bilateral trade in an economic group.pdf

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Nikbakht Z., Nikbakht L. - The analysis of bilateral trade: The case of D8

Zahra Nikbakht, Leili Nikbakht

THE ANALYSIS OF BILATERAL TRADE: THE CASE

OF D8

Abstract

The main pillar of economic globalization is trade liberalization that resulted to competitive national

economies and then going to merging in global market. The communities that adapt their national

economies to new competitive conditions and have an important role in this game have earned a lot of

benefits from globalization phenomenon and economic competitiveness. One of the most important and

fundamental concern of developing countries is the effect and consequence of economic globalization.

In this paper, to investigate the bilateral trade among D8 Islamic countries group, the generalized gravitymodel is used. The results on gross domestic products (GDP) of host and guest countries and geographical

distances are consistent to gravity theory. Furthermore, the variable of differences in economic structures

and also, the variable of economic openness have a positive relationship to bilateral transactions flows.



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180 Business Intelligence Journal Janu

1. Introduction

Considering the current trend of

economy that are going to integration and

globalization, regional cooperation can play

an important role in the expanded presenceof countries in international scenes. For

developing countries that are not ready to

sudden arrival into free trade, the regional

convergence can be the most effective way

to gradual opening of national economies

and merge them into global economy.

The trade gravity model is a powerful

tool for explaining the bilateral trade ow

and volume, which is widely applied to

analyzing the inter-national bilateral tradevolumes since the 1960s and estimating

trade potentials, identifying the effects of

trade group, explaining the trade patterns

and assessing the cost of a border trade (Lin

and Wang, 2004; Liu and Jiang, 2002; Sheng

and Liao, 2004). So it better explained

some economic phenomena observed

in the reality. From the 1960s to 1970s,

many studies concentrated on the extended

model to optimize the trade gravity model

gradually.Regarding the extended trade gravity

model, the economist mainly introduced

the new explanatory variables to modify the

basic gravity model. These new variables

are divided into two types: one is exogenous

variable that affects the trade volume, like

population, per capita GDP and so on, the

other is the dummy variable such as the

preferential trade agreement, integration

organization and so on (Shi et al., 2005).

Aitken (1973) added new dummy variables

to estimate the impact of European Economic

Community on the trade of its member states.

Frankel and Wei (1993) found that the level

of economic development, i.e. per capita

GNP, in a country is an additional signicant

factor to determine bilateral trade.

Researchers used the resultant extended

gravity model to explore the effects of

regional groups on the trade performance.

In the late 1990s, Frankel and David (1999)

took both domestic and bilateral trade

volumes as the research objects. Althoughthe gravity model answers the question of

the ow direction of trade successfully in a

quantitative way, the prediction of potential

bilateral trade volumes is restricted because

of a lack of reasonable economic theoretical

foundation (Liu and Jiang, 2002). In recent

years, the theoretical foundation of gravity

model has been further consolidated

by Anderson, Helpman, Krugman,

Bergstrandand and Deardorff (Sheng andLiao, 2004).

The D8 group consist of eight developing

Muslim countries includes Iran, Turkey,

Pakistan, Bangladesh, Malaysia, Egypt

and Nigeria. The general goals of D8 are

reinforcement and promoting the position of

developing countries in the world economy,

diversication and creation of new

opportunities in trade terms, increasing the

decision making role of D8 in international

levels and providing better life standards(general welfare) for people in member

countries.

This study investigates whether there is a

bilateral trade among D8 members. Because

of differences in the economic structure of

members and also because the locations of

D8 members are geographically dispersed,

fewer studies investigate the economic

convergence of this group.

There are several studies on bilateraltrade. In the convergence and bilateral trade

eld, various models are used. Because of

the exibility of gravity model, in this paper

we applied it to survey the bilateral trade in

D8.


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2. Model specification

Although aspersions have been cast

on the respectability of the gravity model

of trade in the past, by now its theoretical

pedigree has been proven. Earlier worksurveyed by Deardorff (1984, 503-06),

provided a partial foundation for the

approach. More formal approaches relied on

product differentiation. The assumption that

products are differentiated symmetrically

by country of origin has become associated

with Armington (1969). Anderson (1979)

adopted a linear expenditure system in

which the preferences for a countrys goods

are assumed to be homothetic and uniformacross importing countries. Bergstrand

(1985) assumed a more exible utility

function that allowed him to nd evidence

that imports were closer substitutes for

each than for domestic goods. He called

his equation a generalized gravity model

because it also included price terms.

The best-known theoretical rationale

for the idea that bilateral trade depends on

the product of GDPs comes from work by

Helpman (1987) and Helpman and Krugman(1985, section 1.5).

More recently, Deardorff (1998) has

changed his mind, having discovered how

to derive the gravity model from Heckscher-

ohlin theory almost as easily as from the

theory of imperfect substitutes. His main

purpose is to show that the empirical success

of the gravity equation does not necessarily

support the imperfect-substitutes model

relative to the Heckscher-ohlin model. For

our purposes, the main point is that it seems

possible to derive the gravity model from a

variety of leading theories. The equation has

thus apparently gone from an embarrassing

poverty of theoretical foundations to an

embarrassment of riches!

To most readers who have not studied

enough trade theory to have lost sight of

the obvious, the assumption that trade

between countries depends positively on

their size and inversely on distance may

seem self-evident. Those trade theorists who

previously questioned the foundations of the

gravity model did not have an alternativemodel of bilateral trade to offer. It was

just that economics has not tried very hard

to model bilateral trade. Deardorff (1998)

concludes:

While the derivation of a proportionate

relationship between trade ows and

country size is an important foundation, the

theories of Helpman (1987) and most of the

other authors cited do not include a role for

distance and thus cannot properly be calledfoundations of the full gravity model. The

few exceptions include Bergstrands (1985)

version of the imperfect-substitutes theory,

which incorporated a role for shipping costs,

proxied in practice by distance. Distance

is also included in the second of the two

Heekscher-Ohlin-based models developed

by Deardorff (1998). The proportionality

between bilateral trade and the product of

incomes, as well as the inverse dependence

on distance, are also properties of ourtheoretical model, introduced in chapter 7.

We assume that transportation costs raise

the price of a good in the importing country

and that distance has a positive effect on

transportation costs.

Once, one has incorporated rote for

distance in raising the cost of trade, it is

a small step to think of similar roles for

dummy variables indicating whether the

pair of countries shares a common borderor common language. Each of these links

helps reduce the cost of doing business

abroad, just as proximity does. Near the

border, consumers can cross over to shop

in the other country and rms can source

intermediate inputs in the other country,

much more readily than would be possible

if the countries did not share a common




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trade balance between countries i and j and

are calculated as:

TPijt-1

=|Xijt-1

-Mijt-1

|/(Xijt-1

-Mijt-1

)

where Xijt-1 represents the export ofcountry i to country j in year t-1 and

Mijt-1

represents the imports of country i

from country j. This measure takes the

values between 0 and 1, TPijt-1

=0 when the

trade is quite unilateral and TPijt-1

=1 when

the trade is quite bilateral. It is expected

that this variable has a negative effect on

bilateral trade.

After estimation the model (1) in thesecond stage the effect of variables which

are constant over time, are investigated.

Thus, the estimated intercepts from model

(2) are regressed on geographical distances

among economic centers (i.e. countries):

(3)

FEijt=

o+

2Log(Dis

ij)+

ijt

Where FEijt is individual xed effects

from model (2) and Disij is geographicaldistances between capital of country i from

capital of country j. on the other word,Disij

indicates the transportation costs between

countries i and j, indirectly.

3. Research results

3.1. Data source

We use data on the eight countries

of D8 members, Iran, Turkey, Pakistan,

Bangladesh, Malaysia, Egypt and Nigeria,

were obtained from International Financial

Statistics (IFS), and published by the

International Monetary Fund (IMF) from

1985 to 2007.

3.2. Panel unit root tests

We implement ve different types of

panel unit root tests: the Im et al. (2003)

test (IPS), and the Fisher-type ADF and

PhillipsPerron (PP) tests. Results are basedon the inclusion of an intercept and trend.

The results of panel unit root tests in level

and one order of differentiation are reported

in Table 1.

Table 1: Panel unit root test results

Variable Xijt

GDPit

POPit

Series in level:

Im, Pesaran,and Shin

-6.32 3.45 19.64

(0.00)* (0.99) (1.00)

FisherADF385.25 96.47 173.30

(0.00)* (0.85) (0.00)*

FisherPP536.19 89.87 634.37

(0.00)* (0.94) (0.00)*

Series in first differences:

Im, Pesaran,

and Shin

-24.99 -12.20 -14.64

(0.00)* (0.00)* (0.00)*

FisherADF670.75 395.09 366.15

(0.00)* (0.00)* (0.00)*

FisherPP1443.06 503.95 319.80

(0.00)* (0.00)* (0.00)*

* Significant at the 1% level

The results of all panel unit root tests

indicate that theXijtis stationary in level and

rst differences. The results also show that

the GDPitandPOP

it both are stationary in

the rst difference, thus they are I(1).

3.3. Panel cointegration test

For the panel cointegration test, we use

the test method presented by Kao (1999).

The results of panel cointegration test are

provided in Table 2.




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Table 2: The results of panel cointegration test

Statistics Critical

quantityADF

Critical

quantity

Value -4.435 -2.890 -6.386 -3.413

DF

The results of panel cointegration test

indicate that both test statistics are lower

than the critical quantities expressing that

research variables are cointegrated, thus we

can estimate the models (2) and (3).

3.4. Model estimation

First, we run the restricted F-test and

Hausman test to determine the best structureto estimate the model (1). The results of

selection model tests and estimated model

(1) are presented in Table (3). The results of

restricted F-test (F=365.66, P-value=0.00)

indicate that the xed effects model is

better than the pooled estimation for model

(1). The results of Hausman test (H=23.10,

p-value=0.00) also show that the xed

effects model is better than the random

effects model. Therefore, to estimate the

model (1), we apply the xed effects modelapproach.

Variable Coefficient T- Statistics P-value

Intercept -10.64 -4.35 0.00

Log(GDPITit) 0.61 3.55 0.00

Log(GDPjt) 0.62 6.97 0.00

Log(POPit) -7.25 -5.58 0.00

Log(POPjt) 7.76 5.37 0.00

SIMIijt -0.71 -6.31 0.00

Log(Openit) 1.02 6.21 0.00

Log(TPijt-1

) -0.00 -0.05 0.96

Restricted F-test F=365.66 0.00

Hausman test H=23.10 0.00

R2=77.34% Adj.R2=74.23% F-statistics=668.93 P-value=0.00

The estimated results of model (1)

indicates that all variables (except for

Log(TPitj-1

)) are signicant at the 1% level.

Consistent to theory, the GDP of home (0.61)

and host (0.62) countries have a positive

relationship to bilateral trade. The populationof home (-7.25) and host (7.76) countries

have a negative and positive relationship

to bilateral trade, respectively. Similarity

in economic structure has a negative effect

(-0.71) on bilateral trade and economic

openness degree of importer countries has

a positive effect (1.02) on bilateral trade.

Finally, the extant of dynamic ow of goods

and services between countries has no

relationship to bilateral trade.Results also indicate that about 74% of

changes in bilateral trade are explained by

independent variable. As the F statistic of

estimated model (668.93) is signicant at

the 1% level, the model (2) is signicant,

too.

The estimation results of model (3)

are reported in table 4. In this model, the

dependent variable is individual xed effects

resulted from model (2) and independent

variable is geographical distances amongD8 countries.

Variable Coefficient T- Statistics P-value

Intercept 43.70 2.09 0.04

Log(Dij) -5.19 -2.10 0.04

Restricted F-test F=365.66 0.00

Hausman test H=23.10 0.00

R2=7.56% Adj.R2=5.85% F-statistics=4.42 P-value=0.04

Table 3. The estimation results of gravity model (1) Table 4. The secondary estimation of gravity model

The estimation results of model (3) report

that geographical distance has a negative

and signicant (-5.19, t=-2.10) relationship

to individual xed effects and thus has a

negative and signicant relationship to

bilateral trade. Results also show that about

6% of changes in individual xed effects


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2011 185

are explained by distance variable. As the

F statistic of estimated model (4.42) is

signicant at the 5% level, the model (3) is

signicant, too.

4. Conclusion

In this paper, we studied the bilateral

trade in D8. Similar to previous papers in

this area of research, we apply a generalized

version of gravity model to analysis the

bilateral trade in D8. In this model, we

enter the similarity in economic structure,

the economic openness degree of importer

countries and the trade policy into the basic

model and use it to survey the bilateral tradein D8.

The results indicate that all variables

(except for the policy trade) in used model

have expected sign and are signicant. In

summary, results indicate that the GDP of

home and host countries has a positive; the

population of home (host) country has a

negative (positive); similarity in economic

structure has a negative and the economic

openness degree of importer countries has

a positive effect on bilateral trade. Also,the results indicate that the geographical

distances among capital of D8 members has

a negative relationship to bilateral trade.

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