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Evaluating Trade between India
and North America using
Gravity Models
The theoretical foundations of
gravity models: Newton’s Law
 The model has been adapted from Newton’s laws of
gravitation
 Statistically measure bilateral trade flows between
different geographical entities or regions
 In the simplest gravity model, bilateral trade flows
between two countries are assumed to be
proportional to the product of their gross domestic
products
 and inversely proportional to a measure of the
distance between them
The Two Frameworks
The Gravity Model
F = K×GDPi×GDPj
d2
F = The Flow of Trade
GDPi = GDP of country i
GDPj = GDP of country j
d = distance between
economic capitals of countries i
and j (sometimes measured by
ports).
K = is a constant.
The Newton Model
F = G×m1×m2
r2
F = The force of attraction
m1 = mass of object 1
m2 = mass of object 2
r = the distance
G = is a constant.
Estimated gravity equation
Newton’s Law-based Normal Trade
 Normal trade
 The basic equation depend upon two
major variables: GDP and distance
 Positive correlation of trade to GDP and
negative correlation with distance
ln (Tradeij) = C + a ln(GDPi) + b ln(GDPj) +
+c ln(distanceij) + uij
“Augmenting” the gravity equations
 Income per capita (higher income
countries trade more)
 Adjacency
 Common language, colonial links
 Institutions, infrastructures, labour
flows,...
 Surprisingly, bilateral tariff barriers often
missing!!!
“Augmenting” gravity model
 Traditional approach to evaluate the impact of
RTAs: Trade creation and trade diversion
ln (Tradeij) = a ln(GDPi) + b ln(GDPj) +
+c ln(distanceij, ,adjacency, language ..) + d (Dummy i) +
+ e (Dummy j) + g (intra-RTAij)+ h (extra-RTAij) + uij
 IMPORTANT the gravity model does not
estimate welfare effects
North America
GDP & Distance
 GDP is used to determine the size of the
country
 The more the GDP, the more the trade
 India’s GDP is tenth largest in the world
 India’s trade with larger economies is more
 Distance between India and North America is
high
 High distances convert to high freight and
transportation cost
Common Language
 Dummy variable which was augmented
on the Gravity Model
 We took binary variable i.e. 0 and 1
values for having a common language
with India
 Perception is that common language
increases trade between nations
Population & Area
 Higher population translates to more
trade
Methodology
 Data has been taken from 2003 to 2010
 Demographic and GDP data :
WorldBank
 Distances data: WorldAtlas
 Trade data: WITS
 Multiple regression using SPSS
 Data available only for 19 out of 23
countries
The Output
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
1 0.888385 0.789228 0.782317 1.149847
a. Predictors: (Constant), Population, GDP_India, Distance, Area, Language, GDP
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 905.9843 6 150.9974 114.2061 0
Residual 241.953 183 1.322148
Total 1147.937 189
a. Predictors: (Constant), Population, GDP_India, Distance, Area, Language, GDP
b. Dependent Variable: Export
The Output
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Std. Error Beta
1 (Constant) 16.76 17.78 0.94 0.35
GDP 0.12 0.05 0.14 2.30 0.02
GDP_India 1.33 0.20 0.22 6.56 0.00
Area 0.55 0.05 0.67 10.41 0.00
Language 0.16 0.28 0.03 0.58 0.56
Distance -6.04 1.71 -0.18 -3.53 0.00
Population 0.03 0.04 0.04 0.80 0.42
a. Dependent
Variable: Export
Language and Population are not
significant
The Output
Model Summary
Model R R Square Adjusted R Square Std. Error of the
Estimate
Change Statistics
R Square Change F Change
1 0.887876 0.7883 0.783747 1.146065 0.788324 172.2439
a. Predictors: (Constant), Distance, GDP_India, GDP, Area
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 904.946 4.000 226.237 172.244 0.000
Residual 242.991 185.000 1.313
Total 1147.937 189.000
a. Predictors: (Constant), Distance, GDP_India, GDP, Area
b. Dependent Variable: Export
The Output
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
Std. Error Beta Tolerance VIF
1 (Constant) 28.108 12.651 2.222 0.028
GDP 0.144 0.043 0.172 3.312 0.001 0.425 2.354
GDP_India
1.318 0.202 0.223 6.539 0.000 0.988 1.013
Area 0.538 0.043 0.653 12.508 0.000 0.420 2.379
Distance -6.457 1.140 -0.197 -5.663 0.000 0.950 1.053
a. Dependent Variable: Export
The Equation
Thank You!

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Gravity Model

  • 1. Evaluating Trade between India and North America using Gravity Models
  • 2. The theoretical foundations of gravity models: Newton’s Law  The model has been adapted from Newton’s laws of gravitation  Statistically measure bilateral trade flows between different geographical entities or regions  In the simplest gravity model, bilateral trade flows between two countries are assumed to be proportional to the product of their gross domestic products  and inversely proportional to a measure of the distance between them
  • 3. The Two Frameworks The Gravity Model F = K×GDPi×GDPj d2 F = The Flow of Trade GDPi = GDP of country i GDPj = GDP of country j d = distance between economic capitals of countries i and j (sometimes measured by ports). K = is a constant. The Newton Model F = G×m1×m2 r2 F = The force of attraction m1 = mass of object 1 m2 = mass of object 2 r = the distance G = is a constant.
  • 4. Estimated gravity equation Newton’s Law-based Normal Trade  Normal trade  The basic equation depend upon two major variables: GDP and distance  Positive correlation of trade to GDP and negative correlation with distance ln (Tradeij) = C + a ln(GDPi) + b ln(GDPj) + +c ln(distanceij) + uij
  • 5. “Augmenting” the gravity equations  Income per capita (higher income countries trade more)  Adjacency  Common language, colonial links  Institutions, infrastructures, labour flows,...  Surprisingly, bilateral tariff barriers often missing!!!
  • 6. “Augmenting” gravity model  Traditional approach to evaluate the impact of RTAs: Trade creation and trade diversion ln (Tradeij) = a ln(GDPi) + b ln(GDPj) + +c ln(distanceij, ,adjacency, language ..) + d (Dummy i) + + e (Dummy j) + g (intra-RTAij)+ h (extra-RTAij) + uij  IMPORTANT the gravity model does not estimate welfare effects
  • 8. GDP & Distance  GDP is used to determine the size of the country  The more the GDP, the more the trade  India’s GDP is tenth largest in the world  India’s trade with larger economies is more  Distance between India and North America is high  High distances convert to high freight and transportation cost
  • 9. Common Language  Dummy variable which was augmented on the Gravity Model  We took binary variable i.e. 0 and 1 values for having a common language with India  Perception is that common language increases trade between nations
  • 10. Population & Area  Higher population translates to more trade
  • 11. Methodology  Data has been taken from 2003 to 2010  Demographic and GDP data : WorldBank  Distances data: WorldAtlas  Trade data: WITS  Multiple regression using SPSS  Data available only for 19 out of 23 countries
  • 12. The Output Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.888385 0.789228 0.782317 1.149847 a. Predictors: (Constant), Population, GDP_India, Distance, Area, Language, GDP ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 905.9843 6 150.9974 114.2061 0 Residual 241.953 183 1.322148 Total 1147.937 189 a. Predictors: (Constant), Population, GDP_India, Distance, Area, Language, GDP b. Dependent Variable: Export
  • 13. The Output Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Std. Error Beta 1 (Constant) 16.76 17.78 0.94 0.35 GDP 0.12 0.05 0.14 2.30 0.02 GDP_India 1.33 0.20 0.22 6.56 0.00 Area 0.55 0.05 0.67 10.41 0.00 Language 0.16 0.28 0.03 0.58 0.56 Distance -6.04 1.71 -0.18 -3.53 0.00 Population 0.03 0.04 0.04 0.80 0.42 a. Dependent Variable: Export Language and Population are not significant
  • 14. The Output Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change 1 0.887876 0.7883 0.783747 1.146065 0.788324 172.2439 a. Predictors: (Constant), Distance, GDP_India, GDP, Area ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 904.946 4.000 226.237 172.244 0.000 Residual 242.991 185.000 1.313 Total 1147.937 189.000 a. Predictors: (Constant), Distance, GDP_India, GDP, Area b. Dependent Variable: Export
  • 15. The Output Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics Std. Error Beta Tolerance VIF 1 (Constant) 28.108 12.651 2.222 0.028 GDP 0.144 0.043 0.172 3.312 0.001 0.425 2.354 GDP_India 1.318 0.202 0.223 6.539 0.000 0.988 1.013 Area 0.538 0.043 0.653 12.508 0.000 0.420 2.379 Distance -6.457 1.140 -0.197 -5.663 0.000 0.950 1.053 a. Dependent Variable: Export

Hinweis der Redaktion

  1. More bilateral trade the larger the countries Less bilateral trade the more the distance between them