Post on 02-Jul-2018
1 | P a g e
Effects of ASEAN-Indian Free Trade agreements on Agriculture
Trade: Evidence from Augmented Gravity Model
Subhash Jagdambe, PhD Scholar, ADRTC, ISEC, Bangalore
Shjagdambe@gmail.com/ jagdambe@isec.ac.in
Abstract
The world is witnessing a rapid spread of economic regionalization, especially in the last 25
years. Regionalization is taking place through the formation of Free Trade Agreement (FTA).
The prime objective of the paper is to analyses the effects of ASEAN-India Free Trade
Agreement on agriculture trade. It cover ten years of data from 2005 to 2014 i.e. five years
pre and five years post-ASIAN-India FTA (AIFTA) period. Bilateral agricultural Trade data
compiled from World Trade Integration Solution and Gross Domestic product, per capita
GDP and Population data are taken from world Development Indicator published by World
Bank. The paper evaluates agricultural trade creation and diversion effects of major FTAs.
Trade creation and diversion effects are estimated using an augmented gravity model with
various fixed effects to control heterogeneity. The paper estimate the trade creation and trade
diversion effect for agricultural trade using intra and extra dummy variable. The paper found
clearly the trade creation effect for AFITA members. The result reveal that intraregional
agricultural trade among AIFTA members increase by 219 % [exp (1.16)-1)*100] more than
they traded with rest of the world. The Paper also found strong trade diversion effect for
MERCOSURE and EU15 over the study period. The result supports the argument that, the
FTAs is positive path towards freer multilateral trade. In nut shell the FTA has significant
and positive impact on bilateral agricultural trade among members during the study period.
Key words: Agriculture, FTA, Trade creation and Trade Diversion.
******
Introduction
The world is witnessing rapid spread of economic regionalism, especially in the last 25 years.
Regionalism is taking place through the formation of Free Trade Agreement (FTA). It would
reflect that, every month brings news of yet another agreement among a group of countries,
or between one group and another (Frankel, et. al. 1997). The number of FTAs has been
2 | P a g e
increasing steadily after the World Trade Organization (WTO) came into existence in 1995.
For instance, from 1948 to 1994 there were only 124 FTAs have been notified to the General
Agreement on Tariff and Trade (GATT), subsequently over 400 additional FTAs covering
trade in goods and services have been notified to the WTO/GATT. Out of which 300 FTAs
are in force and the remaining are in process in the year 20151. Basically, FTAs are form to
increase economic strength through removing barriers to trade and investment among
members. However, these FTAs had a controversial role in moving towards multilateralism.
Some argued that it hampering to multilateral trade negotiation (Levy 1997), and others say
that FTAs are positive path towards freer multilateral trade (Freund 2000).
The economist have debated long on this issues, whether FTAs are benefited to the countries
included in FTAs and on the rest of the world (Bhagwati and Krueger 1995). The debate
divided between those who support FTAs and those who oppose them. The former group
supported the Trade Creation (TC) effect of FTAs and latter supported the Trade Diversion2
(TD) effect of FTAs. The former group emphasised that TC effect outweighed the TD effect.
According to them, FTAs can improve recourse allocation and improve income among
members through reducing trade barriers. As well as, the production will also shift towards
most efficient producers and consumer will be better off because they can purchase goods at
lower price. Overall FTAs are welfare improving for members as well as the rest of the
world. The latter group are emphasis on TD effect of FTAs. By definition FTAs are
discriminatory in nature because members will grant preference among themselves while
retaining the barriers to non-members. Hence, the extent of these TC and TD effects is an
empirical question. Moreover, this study focuses on the effect of FTAs on agriculture trade
creation and trade diversion effect.
Literature Review
This section provides a brief overview of selected studies pertaining to trade analysis using
gravity model. Reviews divided in two parts, first part discuss with impact of FTAs on
overall trade flows and second parts only agriculture trade flows.
1 http://rtais.wto.org/UP/PublicMaintainRTAHome.aspx.
2 In general, trade creation means that a free trade area creates trade that would not have existed
otherwise. As a result, supply occurs from a more efficient producer of the product. On the other hand,
trade diversion means that a free trade area diverts trade, away from a more efficient supplier outside the
FTA, towards a least efficient supplier within the FTA (Suranovic, S. M.2010)
3 | P a g e
Studies Pertaining to Impact of FTA’s On Trade Flows
Authors Objective Dataset & Technique
of Estimation
Major Findings
Endoh M.
(1999)
Investigation of trade
creation and trade
diversion in the EEC,
LAFTA and CMEA.
Panel data, of 1960-
1994,
OLS
They found that TC and TD effect
for each RTAs generally weakening
during 1960. Further result shows
that positive trade effect to CMEA
with Japan but no strong evidence
found for EEC and LAFTA during
analysis
Sologa and
Winters (2001)
Analysis of regionalism
and trade agreement
effects in trade in the
1990
Cross sectional, 58
countries, 1980-1996.
Tobit, Fixed effects.
Study found no indication of ‘new
regionalism’ boosted intra-bloc
trade. Further, the study found some
evidence of TD only for EU and
EFTA.
Fukao et al.
(2003)
Analysis of trade effect
under NAFTA
Panel data, 1992-1998
OLS with Fixed effects.
Authors found strong evidence of
TD mainly for US imports of textile
and apparel products.
Filippini &
Molini (2003)
Analysis of East Asian
Trade flows
Panel data, 1970-2000,
OLS with Fixed effects
Musila (2005)
Examination of the
intensity of TC and TD
in COMESA, ECCAS
and ECOWAS
Cross sectional data,
1991-1998
OLS
Paper found the intensity of trade
creation or diversion varies from
region to region and from period to
period. Further, the results suggest
that TC is more in ECOWAS
followed by COMESA. He found
less evidence for TD of all three
regions during analysis.
Tang (2005)
Analysis of regional
trading arrangement for
the NAFTA, ANZCER,
and ASEAN countries.
Panel data, 1989-2000,
OLS and 2SLS
The results suggest that the
formation free trade area trade flow
has increase among the member’s
countries particularly ANZCER and
ASEAN. However, The TD effect
found for ANZCER with non-
members, contrast the TC has
increased for ASEAN. No evidence
found for NAFTA. Finally conclude
that the developing member
4 | P a g e
countries which having similar
income trade extensively more each
other
Peridy (2005) Analysis of the
AGADIR FTA effects
Panel data, 1975-2001
OLS with two way
random effects
Empirical result shows that due to
high trade cost the trade flows was
remain low between these countries.
In particular the estimated border
effect shows that deficit of trade
integration among members. Finally
he argued that the lack of trade
Complementarity between them the
export potential was low during
analysis.
Peridy (2005) Investigation of
EMFTA effects to trade
Panel data, 1975-2001
OLS with fixed &
random effects
The empirical results indicate that
the Mediterranean countries exports
increased to the EU by 20-27 %
depending on the model
specification.
Carrere (2006)
Investigation of the
effects of regional trade
agreement
Panel data, 1962-1996
OLS with two way
random effects.
The study found that the regional
agreements have generated
significant increase in trade among
members at the expense of the rest
of the world.
Kalirajan
(2007)
Infestation of regional
cooperation effects in
trade
Panel data, 1999-2002
GLS
The results show that Australia
achieves more 15 % potential
export with IOR-ARC countries due
to regional cooperation.
Lee and Park
(2007)
Investigation of
optimized regional
trade agreements for
east Asia.
Panel data, 1994-1999
OLS with Fixed and
random effects
They found enhancing trade
facilitation is a complementary
policy alternative to tariff reduction.
Study confirmed that RTAs
comprised equipped with better
trade facilitation are more likely to
trade creating and less likely to
trade diverting.
Source: Author
5 | P a g e
Studies Pertaining to Impact of FTA’s on Agriculture Trade Flows
In empirical research very few papers gave attention to an impact of FTAs on agricultural
trade flows among members. Among them Jayasinghe and Sarker (2008) estimate the TC and
TD effects of North American Free Trade Agreement (NAFTA) for six major agricultural
commodities. They found that intra-NAFTA trade has increased. But they did not point out
weather this increased as on the expense on rest of world. Another important study done by
Lambert and McKoy (2009), investigate the intra-and extra-bloc effects of FTAs on
agricultural and food products. They notice that generally members in FTAs increase
agricultural and food trade. For instance, NAFTA members’ agricultural trade has increased
by 145 % during analysis (trade creation effect). As well as they also notice trade diversion
effect for Caribbean Community and Common Market, the Central American Common
Market, the Andean Community, and the Common Market for Eastern and Southern Africa
(COMESA). The study by Lin and Reed (2010) estimate the impact of FTAs on member’s
agricultural trade. They found that ASEAN-China PTAs, EU-15, EU-25 and Southern
African Development Community (SADC) agreements have increased agricultural trade
among their members. There was significant export and import diversification from the EU-
15 but the creation of the SADC increased agricultural exports to non-member countries.
Less evidence found for trade creation among NAFTA but results support for export
diversion.
Overall very few studies focused on the impact of FTAs on agriculture trade. The reason is
that the agriculture sector has been excluded from most of the agreements. Since, the Doha
Round of development (2001)3, the agriculture sector got an important place in most of
FTAs. Earlier studies were consternated mostly only on NAFTA, EU etc. But no study has
found for ASEAN-India Free Trade Agreement (AIFTA). Hence, the present paper focused is
on the effect AIFTA on members’ agricultural trade using gravity model analysis.
GRAVITY MODEL SPECIFICATION
Jan Tinbergen (1962) was the first author applied gravity model to analyses international
trade. Since then the gravity model has became a ‘work-horse of international trade analysis
due to its explanatory power and it is commonly used in explaining the trade flows between
countries (Eichengreen and Irwin 1998). He shows that the size of bilateral trade flows
between any two countries can be approximated by a law called the “gravity equation” by
3 The agriculture sector was a cornerstone of Doha Round of WTO negotiation.
6 | P a g e
analogy with the Newtonian theory of gravitation4, countries trade in proportion to their
respective GDPs and proximity. Initially the gravity model was focused on stable relationship
between countries trade with their size of economise, and their distance. But international
trade theory at that time such as Ricardian and Heckscher-Ohlin (HO) theories relies on
difference in technology across countries and difference in factor endowments among
countries, respectively to explain trade pattern among the countries. These models were not
capable to provide a foundation for the gravity model. For instance, in HO theory country
size has little to do with the structure of trade flows.
Initially there was a lack of theoretical foundation for gravity model. To fill this gap the first
and foremost attempt was made by Anderson (1979). He assumed that where goods were
differentiated by country of origin (Armington assumption) and consumers have preference
defined over all the differentiated products. Subsequent, many others have explored the
theoretical determination of bilateral trade in which gravity equation were associated with
simple monopolistic competition model. In particular, Bergstrand (1985, 89), derived the
micro foundation for gravity model using general equilibrium approach. He used the utility
function to derive trade demand with income constraints, while trade supply is derived from
profit maximisation of firm in exporting country with resource allocation constraints. His
model is known as generalised gravity model because it included both price and income
terms.
The more appropriate theoretical rational related to the determination of bilateral trade
depends on GDP comes from work done by Helpman (1987), and Helpman and Krugman
(1985). They argued that, consumers have a preference for goods which they consumed,
products are differentiated by firm, not just by country, and firms are monopolistically
competitive. Further they said, the HO theory of comparative advantage does not have the
property that bilateral trade depends on the products on incomes, as it does in the gravity
model (Frankel, et.al. 1997).
Deardorff (1997) derived the gravity model from of HO theory as well as imperfect
competition. He incorporated the role of shipping cost as proxy for distance. More recently
Anderson and van Win coop (2003) derived gravity model based on constant Elasticity of
Substitution (CES) expenditure function that can be easily estimated. Further they shows that
4 According to Newton’s law of Gravity, the force between two masses is directly proportional to the product of
their size and inversely proportional to the square of the distance between them (Frankel, et.al. 1997).
7 | P a g e
bilateral trade is determined by relative trade cost, i.e. the propensity of country j to import
from country i is determined by country j trade cost toward i relative to its overall resistance
(average trade cost) to imports and to the average resistance facing exporter in rest of the
world.
Overall the theoretical argument that bilateral trade depend on the product of GDP, Frankel
et al (1997) also derived gravity model including imperfect substitutes and product
differentiation.
Traditional (Basic) Gravity Model
The basic gravity model is has multiplicative form as follows
𝑿𝒊𝒋 = 𝜶 Yi Yj / Distij (1)
Where, Xij is the monetary value of trade between i and j, Yi an Yj represents the income of
countries i and j respectively. Distij represent the distance between country i and j. It implies
that the bilateral trade between countries i and j is proportional to their respective income and
inverse to distance.
Standard Gravity Model
In basic gravity model only income and distance variable were included, while in standard
gravity model these tow variable and some more variable has incorporated like, common
border, common language and per capita income (Frankel 1197). The notation of the standard
gravity model explained in detail subsequent section.
Framework for Estimation of Standard Gravity Model by a Panel Data regression
Model
We applied the following model for estimation proposes.
Xijt = α GDPit β1 GDPjt β2 POPit β3 POP jt
β4 Distij β6 eijt (2)
Where, Xijt is the bilateral trade (export plus import) between pairs5 of country i and country
j. All other variable are defined and expected sign are given table 1. Taking the logarithm of
equation (2) and incorporating the dummy variables listed in table 1.
lnXijt = β0 + β1 ln GDPit + β2ln GDPjt +β3ln POPit +β4 ln POP jt
+ β5 lnDistij + β6Comlij + β7Borderij + β8AIFTA_Intraij
+ β9 AIFTA_Extraij + β10 SAPTA_Intraij + β11 SAPTA_Extraij
5 The unit of observation is a pair of countries, not a single country.
8 | P a g e
+ β12 NAFTA_Intraij + β13NAFTA_Extraij + β16MERCOSURS_Intraij
+ β17 MERCOSURS_Extraij β14 + EU15_Intraij
+ β15 EU15_Extraij + εijt (3) Where, εijt = is the error term.
We applied random effect model (GLS) to estimate equation 3. If panel data with country-
pair dummy variable it should apply random effects rather than fixed effects model (Barun et.
al. 2005, Carrere, 2006). Hence, if we estimate separately the effect of bilateral variables such
as distance, common border etc. that would otherwise be confounded with fixed effect model.
The Problem of Endogenity
Researchers are often facing the problem of Endogenity when estimating the impact of trade
policies using gravity model. For instance the problem of Endogenity is common when
estimating the impact of FTAs with gravity model. FTAs are likely to be purely exogenous.
Countries are forming FTAs among them because they already trade a lot. In addition
countries are signing FTAs because they have common characteristics such as common
language, culture, colonial relationship etc. If this is the case, the FTA dummy on the right
hand side of the gravity equation is correlated with the error term.
An alternative method of dealing with the Endogenity problem is to include fixed effects for
bilateral country pair and time varying fixed effects for reporter and partner countries (Lin
and Reed 2010).
In order to control the individual effect of the time, country and country-pair effect we added
three different dummies for panel estimation. Equation 3 and 4 we estimate three different
ways. First one with time fixed effect (αt), second with time, reporter and partner fixed effect,
which adds αt , αi and αj to the equation and finally with time and country-pair fixed effect,
which adds αt and αij to the equation.
In panel estimation the time fixed effect will control the global economic effect that affect
global trade flows. To measure the country specific factors like, infrastructure, level of
development, trade facilitation and multilateral resistance term etc. it’s very difficult. In order
to control all these factors we add reporter and partner fixed effect dummy variable. The
country-pair fixed effect control the omitted variables that are not included into the model.
Earlier studies found using the time fixed effects, country specific fixed effects and country-
pair fixed effects such as, Bair & Bergstrand, (2007), Lin & Reed, (2010), Egger, P. (2000),
and Matyas (1997) to control endogeneity problem in standard gravity model.
9 | P a g e
Table1. Independent Variable and Expected Signs
Variables Descriptions Expected Sign
Dependent Variable6
lnXij Log* of bilateral trade flows from
country i to country j
Independent variable
lnGDPi Log of GDP of the reporter country i +
lnGDPj Log of GDP of the partner country j +
lnPOPi Log of Population of reporter country i + or -
lnPOPi Log of Population of partner country j + or -
lnDistij Log of distance from country i to j -
Comlangij Dummy variable; =1 if country i and j
have a common official language; =0
Otherwise
+
Comborderij Dummy variable; =1 if country i and j
have a common border; =0 Otherwise +
AIFTA_Intraij Dummy variable; =1 if country i and j
belongs to the AIFTA, otherwise = 0
AIFTA_Extraij Dummy variable; =1 when either
country i or j belongs to AIFTA, where
another country belongs to non-AIFTA,
otherwise =0
SAPTA_Intraij Dummy variable; =1 if country i and j
belongs to the SAPTA, otherwise = 0
SAPTA_Extraij Dummy variable; =1 when either
country i or j belongs to SAPTA, where
another country belongs to non-SAPTA,
otherwise =0
NAFTA_Intraij Dummy variable; =1 if country i and j
belongs to the NAFTA, otherwise = 0
NAFTA_Extraij Dummy variable; =1 when either
country i or j belongs to NAFTA, where
another country belongs to non-NAFTA,
6 For this analysis dependent variable is bilateral trade flows (exports plus imports) between country pair, zero
trade flows are existed. In order t avoid zero trade flows observation from the analysis, 1 is substituted for all
zero observations. It does not affect in log-linear model because log 1 is zero.
10 | P a g e
otherwise =0
MERCOSURE_Intraij Dummy variable; =1 if country i and j
belongs to the MERCOSURE, otherwise
= 0
MERCOSURE_Extraij Dummy variable; =1 when either
country i or j belongs to MERCOSURE,
where another country belongs to non-
MERCOSURE, otherwise =0
EU15_Intarij Dummy variable; =1 if country i and j
belongs to the EU15, otherwise = 0
EU15_Extraij Dummy variable; =1 when either
country i or j belongs to EU15, where
another country belongs to non-EU15,
otherwise =0
*Natural Log.
There are two way of measuring economic size of country in the gravity model: GDP or
population. The high level of income (GDP) in reporting country (i) indicates a high level
of production which increases the availability of goods for trade. Therefore we expect the
coefficient sign of reporter country’s GDPi to be positive. The coefficient sign of partner
country’s GDPj is also positive because a high level of income (GDP) in the partner country
indicates higher purchasing power of goods.
The estimated coefficient sign of population of the reporting country may be positive or
negative (Oguledo and Macphee 1994) depending on weather a big country trades more than
a small country (economies of scale effect) or weather the country trades less when it is big
(absorption effect), (Martinez & Lehmann 2001). Another factor will also influence the
estimated coefficient sign of population in gravity equation is that the composition effect of
population which influence supply and demand of goods or the mix of goods demanded is
also different for each country. The estimated coefficient sign on population of the partner
country may be positive or negative for similar reasons.
The estimated coefficient sign of distance variable is expected to be negative since it is a
proxy of all possible resistance factors to trade. The geographical proximity of any country
will have positive influence on trade flows between countries. Geographical proximity is
captured through common border dummy variable. Countries having common border will
11 | P a g e
trade more than distance. It will reduce the transaction cost of trade between them. Moreover,
the coefficient of common border variable is expected to be positive. Since the basic gravity
model is log-linear form, the coefficient of dummy variable is interpreted by taking its
exponent (Frankel, 1997). For instance, if the value of common border coefficient is 0.60, it
indicates that two countries having a common border trade 82 [exp (0.60)-1*100] per cent
more trade than those without having common border.
Another essential factor is affecting trade flows are cultural link among countries. The
presence of common language will indicate the cultural familiarity between members; hence
the cultural link will reduce the transaction cost among countries. The cultural link is
captured through official common language dummy variable. The estimated coefficient of
common language variable is also expected to be positive.
Finally, we have incorporated FTAs dummies to estimate agricultural trade creation or
diversion effects of the AIFTA, SAPTA, NAFTA, MERCOSURE and EU15. First, dummy
variables are constructed to measure agricultural intra trade enhancing effect (TC) of FTAs
on the members. These dummies are, AIFTA_Intraij, SAPTA_Intraij, NAFTA_Intraij,
MERCOSURE_Intraij and EU15_Intraij. After the formation of FTAs members will expand
their trade among members through eliminating tariff and non-tariff barriers. Hence, a
positive value implies that the FTAs have contributed to increased trade among its member
countries (TC effect). Secondly, we incorporated another dummies to measure agricultural
trade diversion effect of FTAs on non-members. These dummies are, AIFTA_Extraij,
SAPTA_Extraij, NAFTA_Extraij, MERCOSURE_Extraij and EU15_Extraij. The formation of
FTAs members will increase trade among them while simultaneously reducing trade with
non-members. Hence, a negative values it implies that trade diversion effects of FTAs,
whereas positive value suggest that absence of diversion effects on non-members.
Data Source
The sample size uses fifty countries of Asia, European Union, United States and South
American continents that part of five regional trade agreement (SAPTA, AIFTA, NAFTA,
MERCOSURE and EU). These countries are selected on the basis of export and import share
of major Asian- India Free Trade Agreement (AIFTA) members. The major AIFTA members
are Cambodia, India, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam.
These eight are selected for the analysis and the other three countries namely, Brunei, Laos
PDR and Myanmar are excluded due to non-availability of data.
12 | P a g e
The study cover ten years of data from 2005 to 2014 i.e. five years pre and five years post-
AIFTA analysis. AIFTA come into force in the year of 2010. The total number of
observations are 24500 [50*49*10] for ten years from 2005 to 2014. It includes six SAPTA
countries, seven ASEAN countries, three NAFTA countries, six MERCOSURE countries and
fifteen EU countries and other thirteen countries.
The definition of agriculture sector for this analysis is based on Uruguay Round of
Agreement on Agriculture (URAoA) and Harmonized Commodity Description and Coding
System (HS)7. Bilateral trade data compiled from World Integrated Trade Solution (WITS)
which sourced from UN Comtrade data base. Gross Domestic Products (GDP), Per Capita
GDP and Population data were obtained from the World Development Indicators database.
Bilateral trade flows and GDP are at current prices. Earlier studies found that there is only
marginal difference exist while using real prices. For instance, Srinivasan (1995) showed that
purchasing power parity rates are subject to large measurement error. Frankel (1997) found
little difference in the gravity equation results using real data. Data on common language,
common border and distance taken from Centre d’Etudes Prospective et d’Informations
Internationals.8 CEPII uses the great circle formula to calculate the geographic distance
between countries, referenced by latitudes and longitudes of the largest urban agglomerations
in terms of population.
Estimation Result
The following result estimated using equation 3 with various fixed dummy variable to control
endogeneity. Table 2 include the result listed columns 1 to 3. For equation 3 used random
effect model (GLS), which includes zero trade flows observation with adding small fraction.
First column gives result with time fixed but no country fixed effect, second column with
time, reporter and partner country fixed effect, and third column is with time and country pair
fixed effect.
7 It follows HS code 01 to 24 (excluding fish and fish products). It also include HS code 2905.43(mannitol), HS
Code 2905.44(sorbitol), HS Code 33.01(essential oils), HS code 35.01 to 35.05(albuminoidal substances,
modified starches, glues), HS Code 3809.10(finishing agents), HS Code 3823.06(sorbitol n.e.p.), HS Code 41.01
to 41.03(hides & skins), HS Code 43.01(raw fur skins), HS Code 50.01 to 50.03(raw silk & silk waste), HS Code
51.01 to 51.03(wool & animal hair) HS Code 52.01 to 52.03(raw cotton, waste and cotton carded or combed),
HS Code 53.01(raw flax) and HS Code53.02(raw hemp).
8 http://www.cepii.fr/CEPII/en/bdd/_modele/download.asp?id=6.
13 | P a g e
Impact of GDP, Population, Distance and Language
The model with time fixed effect shows the estimated GDP coefficient for both reporter and
partner country have the expected positive sign. The magnitude of GDP coefficient for
reporter is greater than partner country. They are significant at the 1 % level of significance.
Hence, results indicated that there is positive relationship between country pair income and
bilateral trade. The estimated coefficient of population for reporter country was shows
statistical insignificant sign. Contrast, the magnitude of partner country population coefficient
was notice statistical significant at 1 per cent level. It implies that estimated coefficient of
population in partner countries will have greater impact on bilateral trade between pair. The
bilateral variables such as distance, common language and common border have expected
sign and statistical significance. Among bilateral variable the magnitude of common border
(0.93) is higher than other variables. It shows that courtiers having common border will trade
more than distance. The result are support the ‘natural’ trading hypotheses.
The model with time, reporter and partner country fixed effect shows similar sing for GDP
coefficient as it was in time fixed effect model. But, the magnitude of GDP coefficient has
declined for both reporter and partner country. It means country specific fixed such as
infrastructure, level of development etc. affecting bilateral trade between pair. The estimated
coefficient of population for reporter has turns from statistical insignificant to significant
level. Contrast, for partner country has turns from statistical significant to insignificant level.
The other bilateral variables are similar to the previous one but the magnitude has change.
For instance the estimated value of common border has declined for 0.93 to 0.30.
The model with time and country pair fixed effect listed in column 3 shows the similar sign
for GDP and population coefficient as previous one except partner country. The distance,
common language and common border (time invariant variable) capture the country pair
fixed effect dummy variable. The time invariant variable fall out of the model because the
country-pair fixed effects encompass them (Lin & Reed, 2010)
Impact of Intra FTAs Dummy Variable (TC)
The model with only time fixed effects shows the trade creation effects for ASIAN-India free
trade Agreement (AIFTA), South Asian Preferential Trade Agreement (SAPTA), Southern
Common Market (MERCOSURE) and European Union (EU) 15. The result indicates that
intraregional agricultural trade among AIFTA members increase by 219 % [exp (1.16)-
14 | P a g e
1)*100] more than they traded with rest of the world. For SAPTA, MERCOSURE and EU15
shows purely trade creation for agricultural trade among members. The estimated coefficient
of NAFTA dummy was found statistical insignificant.
The model with time, reporter and partner country fixed effect shows that significant trade
creation effect for AIFTA, SAPTA and NAFTA among them. The magnitude of the SAPTA
and NAFTA is found more than AIFTA. For NAFTA, it found statistical insignificant in the
previous model, but it turns at statistical significant level at 1 %. It showing a stronger
magnitude compare to other coefficient. For MERCOSURE and EU15 it turns negative,
showing absence of trade creation among them. Finally, the result suggest that for AIFTA
members will trade 136 [exp(0.86)-1)*100] per cent more than with rest of the world as it
would predict standard gravity model.
The model with time and country pair fixed effect listed in column 3 shows that trade
creation effects among members except EU15. The magnitude of NAFTA variable found
more than any other FTAs dummy variable. For the AIFTA and SAPTA which are found to
increase intraregional trade among members by 141 per cent and 76.82 per cent respectively.
For NAFTA result found more ambiguous. Overall the formation of FTA agriculture trade
has increased among members, it shows that formation of FTA it will positively affect on
agricultural trade among them.
Impact of Extra FTAs Dummy Variable (TD)
The model with only time fixed effects shows that for AIFTA and MERCOSURE found
statistical significant and all other found statistical insignificant. The result is indicating that
after the formation of FTA agriculture trade has increased with non-FTA members. For
instance, AFITA and MERCOSURE agriculture trade will increase by 17 % and 46 % with
non-FTA respectively.
The model with time, reporter and partner country fixed effect for MERCOSURE and EU15
found negative sign for extra dummy variable. It shows purely trade diversion effect with
respect to non-FTA members during the study period. For SAPTA and NAFTA found
absence of trade diversion effect.
Finally the model with time and country pair fixed effect listed in column 3 shows that that
purely trade diversion effects for the MERCOSURE and EU during the study period but these
result are not supported to the earlier findings of the study. The remaining extra dummy
15 | P a g e
variable found absence of trade diversion effect with non-FTA members over the study
period. The R2 has increased from 0.53 to 0.89, it means the model with time and country
pair fixed effect is capturing the time, country and country-pair fixed effects which we
assumed.
Conclusion
The paper estimates the impact of FTA on members’ agricultural trade using random effect
model (GLS) for standard gravity model analysis. The paper estimate the trade creation and
trade diversion effect for agricultural trade using intra and extra dummy variable. The result
noticed that for AIFTA members will trade 136 [exp (0.86)-1)*100] per cent more than with
rest of the world as it would predict standard gravity model. In contrast paper also found that
AIFTA’s agriculture trade will increase by 17 % with non-FTA. The paper also found clearly
trade creation for SAPTA, NAFTA, MERCOSURE and EU15 during the study period. The
paper found strong trade diversion for MERCOSURE and EU15 over the study period. The
result are supported to debate, the FTAs are positive path towards freer multilateral trade. In
nut shell the FTA has significant and positive impact on member’s bilateral agricultural trade
during the study period.
Table 2: The Gravity Model Regression Result
Time but No
Country Fixed Effect
Time and Reporter
and Partner Country
Fixed Effect
Time Bilateral
Country-pair Fixed
Effect
1 2 3
GLS GLS GLS
Ln_GDPi 0.78***
(0.05)
0.74***
(0.09)
0.74***
(0.10)
Ln_GDPj 0.48***
(0.03)
0.32***
(0.08)
0.32***
(0.08)
Ln_POPi 0.06
(0.04)
-1.94***
(0.32) -1.94***
(0.34)
Ln_POPj 0.24***
(0.03)
-0.13
(0.28) -0.13*
(0.29)
Ln_Distij -0.42***
(0.06)
-0.62***
(0.06)
C Langij 0.75***
(0.11)
0.78***
(0.10)
C Borderij 0.93***
0.30**
(0.17)
16 | P a g e
(0.17)
AIFTA_Intra 1.16***
(0.39)
0.86***
(0.38) 0.88*
(0.55)
AIFTA_Extra 0.16***
(0.06)
0.12
(0.08) 0.12**
(0.07)
SAPTA_Intra 0.91***
(0.37)
3.39***
(0.58) 0.57*
(1.55)
SAPTA_Extra -0.17
(0.15)
1.39***
(0.50) 2.05***
(0.48)
NAFTA_Intra -1.36
(1.89)
3.34***
(1.73) 6.38***
(0.57)
NAFTA_Extra 0.03
(0.18)
3.53***
(0.56) 2.71***
(0.57)
MERCOSURE_Intra 2.00***
(0.24)
-2.45***
(0.45) 0.53*
(0.94)
MERCOSURE_Extra 0.38***
(0.15)
-3.49***
(0.34)
-5.40***
(0.28)
EU15_Intra 1.75***
(0.15)
-3.20***
(0.63)
-4.24***
(0.90)
EU15_Extra -0.05
(0.13)
-4.61***
(0.62)
-3.30***
(0.64)
Cons_ -10.35***
(0.86)
4.75***
(1.83)
-1.00*
(0.59)
N 24500
24500
24500
R2 0.53
0.70
0.89
Note: Standard errors in Parentheses; *p<0.1, **p<0.05, ***p<0.01; Standard errors were calculated
using White’s heteroskedastic robust standard errors.
17 | P a g e
Reference:
Anderson J. E. (1979), A Theoretical Foundation of the Gravity Model. American Economic
Review 69 (1): 106-116.
Anderson, J. E and E. Van Wincoop. (2003), Gravity with Gravitas: A Solution to the Border
Puzzle. American Economic Review 93 (1): 170-192.
Bair, S. L. And Bergstrand, J. H. (2007), Do free Trade Agreement Actually Increase
Members’ International Trade? Journal of International Economics 71 (1):72-95.
Burger, M., Oort, F. And Linders, G. (2009), On the Specification of the Gravity Model of
Trade: Zeros,Excess Zerosand Zero-Inflated Estimation. Spatial Economic Analysis
4(2): 167-190.
Bhagwati, J and Krueger A. O.ed. (1995),The Dangerous Drift to Preferential Trade
Agreements. Washington DC, American Enterprise, Institute for Public Policy
Research.
Brun et. al. (2005), Has Distance Died? Evidence from a panel gravity Model, The World
Economy Review, 19(1): 99-120.
Carrere, C. (2006), Revisiting the Effects of Regional Trade Agreements on Trade flows with
Proper Specification of the Gravity Model. European Economic Review 50(2): 223-
247.
Deaedorff, A. (1998), Determinants of Bilateral Trade: Does gravity Work in a Classical
World? In: The Regionalization of the world Economy, ed. Jaffery Frankel, Chicago
University Press.
Eichengreen, B. and Irwin D. A. (1998), The Role of History in Bilateral Trade Flows. NBER
Chapters In: The Regionalization of the world Economy, ed. Jaffery Frankel, 33-57.
Chicago University Press.
Endoh M.(1999), Trade creation and Trade Diversion in the EEC, the LAFTA and the
CMEA: 1960-1994. Applied Economics 31(2): 207-216.
Freund, C. (2000), Different Paths to Free Trade: The gains From Regionalism. Quarterly
Journal of Economics 115(4):1317-1341.
Fukao, K, and Okubo, T. (2003), An Econometric Analysis of Trade Diversion under
NAFTA. North American Journal Economics & Finance 14(1): 2-24.
Filippini C, and Molini V. (2003), The determinants of East Asian trade flows: a Gravity
Equation Approach. Journal of Asian Economics 14(5): 695-711.
18 | P a g e
Frankel, J. A. (1997), Regional Trading Blocs in the world Economic System. Washington
DC, Institute for International Economics.
Frankel, J. A. (1998), the Regionalization of the World Economy. Chicago University Press
Ghosh S, and Yamarik S. (2004), Are Regional Trading Arrangements Trade Creating? An
Application of Extreme Bounds Analysis. Journal of International Economics 63(2):
369-395.
Grant, J. H. And Lambert, D. M.(2008), Do Regional trade Agreements Increase Members’
Agriculture Trade. American Journal of Agricultural Economics 90 (3): 765-782.
Helpman, E. And Krugman, P. (1985), Market Structure and Foreign trade: Increasing
Returns, Imperfect Competition, and the International Economy. Cambridge, MA:
MIT Press.
Helpman, E.(1987), Imperfect Competition and International Trade: Evidence From
Fourteen Industrial Countries . Journal of the Japanese and International Economies
1:62-81.
Jayasinghe, S. And Sarker, R. (2008), Effects of Regional Trade Agreements on Trade in
Agrifood Products: Evidence from Gravity Modelling Using Disaggregate Data.
Review of Agricultural Economics 30(1): 61-81.
Kalirajan, K. (2007), Regional Cooperation and Bilateral Trade Flows: An Empirical
Measurement of Resistance. The International Trade Journal 21(2): 85-107.
Lin, S. And Reed, M.R. (2010), Impact of free Trade Agreements on Agriculture Trade
Creation and Trade Diversion. American Journal of Agricultural Economics 92(5):
1351-1363.
Lambert, D. M. And McKoy S. (2009), Trade Creation and Diversion Effects of Preferential
Trade Association on Agricultural and Food Trade. Journal of Agricultural
Economics 60(1): 17-39.
Levy, P.I. (1997), A Political Economy Analysis of Free-Trade Agreements. American
Economic Review 87(4): 506-519.
Lee H, and Park I.(2007), In Search of Optimized Regional Trade Agreements and
Applications to East Asia”. World Economy 30(5): 783-806.
Musila J.(2005), The Intensity of Trade Creation and Trade Diversion in COMESA, ECCAS
and ECOWAS: a Comparative Analysis. Journal of African Economics 14(1): 117-
141.
19 | P a g e
Martinez, I. and Lehmann, L.(2001), Augmented Gravity Model: An Empirical Application
to MERCOSURE-European Union Trade Flows. Journal of Applied Economics 2:
291-316.
Matyas, L. (1997), Proper Econometric Specification of the Gravity Model, The World
Economy 20(3): 363-368.
Oguledo, V. and Macphee, C. (1994), Gravity Models: A Reformulation and an Application
to Discriminatory Trade Agreements. Journal of Applied Economics 26: 107-120.
Peridy N. (2005), Toward a Pan-Arab Free Trade Area: Assessing Trade Potential Effects of
the AGADIR Agreement. Development Economics XLIII-3: 329-345.
Santos S. And Tenreyro, S. (2006), The Log of gravity. Review of Economics and Statistics
88(4):641-658.
Soloago, I. and Winters, A. (2001), How Has Regionalism in the 1990sAffected Trade?
North American Journal of Economics and Finance 12(1):1-29.
Suranovic, S. M. (2010), International Trade: Theory and policy, George Washington
University.
Tang D. (2005), Effects of the Regional Trading Arrangements on Trade: Evidence from the
NAFTA, ANZCER and ASEAN Countries, 1989-2000. The Journal of International
Trade & Economic Development 14(2): 241-265.
Tinbergen, J. (1962), Shaping the World Economy: Suggestions for an International
Economic Policy. New York: The Twentieth Century Fund.
20 | P a g e
Appendix
Sample Countries
SAPTA AIFTA NAFTA MERCOSURE EU_15 Rest of the
World
Afghanistan Cambodia Canada Argentina Austria Algeria
Bangladesh Indonesia Mexico Bolivia Belgium Australia
India Malaysia USA Brazil Denmark China
Maldives Philippines Paraguay Finland Ghana
Pakistan Singapore Uruguay France Hong Kong
Sri Lanka Thailand Venezuela Germany Israel
Vietnam Greece Japan
Hungary Korea RP
Ireland Qatar
Italy Russian F
Luxembourg Saudi Arab
Netherland South Africa
Portugal Switzerland
Spain Sweden U.K.
Source: Author’s Aggregations