This research paper offers a selective survey of gravity equation in the International Trade. Gravity equation was first introduced in the Sixties as a purely empirical proposition to explain bilateral trade flow. The data was taken from the SAARC countries to evaluate the factors affecting the bilateral trade volume. The variables that impact on trade volume are studied in this thesis, as in : Transport Cost, Inflation, Exchange Rate, GDP, population, Tariff and distance by using multiple linear regressions. The results of transport cost, inflation, exchange rate, GDP and distance has the significant impact on the bilateral trade, whereas tariff and population have insignificant values.Bilateral Trade; SAARC countries

Subhani, Muhammad Imtiaz

English

Research Papers in Economics

Published in  
South Asian Journal of Management Sciences  
Vol. 3, No. 2, (Fall 2009) pp 15 - 21 
 
 
 
 
Bilateral Trade  
A Study on SAARC Countries 
 
 
M. I. Subhani and Rajnesh Kumar 
 
 
ABSTRACT 
 
This  research  paper  offers  a  selective  survey  of  gravity  equation  in  the 
International Trade. Gravity equation was first introduced in the Sixties as a 
purely  empirical proposition  to explain bilateral trade  flow. The data  was 
taken from the SAARC countries to evaluate the factors affecting the bilateral 
trade volume. The variables that impact on trade volume are studied in this 
thesis, as in : Transport Cost, Inflation, Exchange Rate, GDP, population, 
Tariff  and  distance  by  using  multiple  linear  regressions.  The  results  of 
transport cost, inflation, exchange rate, GDP and distance has the significant 
impact on the bilateral trade, whereas tariff and population have insignificant 
values. 
 
 
Introduction 
 
Gravity equation (GE) first introduced in 1960’s as an experimental  study to explain bilateral 
trade flows. GE was introduced through a series of theoretical work which showed that the 
basic  gravity equation form was consistent with different models of trade flow. Practical 
applications of gravity equation prolonged to cover a range of issues, such as the impact of 
regional trade agreements, currency unions, national borders on trade, and use of the gravity 
equation to sort out the relative merit of different trade theories (Fratianni, 2007). 
Sanso,  Cuairan,  and  Sanz  (1993)  the  gravity  equation  has  been  successfully  and 
frequently used for years to further understand  the determinants of bilateral trade flow across 
countries, and subsequently to analyze commercial policy measures. Its basic formulation 
was a log linear function upon a well defined set of variables. The explanatory variables are 
the incomes and populations of both countries and the distance between the borders. 
Gravity model of trade in international economics predicts the bilateral trade flows 
based on some variables. The gravity model has been used extensively in empirical studies of 
international  economics  since  the  1960s.  According  to  the  model,  bilateral  trade  is 
determined by the wealth and size of countries, the distance between the countries, and other 
factors that distort trade. The theoretical foundations of the gravity model are based on the 
theory of trade under imperfect competition and has  recently been integrated with the factor 
proportions and demand based theories of International Trade (Tamirisa, 1999). 
The gravity equation has been long recognized for its consistent empirical success in 
explaining  many  different  type  of  flows,  such  as  Migration,  Commuting,  Tourism,  and Commodity Shipping. Typically, the log linear equation specifies that a flow from origin I to 
destination J can be explained by the economic forces at the trade flow origin, economic 
forces at the flow's destination, and the economic forces either aiding or resisting the flow's 
movement from origin to the destination (Bergstrand, 1985). 
SAARC countries are low and middle income developing countries whose economies 
share many similarities related to their geography as well as the common aspects of their 
Culture, History and Economic and Social development. Given their high population level by 
comparison with not only the major industrial countries but also most countries in Africa, 
Latin America and West Asia, the SAARC countries have a comparative advantage in the 
production and international trade of many labor-intensive manufacturers. Yet, trade relations 
of  the  SAARC  countries  are  also  shaped  by  political  factors;  in  many  instances  using 
quantitative  restrictions  and  barriers  to  imports,  these  countries  hinder  trade  to  protect 
favored domestic industries. 
The objective was to explain trade flows in terms of the gravity equation, the reason 
for  focusing  on  gravity  was    the  GE,  unlike  other  frameworks,  has  had  great  empirical 
success  in  explaining  bilateral  trade  flows  and  very  useful  in  international  trade.  In  its 
simplest  form,  the  gravity  equation  explains  flows  of  a  of  good  bond between  pairs  of 
Countries. In this study, GDP, Transport cost, population, tariff, exchange rate, inflation and 
distance between the countries are used as the independent variables and trade volume i.e. 
imports and exports volume as dependent variables, by using multiple regression technique. 
 
Literature Review 
Exchange Rate 
 
Guedae,  Sheldon,  and  McCorriston  (2002)  analyzed  that  there  is  a  negative  effect  of 
exchange  rate  uncertainty  on  the  agricultural  trade,  further  more  the  negative  effect  of 
exchange rate volatility has been more significant compared to other sectors and the most 
common assertion has been that the risk associated with this exchange rate volatility has 
reduced the level of exports. Rose (2000) used bilateral trade for a panel of 186 countries, 
over  the  period  1970-90,  finding  a  small,  but  statistically  significant,  negative  effect  of 
exchange  rate  volatility  on  trade  and  suggested  that  the  agricultural  trade  is  especially 
affected  by  medium- to  long-run  exchange rate uncertainty.  The  evidence  reported  here, 
suggests that agricultural trade was more susceptible to exchange rate uncertainty than the 
aggregate trade data would suggest and that the negative effects on the growth of trade have a 
stronger effect on trade in agricultural goods compared with other sectors. 
Higher exchange rate unpredictability leads to high costs for risk aver sing traders and 
to less foreign trade, if variations in exchange rate becomes unpredictable, which also creates 
uncertainty about the earnings to be made and hence reduces the benefits of international 
trade. Results concerning the impact of exchange rate uncertainty on export flows suggest 
that there is a negative and statistically significant long-run relationship between export flows 
and exchange rate variability. In most of the countries, exchange rate volatility has a short run 
effect on exports flow and has a substantive causal relationship in which changes in exchange 
rate volatility causes changes in real exports. 
Stockman (1985) examined how steady state inflation affects the direction and size of 
international trade. The effects of inflation work through changes in supplies of labor and 
capital. For certain rates of inflation, the minor changes can have remarkable effects on the 
direction of trade; for other rates of inflation, the volume of trade is affected. When money is 
held for transactions purposes and factor supplies are endogenous, changes in inflation can 
cause changes in the pattern of trade. Depending upon the income of elasticity of demand for 
the two traded goods, and upon the initial pattern of trade the volume of trade may rise or fall 
and the pattern of trade may be reversed by a change in the rate of inflation.  
Research Methods 
Method of Data Collection 
 
Secondary  data  is comprised on  21 observations  for  year  2006-2008, collected  from  the 
different  sources  i.e.  official  website  of  world  trade  organization  (WTO),  International 
monetary fund (IMF), CIA factbook files and official websites of the SAARC countries. The 
data is comprised on independent variables: 
Gross  Domestic  Product  (GDP),  Tariff,  Distance,  Population,  Transport  cost, 
Exchange  rate  and  Inflation.  The  dependent  variables  are  the  import  volume  and  export 
volume. 
 
Hypothesis Development 
 
H1: There is a positive relationship between Transportation cost and export volume. 
 
H2: There is a negative relationship between Exchange rate and Export volume. 
 
H3: There is a negative relationship between Distance and Export volume. 
 
H4: There is a positive relationship between GDP and import volume. 
 
H5: There is a positive relationship between Inflation and import volume. 
 
Research Model Developed 
 
Following model was determined the impact of different variables on the trade volume and to 
test the hypothesis that the variables that impact on trade volume were studied in this research 
paper, like: transport cost, inflation, exchange rate, GDP, population, Tariff and distance by 
using multiple linear regression. 
Import Volume and Export Volume = α + β (Gdp) + β (Tariff) + β (Distance) + β 
(Population) + (Transport Cost) + (Exchange Rate) + (Inflation) + ū 
Where Secondary data was taken comprised on 21 observations for year 2006-2008, 
collected from the different sources i.e. official website of world trade organization (WTO), 
International monetary fund (IMF), CIA Factbook files and official websites of the SAARC 
countries. The data is comprised on independent variables: 
Gross Domestic Product (GDP) and the coefficients α and β are regression parameters 
for the independent variable and ū denotes the error term. Same model was used by Guedae, 
Sheldon, and McCorriston (2002); Broll and Eckwert (1999); Feenstra, Markusen, and Rose, 
2001) for examining the impact of different variable on International trade.  
 
Statistical Technique 
 
After  collecting  the  data  from  the  selected  population,  it  was  analyzed  by  using  SPSS 
software  to  measure  the  impact  of  change  in  independent  variables  on  the  dependent 
variables. 
Statistical technique “Multiple Linear Regression” was used to identify the variables 
that impact on bilateral trade volume i.e. Import and export volume. 
 
 
 
 
 Results 
Findings and Interpretation of the Result 
 
Applying  the  multiple  regression  analysis  through  SPPS  software  by  using  the  stepwise 
method, which is highly recommended for this type of analysis. Following results appeared: 
 
Table 1: Model Summary for Export 
 
Mode
l  R  R Square  Adjusted R 
Square 
Std. Error of the 
Estimate 
1  .812(a)  .659  .637  1.98734 
2  .903(b)  .816  .791  1.50908 
3  .971(c)  .943  .931  .86844 
   Dependent Variable: Exports 
 
  Above table shows, percentage change in the dependent variable due to change in 
independent variables. It shows that 65.9 % change in dependent variable (Export volume) is 
due to independent variables which are Transportation cost, Exchange rate and Distance. 
Transportation cost has negative impact on export volume because the significant value was 
less than .05, it means the Hypothesis is rejected and result is significant. 
  Second percentage changes in dependent variable due to taken independent variables 
shows that 81.6%.  Exchange rate has negative impact on exports because the significant 
value was less than .05, it means the Hypothesis is rejected and result is significant. 
  Third percentage change in dependent variable due to taken independent variables it 
shows that 94.3%.  Distance has negative impact on exports because the significant value is 
less than .05, its means the Hypothesis is rejected and the result is significant. 
 
Table 2: Model Summary for Import 
 
Model  R  R Square  Adjusted R 
Square 
Std. Error of 
the Estimate 
1  .902(a)  .814  .803  1.61008 
2  .929(b)  .863  .845  1.42633 
Dependent Variable: Imports 
 
  Above table shows percentage change in the dependent variable due to change in 
independent variables. It shows that 81 % change in dependent variable (Import volume) is 
due to independent variables which are GDP and Inflation. 
  GDP has positive impact on import volume because significant value is less than 
.05, it means the Hypothesis is rejected and the result is significant. 
  Second percentage changes in dependent variable due to taken independent variables 
shows that 86.3%.  Inflation has negative impact on imports because the significant value is 
less than .05, it means the Hypothesis is rejected and the result is significant. 
 
Table 3: ANOVA for Export 
 
Model  Sum of  
Squares  D.F  Mean Square  F  Sig. 
1.    Regression 
       Residua 
       Total 
121.970 
63.192 
185.162 
1 
16 
17 
121.970 
3.950 
30.882  .000
a 2.   Regression 
      Residua 
      Total 
151.002 
34.160 
185.162 
2 
15 
17 
75.501 
2.277 
33.154  0.00
b 
3.   Regression 
      Residua 
      Total 
174.603 
10.559 
185.162 
3 
14 
17 
58.201 
0.754 
77.17C  0.00
c 
   Dependent variable: Exports 
 
The table shows that there is no relationship among the Export volume, Inflation, 
Tariff, population and GDP because its significant value is less than .05. 
   
Table 4:  ANOVA for Import 
 
Model  Sum of  
Squares  D.F  Mean Square  F  Sig. 
1.    Regression 
       Residua 
       Total 
181.762 
41.478 
223.239 
1 
16 
17 
181.762 
2.592 
70.114  .000
a 
2.   Regression 
      Residua 
      Total 
192.723 
30.516 
223.239 
2 
15 
17 
96.362 
2.034 
47.366  0.00
b 
a  Predictors: (Constant), GDP 
b  Predictors: (Constant), GDP, Inflation 
c  Dependent Variable: Imports 
 
The table shows that there is no relationship among the Import volume, exchange rate, 
tariff, distance and population because its significant value is less than .05 
 
Table 5: Coefficient for Export 
 
Model 
Unstandardized 
Coefficients 
Standardized  
Coefficients  T  Sig. 
Beta  Std. 
Error  Beta 
1. (Constant), TansExp  -2.293 
.924 
2.295 
.166 
.812  -.999 
5.577 
.333 
.000 
2. (Constant), TransExp, 
ExRate 
 
3.255 
1.100 
-2.044 
2.335 
.136 
.572 
.966 
-.425 
1.394 
8.115 
-3.571 
.184 
.000 
.003 
3. (Constant), TransExp, 
ExRate, DISTANCE 
-29.457 
1.384 
-2.316 
4.211 
6.000 
.093 
.333 
.753 
  -4.909 
14.871 
-6.956 
5.594 
.000 
.000 
.000 
.000 
a. Dependent variable: Exports 
 
Equation 1 
 
Exports = -29.457 + 1.384*Transport cost – 2.316*Exchange rate+ 4.211*Distance + εit                 
 
Where Exports is the dependant variable, β is the constant term and the independent 
variables include Transport cost, exchange rate and Distance, εit is the error term. If exports change by 1 unit then transport cost also increase by 1.834, exchange rate decreases by 2.316 
and distance increases by 4.211. 
 
Table 6: Coefficient for Import 
 
Model 
Unstandardized 
Coefficients 
Standardized 
Coefficients  T  Sig. 
B  Std. Error  Beta 
1.Constant  
GDP 
4.195 
1.225 
0.590 
0.146 
0.902 
 
5.010 
8.373 
0.000 
0.000 
2.(Constant) 
GDP 
Inflation 
0.227 
1.185 
2.295 
1.980 
0.131 
0.989 
0.873 
0.22. 
-0.115 
9.071 
2.321 
0.910 
0.000 
0.035 
a.  Dependent Variable: Import 
 
Equation 2 
 
Imports = - .227 + 1.185*GDP + 2.295*Inflation + εit 
Where Imports is the dependant variable, β is the constant term and the independent 
variables include GDP and Inflation, εit is the error term. If Imports change by 1 unit then 
GDP increase by 1.185 and inflation increases by 2.295. 
 
Discussion, Implication, Future Research and Conclusions 
 
This research paper shows how the bilateral trade volume in the SAARC countries varies due 
to different  variables,  the  main objective  of  this research  was  to  identify the barriers  to 
bilateral trade in SAARC countries, the data consists of three years that is from 2006-2008. 
Gross Domestic Product (GDP), Tariff, Distance, Population, Transport cost, Exchange rate 
and Inflation is tested to predict their affect on dependent variable. With the help of gravity 
model regression is applied. The tool used for the analysis and interpretation of data is SPSS. 
SAARC countries are low and middle income developing countries whose economies 
share many similarities related to their geography as well as the common aspects of their 
culture, history and economic and social development. This research may help to improve 
trade facilities and trade relations and enhance trade volumes. 
Though, all variables were considered to be in line with the literature, however, based 
on regression coefficients  shown by many variables along with dependency problem, the 
final model comprised of independent variables that is Transportation cost, GDP, Inflation, 
exchange rate and distance having significant value less than 0.05 which suggests that these 
variables have significant impact on the bilateral trade in SAARC countries. On the other 
hand,  results  reveal  that  Tariff  and  Population  have  significant  value  greater  than  0.05 
therefore it may not necessarily lead to an impact on bilateral trade in SAARC nations. 
 
 
 
 
 
 
 
 
 References 
 
Bergstrand, J. H. (1985). The Gravity Equation in International trade: some Microeconomic 
Foundations and Empirical Evidence. Review of Economics and Statistics, 67(4), 474-
481. 
Broll  &  Eckwert  (1999).  Exchange  Rate  Volatility  and  International  Trade.  Southern 
Economic Journal, 66(1), 178-185. 
Feenstra,  R.,  Markusen  &  Rose,  P.  (2001).  Using  the  Gravity  Equation  to  Differentiate 
among Alternative Theories of Trade. The Canadian Journal of Economics / Revue 
canadienne d'Economique, 34, 2, 430-447. 
Fratianni, M. (2007). The Gravity equation in International Trade. Journal of Economics, 
90(1), 226-237. 
Guedae,  A.,  Sheldon,  &  McCorriston,  H.  (2002).  Exchange  Rate  Uncertainty  and 
Agricultural Trade. American Journal of Agricultural Economics, 84(4), 931-942. 
Rose,  A.  K.  (2000).  One  Money,  One  Market?  The  Effects  of  Common  Currencies  on 
International Trade. Economic Policy, 15(30), 7-46. 
Sanso, M., Cuairan, R. & Sanz, F. (1993). Bilateral Trade Flows, the Gravity Equation, and 
Functional Form. Review of Economics and Statistics, 75(2), 266-275. 
Stockman,  A.  C.  (1985).  Effects  of  Inflation  on  the  Pattern  of  International  Trade.  The 
Canadian Journal of Economics / Revue canadienne d'Economique, 18(3), 587-601. 
Tamirisa, R. (1999). Exchange and Capital Controls as Barriers to Trade. IMF Staff Papers, 
46(1), 69-88. 
 

Bilateral Trade Flows, the Gravity Equation, and Functional Form.

Effects of Inflation on the Pattern of International Trade.

Exchange and Capital Controls as Barriers to Trade.

Exchange Rate Uncertainty and Agricultural Trade.

Exchange Rate Volatility and International Trade.

One Money, One Market? The Effects of Common

The Gravity Equation in International trade: some Microeconomic Foundations and Empirical Evidence.

The Gravity equation in International Trade.

Using the Gravity Equation to Differentiate among Alternative Theories of Trade.

Bilateral trade: a study on SAARC countries

Bilateral trade: a study on SAARC countries

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