speed train") and some segments of the RER (Réseau Express Régional, "Regional Express Network".

TGV (Train à Grande Vitesse) is high-speed rail service of France, developed by GEC-Alsthom and SNCF and operated primarily by SNCF. It is recorded as the fastest wheeled train, having reached 574.8 km/h (357 mph) on April 3, 2007 and it is regarded as the world's highest average speed for a regular passenger service.

TAN (Les Transports de l'agglomération nantaise) is the tramway network in Nantes, France. Its system was seriously opened in 1985, and is now the largest tramway network in France. To date, it consists of Tramway lines, Busway line, Bus lines, and Navibus lines.

TER (Le Transport express régional) is medium-distance passenger rail services within each region of France.

Busway is regarded as a bus rapid transit line first served on November 6, 2006 in the city of Nantes, France.

CAF (Les caisses d'allocations familiales), namely National Office for Family Allocations, is one of the family-oriented sector of the French social security system.

Préfecture is an administration that belongs to the Ministry of the Interior. It is in charge of the delivery of identity cards, driving licenses, passports, residency and work permits for foreigners and vehicle registration etc.

LCL (Le Crédit lyonnais) is the French Bank founded in 2005 owned by Crédit Agricole.

The following lists are the name of department stores in Nantes, France.

Monoprix is an inexpensive department store that has many locations throughout France.

Carrefour is worldwide hypermarket in many countries which carries a range of goods from multi-media, clothing, electrical goods and household goods through to foods.

Galaries Lafayette is the largest department store in France and in Europe. There are over 65 locations throughout France.

Champion is supermarket which has a variety of smaller subsidiary mini-markets such as the Marché Plus chain.

Grand Jury is the own brand in the Carrefour group, launched in 1995. It includes nearly 1200 food items and non-food items.

The following lists are the name of TV channels in Nantes, France.

TF1 is a private French TV channel, controlled by TF1 Group, whose major share-holder is Bouygues. For more details see http://www.tf1.fr/.

M6 or Métropole 6 is a French television service owned by a company called Métropole Télévision. For more details see http://www.m6.fr/.

source: see more details in www.wikipedia.org.

Fête de la musique

2008-05-29T16:11:00.000+02:00

The Music Festival in Nantes 2008

The popular and festive occasion, the music festival, celebrates its 27th anniversary by bringing together each year as musicians and music lovers. This new edition is under the theme of film music!

Since 1982, the music festival day has become an annual meeting date for the philosophy of sharing, diversity and creativity. This event has exceeded far today where there are over one hundred and ten countries and two hundred fifty cities on five continents that celebrate the music.

Source: http://fetedelamusique.culture.fr/

9 temples buddha trip

2008-05-29T15:04:00.000+02:00

Worshipping at the Buddha's image in nine temples around Rattanakosin Island in one day

Rattanakosin Island is a centre of culture and worship in Thailand. It offers a marvelous sightseeing for travellers and buddhists, who are drawn to the region for its magnificent temples and historic traditions. Here is a list of 9 sacred places to visit:

City Pillar Shrine:

The City Pillar Shirne, often called Chao Pho Lak Meuang, is opposite to Wat Phra Kaew or Grand Palace built on 1782 by King Rama I who established Bangkok as the capital of Thailand. The spirit of the City Pillar is regarded as Bangkok's guardian deity. Many Thais believe that if they worship at the shrine, their misfortunes will be eliminated.

http://th.wikipedia.org/wiki/ศาลหลักเมืองกรุงเทพมหานคร

Wat Phra Sri Rattana Satsadaram:

The Wat Phra Sri Rattana Satsadaram, known to the Thais as Wat Phra Kaew or Temple of the Emerald Buddha, is located on Rattanakosin island, the heart of Bangkok city, close to the Chao Phraya River.

http://www.dhammathai.org/watthai/bangkok/watprasiratana.php

http://www.art-and-archaeology.com/seasia/bangkok/rp07.html

The construction of the temple of the Emerald Buddha started when King Rama I moved the capital from Thonburi to Bangkok in 1785. This temple was constructed in the Royal Palace in order to install the Emerald Buddha obtained from the city of Vientiane in Laos. The Emerald Buddha is the most sacred statue of the Buddha in Thailand changed the garments three times a year, at the beginning of summer, rainy season, and winter by the Thai king. Many Thais believe that if they worship the Emerald Buddha, they will possess the wealthy.

Wat Phra Chetuphon:

The Wat Phra Chetuphon or Wat Pho, known as the Temple of the Reclining Buddha, is a Buddhist temple located in the south of the Grand Palace in Bangkok. The official full name is Wat Phra Chetuphon Vimolmangklararm Rajwaramahaviharn. This temple is initiated the traditional Thai massage. Many Thais believe that if they salute to this temple, it brings them more happiness.

http://www.flickr.com/photos/clneira/336390029/

http://www.mir.com.my/leofoo/Thai-amulets/Buddhist_Landmarks/Bangkok/index.htm

Tiger God Shrine:

The San Chao Pho Suea or Tiger God Shrine is the old Taoist shrine constructed in Southern Chinese architectural style and located on Tanao Road near the old town. It is one of the popular places of worship in Bangkok. The tiger statue is believed to contain tiger bones and its spirit helps to protect people from the forces of evil. The deity is highly respected by the local people and is believed to be able to have more power.

http://www.nairobroo.com/76/modules.php?name=News&file=article&sid=356

Wat Suthat:

The Wat Suthat Thepwararam is a royal temple of the first grade constructed in 1807 after the 27th anniversary of the founding of Bangkok by King Rama I. This temple contains the Buddha image Phra Sri Sakyamuni or "Sisakayamunee" which moved from Sukhothai province. Many Thais believe that if they salute to this temple, it makes them to have good visions.

http://www.dhammajak.net/gallery/displayimage.php?album=3&pos=69

Wat Chana Songkhram:

The Wat Chana Songkhram is the meaning of the “Victory in the battle”. Many Thais believe that if they salute to this temple, they are able to get rid of the obstacles to their success.

http://www.dhammathai.org/watthai/wat9wat.php

Wat Rakhang:

The Wat Rakhangkhositraram Woramahavihara or Wat Rakhang is opposite to Tha Chang Wang Luang Pier. It originally called Wat Bang Wa Yai was an ancient temple which was built during the Ayutthaya Period. Importantly, King Taksin raised its status to that of a royal temple. Many Thais believe that if they salute to this temple, they are beloved of the others.

http://www.dhammathai.org/watthai/bangkok/watrakhang.php

Wat Arunrajawararam:

The Wat Arunrajawararam Rajaworamahavihara (Wat Chaeng) is opposite to the Wat Phra Chetuphon or Wat Pho. Historically, King Taksin arrived at this temple while the dawn was breaking. Later on, he renovated and renamed it Wat Chaeng or the Temple of the Dawn. Many Thais believe that if they salute to this temple, their lives will be prosperous.

http://www.dhammathai.org/watthai/bangkok/watarun.php

Wat Kalayanamit:

The Wat Kalayanamit is a very beautiful and very large temple which was constructed in 1825 belonging to King Rama III. Many Thais believe that if they salute to this temple, they save from every journey.

http://www.dhammathai.org/watthai/bangkok/watkalayanamit.php

Welcome

2008-05-28T21:11:00.000+02:00

I would like to welcome you to my own website for study, research, and visit. The contribution to this website is to present the past, ongoing and future studies in the field of my interest. I will devote myself to develop future studies in terms of development and international economics in principle.

I am very happy that you are visiting here. I hope that this website might be able to encourage you all to have more foresight in economic studies including the methodological improvement.

Warmly welcome to pursue my contribution to economic studies. Thank you very much.

Enjoy the website.

natha.memo