Herath H. M. P. S et al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

1

VVoolluummee 0033,, 22001144

IIssssuuee 0011

SSEETTSScchhoollaarrss

wwwwww..sseettsssscchhoollaarrss..oorrgg

Inte

rnat

ion

al J

ou

rnal

of

Ap

pli

ed R

esea

rch

in

International Journal of Applied Research in

Ca

ISSN: 1839-8456 Perth & Melbourne, Australia

www.setscholars.org/index.php/ijarbae

EExxeeccuuttiivvee EEddiittoorr--iinn--CChhiieeff Md. Al Mamun La Trobe University, Australia.

CChhiieeff,, EEddiittoorriiaall BBooaarrdd Md. Abdul Hannan Mia, PhD

EEddiittoorriiaall BBooaarrdd MMeemmbbeerrss Shaokun Coral Yu, PhD Northern Illinois University, USA Dr. Iqbal Hossain, Post-Doc in Econometrics, Imperial College, UK M Kabir Hassan, PhD University of New Orleans, USA Md. Shariat Ullah University of Dhaka, Bangladesh & Ritsumaiken University, Japan. Saalem Sadeque University of Western Australia, Australia. Rami M. Ayoubi, PhD Damascus University, Syria Ondrej Zizlavsky PhD Brono University of Technology, Czech Republic Rarshid Mehrdoust, PhD University of Guilan, Iran Md. Moazzam Hossain Curtin University, Australia Jai Ganesh PhD Infosys Labs, Bangalore, India Md. Mohan Uddin, PhD University Utara Malaysia, Malaysia Mahfuja Malik University of Boston, USA Md. Anisur Rahman University of Dhaka, Bangladesh. Md. Mamun Habib, PhD, AIUB, Bangladesh Kazi Sohag, UKM, Malaysia Fazeela Jameel Ahsan, PhD University of Colombo, Sri Lanka Rajeshkumar U. Sambhe, PhD Jawaharalal Darda Institute of E&T, India.

VVoolluummee 0033,, 22001144

IIssssuuee 0011

CCoorrppoorraattee LLeeaaddeerrsshhiipp SSttyyllee iinn BBaannggllaaddeesshh

IIJJAARR--BBAAEE,, 0033((0011)):: 0022--1155..

MMaarrzzaann AAzzmmaarryy,, MMdd.. AAnnaammuull HHooqquuee,, FFaarriiddaa YYaassmmeeeenn

EEmmppiirriiccaall SSttuuddyy oonn TTeeaa EExxppoorrtt CCoommppeettiittiivvee iinn SSrrii

LLaannkkaa:: AA PPaarrttiiaall LLeeaasstt SSqquuaarree SSttrruuccttuurraall EEqquuaattiioonn

MMooddeell ((PPLLSS--SSEEMM))

IIJJAARR--BBAAEE,, 0033((0011)):: 1177--3300..

PP.. JJ.. KKuummaarraassiinngghhee,, KK.. MM..VV.. SSaacchhiittrraa

IImmppaacctt ooff AASSEEAANN FFrreeee TTrraaddee AAggrreeeemmeenntt ((AAFFTTAA)) oonn

AAggrriiffoooodd TTrraaddee CCrreeaattiioonn aanndd TTrraaddee DDiivveerrssiioonn

IIJJAARR--BBAAEE,, 0033((0011)):: 3322--4422..

HHeerraatthh HH.. MM.. SS.. PP..,, CCooaa LLiiaanngg,, CChheenn YYoonnggbbiinngg

BBaannggllaaddeesshh RReeaaddyymmaaddee GGaarrmmeenntt EExxppoorrtt GGrroowwtthh:: AA

CCoonnssttaanntt MMaarrkkeett SShhaarree AAnnaallyyssiiss

IIJJAARR--BBAAEE,, 0033((0011)):: 4444--5533..

MMdd.. SShhaaffiiqquull IIssllaamm,, QQuu QQiinngg LLiiaanngg

RRoollee ooff TTrraaiinniinngg aanndd DDeevveellooppmmeenntt iinn tthhee PPeerrffoorrmmaannccee

ooff HHootteell IInndduussttrriieess,, CChheennnnaaii..

IIJJAARR--BBAAEE,, 0022((0066)):: 5555--6633..

DDeenniiss AAmmrriitthhaarraajj,, VVaannaatthhii VVeemmbbeerr

CCaallll ffoorr PPaappeerr VVooll.. 0033,, IIssssuuee 0022,, 22001144..

IIJJAARR--BBAAEE

Herath H. M. P. S et al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

2

IImmppaacctt ooff AASSEEAANN FFrreeee TTrraaddee AAggrreeeemmeenntt ((AAFFTTAA)) oonn AAggrriiffoooodd TTrraaddee CCrreeaattiioonn aanndd TTrraaddee DDiivveerrssiioonn

Herath H.M.S.P.1, Cao Liang2, Chen Yongbing3

1PhD Research Scholar, School of Business Administration, Zhongnan University of Economics and Law, Wuhan, P.R. China, E mail:

shan_wayamba@yahoo.com 2Professor, School of Business Administration, Zhongnan University of Economics and Law, Wuhan, P.R. China, E- mail:

frankcao@126.com 3Associate Professor, School of Business Administration, Zhongnan University of Economics and Law, Wuhan, P.R. China, E-mail:

yongbingchen@163.com

Article History ABSTRACT

Received: 12-03-2014 Accepted: 26-03-2014 Available online: 08-04-2014

Keywords: Regional Trade Agreements, Trade Liberalization, Agrifood Commodities, Trade Creation, Trade Diversion.

JEL Classification: F13, F14, F15, F53.

Discriminatory trade liberalization in agricultural trade is becoming a new trend in the second wave of regional trade agreements. Interdependence of agricultural development and international trade is a key stimulus for the recent developments of trade liberalization in agricultural sector. In developing countries, agricultural sector is still the most dominant sector and contributes a larger portion to national income and employment. Majority of the people of these countries have employed in farming and agricultural related works. As a result, the standard of living of these people greatly depends on the development of agricultural sector. Poverty and hunger which are the cause and consequence of food insecurity is linked with agricultural sector of those economies. The main objective of this study is to capture trade benefits of ASEAN free trade agreement (AFTA) on agrifood trade. The study employs gravity model to estimate impacts of AFTA on agrifood trade and investigate trade effect of AFTA on eight agrifood commodities individually and on the whole. Findings of the study support the statement that joint membership has significantly enhanced agrifood trade among member countries of AFTA. Also, trade creation in agrifood trade is found with the formation of AFTA. Further, study concludes that joint membership has created favorable environment to increase trade among members in cereals, drinks, edible foods, oilseeds, red meat and sugar trade while making negative impacts on fruits and vegetable trade. Trade creation is found in cereal, drinks, edible foods, fruits, vegetables and sugar with AFTA. Oil seeds and red meat found to be trade diverted by AFTA.

Citation: Herath et al., (2014). Impact of ASEAN Free Trade Agreement (AFTA) on Agrifood Trade Creation and Trade Diversion,, IJAR-BAE, 03(01): 32 – 42.

Copyright: @2014 Herath H. M. S. P. This is an open access article distributed according to the terms of the Creative Common Attribution (CCC) 3.0 License under PKP (Public Knowledge Project) of Simon Fraser University, Canada.

1.0 Introduction Regional Trade Agreements (RTAs) have become a well popular element of global trade environment. Failure of efforts to move for broader trade liberalization is one of the main reasons for the proliferation of RTAs over the past few decades (Lambert et al, 2009). According to the World Trade Organization (WTO), as of 31st of January 2014, 583 notifications of RTAs have been received to WTO/GATT. It is reported that 377 are in force. Further, all most all WTO’s member countries have reported participation in one or more RTAs while some countries are party to twenty or more.

IJAR-BAE Vol. 03. Issue 01. Article No. 03

Full-length Original Open Access Research Paper

www.setscholars.org/index.php/ijarbae

International Journal of Applied Research in Business Administration and Economics (2014), 03(01): 32–42.

Herath H. M. S.P. et al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

3

Obtaining trade benefits from improved resource allocation and higher competition, increasing multilateral bargaining power in international forums and attracting improved technology and investments are some basic economic motives behind the rapid growing trend of RTAs. With complete elimination or reduction of tariffs and other non tariff trade barriers within a trade bloc, member countries can specialize on production lines which have comparative advantage within the region. As a result, member countries can allocate scarce resources more efficiently in production lines with higher competition. RTAs are also a vital avenue for smaller countries to increase bargaining power working collectively with their member countries of trade blocs. Low level of savings and investments and lack of technological improvements are some major reasons for low economic performance of developing countries. This has led people of developing countries to get entangled in vicious circle of poverty. The literature on international trade has provided evidence for moving developing countries out of the poverty circle taking the advantage of technological spill over and attracting investment from members of trading blocs (Gulati and Narayanan, 2003; Bezuneh and Yiheyis, 2009).

Table 01: Agriculture share of GDP and employment of Asian countries (2011)

Country Agriculture Value Added (%

of GDP) Employment in Agriculture (%

of Total Employment)

Afghanistan 20.83 … Bangladesh 18.29 48.09e Bhutan 15.93 65.4b Brunei 0.64 … Cambodia 36.68 72.2c China 10.04 39.59c India 17.22 51a Indonesia 14.72 38.2a Japan 1.16a 3.7a Korea, Rep. 2.71 6.6 Laos 30.8 85.4h Malaysia 11.87 13.5b Maldives 5.52 11.5d Myanmar 48.35f 62.7g Nepal 31.75 65.69 Pakistan 21.63 44.7c Papua New Guinia 35.88 72.3f Philippines 12.79 35.2b Singapore 0.03 1.1b Sri Lanka 32.6b 12.09 Thailand 12.36 41.5b Viet Nam 22.02 51.7d South Asia 17.77 51.09a

East Asia & Pacific Region 3.31a 36.03c a 2010 b- 2009, c- 2008, d- 2006, e- 2005, f- 2000, g – 1998, h-1995 Source: World Development Indicators – United Nations

Although the aforementioned facts are in favor of RTAs, debates have been arisen on trade diversion possibilities with the implementation of RTAs. Discriminatory trade liberalization policies can promote higher cost producers within the member countries of a trade bloc replacing the lower cost producers of non member countries. This creates trade diversion. Though trade diversion increases the welfare level of member countries as a result of increased trade among member countries, it reduces the welfare level of non member countries due to displacement of their efficient producers. On the other hand, discriminatory trade policies can further encourage lower cost producers of member countries of a trade bloc which results trade creation. But this leads to increase the welfare level of both members and non members to a trade bloc. The welfare level of member countries is enhanced as a result of increasing trade within the trade bloc. The welfare level of non member countries is enhanced because of the higher demand for their exports from member countries of trade bloc since trade bloc formation leads to increase real income of its members. With these different views, one school of thoughts views RTAs as stumbling blocks to multilateral free trade since they can reduce global welfare (Bhagwati, 1991; Panagariya, 2000). The other school of thoughts views RTAs as building blocks to multilateral trade which raise global welfare (Summers, 1991 and Ethier, 1998).

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

4

In literature, it is discussed about two waves of RTAs (Mathews, 2003). In the first wave of RTAs, countries have paid attention on forming RTAs to promote industrial good trade with the prospect of accelerating industrialization. However, in the second wave of RTAs, commodity coverage has been widened to the fields of agriculture, services and investment as well. In developing countries of the world, agricultural sector is still the most dominant sector and contributes a larger portion to national income and employment (Table 1). Majority of the people of these countries have been employed in farming and agricultural related works. As a result, the standard of living of these people greatly depends on the development of agricultural sector. Poverty and hunger which are the cause and consequence of food insecurity is linked with agricultural sector of those economies (Bezuneh and Yiheyis, 2009). The primary objective of the study is to capture the trade creation and diversion effects of AFTA on agrifood trade during the study period.

2.0 Literature review The literature review of the study basically explores theoretical and empirical investigations on discriminatory trade liberalization policies and their impacts on agricultural sector. According to the literature review, relatively few articles were found in the field of RTAs and agricultural trade. The study of Sun and Reed (2010) pays special attention on impacts of free trade agreements on agricultural trade creation and trade diversion. Sample size of the study is comprised with 81 countries with sample period from 1993 to 2005. In order to estimate welfare effects, the researchers have used Gravity model in Ordinary Least Square (OLS) method and a Poisson Pseudo Maximum – Likelihood (PPML) estimator. Basically, the study has found that the ASEAN-China RTAs and the EU-15, EU 25, and Southern African Development Community (SADC) agreements have generated large increases in agricultural trade among their members. However, their results shows that trade diversion and creation effects are in varying nature from one trading bloc to the other. Also, their results show that there is significant export and import diversion from the EU-15, but the creation of the SADC increased agricultural exports to third party countries. According to their results, there is only export diversion for NAFTA and have not found any trade creation attributed to this agreement. Trade creation has found only in SADC by increasing imports from third party countries. Hence, their results shows that trade diversion and creation effects are in varying nature from one trading bloc to another. Most importantly, sun and Reed’s study has obtained vital results on static and dynamic results on RTAs. Accordingly they have found export creation effects early period of RTAs but it has been disappeared in later years. This is found in Common Market for Eastern and Southern Africa (COMESA), EU 15, and North American Free Trade Agreement (NAFTA) trading blocks. Researchers have found more diversion effect in dynamic results than in static results among RTAs. In an attempt to investigate the effects of regional trade agreements in agricultural commodities, Jayasinghe and Sarker (2004) like other researchers have applied the Gravity Model to achieve their research objectives. Their study is limited to NAFTA trading agreement with selected six agricultural commodities during the period from 1985 to 2000. According to their major findings, NAFTA has significantly reduced the degree of relative openness to agricultural commodity trade with non members while boosting it with the member countries. However, Jayasinghe and Sarker’s study does not provide information on the extent of trade creation and trade diversion effect for those selected commodities attributable to NAFTA. Koo, Kennedy and Skrinitchenko (2006) have given special attention in their study to selected regional trade agreements and their impacts on trade creation and diversion on agricultural trade. Researchers have used cross sectional data with 131 regional trade arrangements reported to WTO. Koo, Kennedy and Skrinitchenko’s study has used standard gravity model with some other additional independent variables. According to Koo, Kennedy and Skrinitchenko’s (2006) conclusion, RTAs generate positive impacts for both member and non member countries. Their study shows RTAs increase trade volume among member countries through both inter-industry and intra-industry trade. The total trade diverting effects has been derived as a positive value indicating that RTAs do not displace agricultural trade with non member countries. This suggests that RTAs are not harmful for non members though trade benefits for members are higher than nonmembers. A study by Lambert and Mckoy (2008) supports the findings of Koo, Kennedy and Skrinitchenko (2006) and Sun and Reed (2010) that regional preferential

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

5

agreements increase trade creation within the members while trade volume with non members depending on particular regional trade agreement.

3.0 Methodology The data used in this study comes from various secondary sources. The time span for the collected data to employ the gravity model for this study is from 2000 to 2011 and 94 countries are included in the sample. The study use annual data for gravity variables for 12 years. However, the study does not use each year in the analysis in order to simplify the calculations and save computational time. Therefore, the study uses four year intervals of agrifood data for the period from 2000 to 2011. As a results number of observations that included in the regression analysis is reduced from 104,904 observations (94*93*12) to 26226 observations (94*93*3). Bilateral trade flows are collected from 94 countries from the United Nations Commodity Trade Statistics Database (COMTRADE). Data for Gross Domestic Product, population is collected from the World Bank Development Indicators data base (WDI) of the United Nations. Data to be employed in gravity model variables namely language, distance, border adjacency are collected from the Centre for prospective studies and international information in France. In achieving the central objectives of the study, the research model is estimated by employing multiple regression technique. The gravity model is used to capture the impacts of AFTA on agrifood trade with standard gravity variables and RTA dummy variables. Since zero bilateral trade flows are common in agricultural trade flows, Pseudo Poisson Maximum Likelihood Estimate (PPML) is employed as a remedy to cope that zero trade issues. The specification of empirical model is presented below.

3.01 Specification of empirical model Static welfare gains of RTAs can be estimated with gravity model. The gravity model is written in general as in equation 1.

ijtijjtitjtit

D

ijt DISNNYYeX ii 54321)(

……………………………..………(1)

Where Xijt is the export volume from exporting country i to importing country j at time t, Yi and Yj are GDP levels of country i and country j for time t respectively, Ni and Nj are population of country i and country j at time t respectively, DISij is the distance between exporting country i and importing country j, Dis are various dummy variables which are important in determining bilateral trade flows and vit is the error term. Taking the logarithm of variables and including dummy variables considered the equation 1 can be written as in equation 2.

ijtjtitijtij

ijjiijj

ijtitjtitijt

AFTAAFTAAFTACL

CBOLLLLDISLA

LANNYYX

14131211

109876

543210

ln

lnlnlnlnln

lnlnlnlnlnln

....(2)

Where, Yi(j) = Gross domestic production of country i and j Ni(j) = Population of country i and j DISij = Distance between country i and j LAi(j) = Land area of country i and j CBOij = Dummy variable: 1 if country i and j share a common border LLi(j) = Dummy variable: 1 if the country is a land locked Country CLij = Dummy variable: 1 if country i and j use a common language AFTAij = Dummy variable: 1 if country i and j are members of AFTA AFTAi = Dummy variable: 1 if only country i is a member of AFTA but country j is not

Herath H. M. S. P. Set al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

6

AFTAj = Dummy variable: 1 if only country j is a member of AFTA but country i is not

ij = the error term which is equal to

ijvln

The equation 2 can be applied to estimate gravity model if bilateral trade flows are not zero values. Because logarithmic form of the equation make an issue to estimate parameters since log of zero is undefined. Especially zero trade flows can be seen when effects of RTAs need to be estimated on a particular sector. For example, bilateral agricultural trade flows between two countries for a given period may have sizeable number of zero observations in a panel data set. Past literature has applied one of three methods to solve this problem. First method is to delete the zero observations from the data set. Second method is to apply some transformation such as adding a smaller value to those zero observations. Third method is to apply Tobit model without making any change to zero observations. However, Santos and Tenreyro (2009) have shown that these three methods would produce inconsistent coefficient estimates if the constant elasticity functional form is applied. Therefore they propose a Poisson Pseudo Maximum – Likelihood (PPML) method to derive efficient parameters than other methods. Because of these difficulties in applying log-linear model in bilateral trade analysis, importance of Poisson models has been increased. Therefore, empirical model of the present study is specified as in equation 3.

}ln

lnlnlnlnln

lnlnlnlnlnexp{

14131211

109876

543210

ijtjtitijtij

ijjiijj

ijtitjtitijt

AFTAAFTAAFTACL

CBOLLLLDISLA

LANNYYX

……(3)

Impacts of ASEAN free trade agreement of agrifood trade is to be estimated primarily with use of gravity model. In determining the effect of AFTA on intra- and extra – bloc trade, this study adopt the approach developed by Soloaga and Winters (2001). This approach is further used by Lambert McKoy (2008) and Clarete et al (2003). Accordingly, three dummy variables are included in the empirical model to capture the impacts of AFTA on agrifood trade. The coefficient of AFTAij dummy variable (

12 ) measures the

extent to which intra-bloc trade is enhanced by joint membership in AFTA. Coefficient of AFTAi dummy variable (

13 ) captures additional exports of ASEAN member countries to all other countries, excluding

intra-bloc trade captured in the 12 coefficient. The coefficient of AFTAj ( 14 ) captures the extra imports

of ASEAN members received from countries outside the AFTA. According to the specification of Soloaga

and Winters, the rejection of hypothesis that 012 indicates that joint membership in AFTA has

significant effect on trade between exporter and importer. Trade creation is described according to the Soloaga and Winters specification, if within bloc trade is enhanced )0( 12 and trade with non-

members to AFTA also increased )0( 1413 . On the other hand, trade diversion is supported if

within bloc trade is enhanced )0( 12 and trade of non members to AFTA is decreased )0( 1413 .

4.0 Results In estimating gravity model for international trade, zero value observations are a main issue to get reliable estimates. Past literature shows, several methods to deal with zero value observations ranging from simple to advanced techniques. Among those methods most effective technique to get reliable and accurate estimates for regressions is to employ the PPML estimates (Soloaga and Winters, 2001; Lambert and McKoy, 2009; Sun and Reed, 2010). Accordingly present study have employed PPML model to get reliable regression estimates since bilateral agrifood trade flows are comprised with a larger portion of zero values.

The study uses eight agrifood commodities to estimate trade impacts of AFTA. Selected eight agrifood commodities based on the Standard International Trade Classification System (SITCS) are cereals, drinks, fruits, edible foods, oilseeds, sugar, and vegetables. The study employs PPML regression model for each commodity. Finally, the total export of agrifood is calculated by taking sum of bilateral exports of selected eight commodities and regress PPML regression model for each time interval and pooled data.

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32–42

Pag

e 3

7

All PPML parameters are heteroskedastic corrected robust estimates calculated by STATA statistical software version 12. Table 2 summarizes the PPML regression results of gravity model for bilateral agrifood exports. In estimating the impacts of ASEAN free trade agreement on agrifood trade, the exports data for the variable is calculated by taking the sum of the exports of eight agrifood commodities concerned in the study. Regression results are shown for three individual time periods and pooled data. The table presents regression coefficients for standard gravity variables and three dummy variables to represent impacts of preferential trade policies with regard to total agrifood trade. R square for models for three time period and pooled data ranges from 0.48 to 0.62 for agrifood trade. This implies that 48 percent to 62 percent of total variation of agrifood exports is explained by the empirical gravity model of the study. The coefficients of GDP for exporter and importer are always highly significant at one or five percent for all periods and pooled regression. Coefficients of GDP have the expected positive sign for the all regression equations indicating higher income of trading partners increase the bilateral agrifood trade. The coefficients of GDP are always below one and vary from 0.54 to 0.66 for all the time intervals of the study period. The coefficient of Population for exporter and importer are statistically significant at standard levels of significance for only in few occasions. Also, the signs of Population coefficients are not consistent with exporter and importer countries. The land area which is the third variable introduced to the empirical model to measure the country size of exporter and importer is highly statistically significant for exporters all the time. The coefficient of land area is statistically significant at 5 percent level of significance in all the times except one occasion for importer countries. Importantly the signs of the coefficients of land area are as expected with exporters and importers since agrifood production is highly land intensive commodity.

The coefficients of distance variable are highly significant at 1 percent level for all the times and show a negative value for coefficients as expected. Countries are encouraged to trade with countries which are closer to them due least transport cost and are discouraged when they are far away to each other. This indicates the higher possibility to engage in agrifood trade among member countries of AFTA since they are closer than the rest of the countries. The coefficients for land locked dummy variable are highly statistically significant at one percent level of significance for all regression equations for all time periods with regard to agrifood trade. It is expected a negative coefficient for land locked dummy variable for both trading partners. As expected in gravity model, land locked dummy variable share a negative sign for all the regressions for both trading countries. The parameter estimates of contiguity dummy variable are positive and statistically significant at standard level of significance for all regression equations for the study period. This positive sign of coefficients indicates that when countries share a common border, the agrifood trade is much encouraged with those countries than other trading partners.

The coefficient of common language dummy variable is expected to be positively influenced on bilateral trade flows between countries. However, with regard to the agrifood trade the coefficient of common language dummy variable is not statistically significant all the regression equations. However, as expected with the theoretical expectations, the sign of the common language is having a positive sign in the agrifood trade all regression models.

The effects of AFTA on total agrifood trade are described by three AFTA dummies incorporated in the regression models. AFTAij dummy variable captures the influence on agrifood exports by joint membership in AFTA. The coefficient of AFTAij is highly statistically significant and bears positive values for all regression models. This indicates that joint membership of AFTA has significantly enhanced the agrifood trade during the study period. The trade creation or diversion effect is estimated by summing the values of the coefficients of AFTAi and AFTAj. While coefficient of AFTAi measures the additional agrifood exports to ASEAN non members, the coefficient of AFTAj measures additional agrifood imports received by ASEAN members from non member countries. In this context, with regard to agrifood trade, sum of the two coefficients shows statistically significant and positive values for all time periods including the pooled regression model and indicate trade creation in agrifood trade with the formation of AFTA.

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32–42

Table 02: Estimated Gravity Model Coefficients for Agrifood Trade (2000 - 2011)

Independent Variables

2000 - 2003 t Statistic 2004 - 2007 t Statistic 2008 - 2011 t Statistic Pooled t Statistic

lnYi .548 12.10 *** .558 14.52 *** .599 12.41 *** .584 14.66 *** lnYj .681 18.59 *** .670 17.89 *** .625 15.90 *** .657 18.74 *** lnNi .036 0.71 .028 0.57 -.014 0.30 .004 0.10 lnNj .033 0.85 .075 1.66 * .138 2.06 ** .098 1.84 * lnLAi .121 3.19 *** .132 3.46 *** .168 4.51 *** .149 3.97 *** lnLAj -.056 1.58 -.066 2.24 ** -.076 2.54 ** -.0719 2.38 ** lnDIS -.652 8.41 *** -.649 8.62 *** -.634 9.00 *** -.636 8.99 *** LLi -1.04 6.28 *** -.732 4.48 *** -.384 2.21 ** -.578 3.48 *** LLj -.917 7.23 *** -.764 5.57 *** -.663 4.48 *** -.726 5.29 *** CBO .891 3.43 *** .906 3.48 *** .864 3.55 *** .886 3.61 *** CL .225 1.40 .158 0.99 .125 0.84 .148 0.97 AFTAij 1.20 6.34 *** 1.062 5.26 *** 1.061 5.19 *** 1.120 5.90 *** AFTAi .569 4.54 *** .483 3.61 *** .466 3.38 *** .5058 3.95 ***

AFTAj .202 1.18 .153 1.00 .206 1.26 .2159 1.42 Creation/Diversion Effect

0.771 11.99*** 0.636 7.96*** 0.672 7.60*** 0.7218 10.64***

R Squared 0.615 0.5863 0.484 0.520

Observations 8556 8556 8556 25668

Note: 1. 1. The t-statistics are absolute values of t-ratios. Single, double and triple asterisks denote significance of coefficients at the 10 %, 5% and 1 % level, respectively. In case of joint hypothesis test for trade creation and diversion effect, test statistic is Chi Squared value. 2. Each PPML model is tested for overall model significance and statistically significant at any standard significant level and all PPML model estimates are heteroskedastic corrected robust estimates. 3. All variables except dummy variables are in logs and the dependent variable is total exports between country pairs

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32–42

Table 3: Estimated Gravity Model Coefficients for Selected Agrifood Commodities (2000 - 2011)

Independent Variables

Cereal Drinks Edible Foods Fruits Coefficient t Statistic Coefficient t Statistic Coefficient t Statistic Coefficient t Statistic

lnYi .855 8.69 *** .249 5.77 *** .870 20.64 *** .422 10.17 *** lnYj .210 2.42 ** 1.027 22.30 *** .531 18.18 *** .953 17.19 *** lnNi -.287 3.39 *** .4021 10.65 *** -.191 3.08 *** .169 2.67 *** lnNj .383 5.47 *** -.358 8.06 *** .001 0.01 -.184 2.42 ** lnLAi .524 11.79 *** -.096 2.97 *** -.034 1.55 .0171 0.34 lnLAj -.108 1.51 .0105 0.30 .029 0.92 -.047 0.95 lnDIS -.777 6.39 *** -.653 11.86 *** -.774 11.53 *** -.612 7.61 *** LLi .675 2.30 ** -.676 4.41 *** -.638 3.71 *** -1.774 6.28 *** LLj -1.710 7.86 *** -.501 2.83 *** -.599 3.67 *** -.627 3.35 *** CBO .6913 2.04 ** .997 7.60 *** .724 3.19 *** .871 2.99 *** CL -.240 0.87 .176 1.68 * .557 3.88 *** .264 1.54 AFTAij 1.882 4.12 *** 1.301 4.66 *** 1.733 6.34 *** .324 1.53 AFTAi 1.642 5.69 *** .479 3.05 *** 1.261 8.11 *** .301 1.59 AFTAj -.015 0.06 .092 0.49 .552 3.80 *** .508 2.21 ** Creation/Diversion Effect

1.626 17.92*** 0.571 4.25** 1.813 53.61*** 0.809 6.16*

R Squared 0.220 0.512 0.683 0.346

Observations 25668 25668 25668 25668

Notes: 1. The t-statistics are absolute values of t-ratios. Single, double and triple asterisks denote significance of coefficients at the 10 %, 5% and

1 % level, respectively. In case of joint hypothesis test for trade creation and diversion effect, test statistic is Chi Squared value. 2. Each PPML model is tested for overall model significance and statistically significant at any standard significant level and all PPML

model estimates are heteroskedastic corrected robust estimates 3. All variables except dummy variables are in logs and the dependent variable is total exports between country pairs

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32–42

Table 3: Estimated Gravity Model Coefficients for Selected Agrifood Commodities (2000 - 2011) ……...Cont

Independent Variables

Oil Seeds Red Meat Vegetables Sugar

Coefficient t Statistic Coefficient t Statistic Coefficient t Statistic Coefficient t Statistic

lnYi .887 5.24 *** .728 9.20 *** .443 8.03 *** .486 15.44 ***

lnYj .670 10.18 *** .824 9.60 *** .901 8.68 *** .439 7.50 ***

lnNi -.350 2.74 *** -.552 3.70 *** .333 4.34 *** .118 2.98 ***

lnNj .735 4.64 *** -.004 0.06 -.095 0.99 .108 1.20

lnLAi .688 9.41 *** .359 3.86 *** -.031 0.74 .153 3.70 ***

lnLAj -.288 4.25 *** -.160 2.04 ** .017 0.35 .0465 0.70

lnDIS -.142 0.64 -.406 3.16 *** -.950 7.63 *** -.677 8.78 *** LLi 2.176 4.28 *** -1.165 2.27 ** -1.480 4.76 *** -.286 1.26 LLj .001 0.00 -1.651 7.28 *** -.585 2.75 *** -.652 2.93 ***

CBO .865 1.82 * 1.183 3.66 *** .851 1.84 * .971 4.00 *** CL -.725 1.92 * .141 0.42 .0172 0.06 .084 0.50 AFTAij 1.112 2.25 ** -1.523 2.99 *** -.109 0.45 1.722 4.33 ***

AFTAi -2.082 4.24 *** -5.593 13.10 *** -.085 0.23 .537 1.96 ***

AFTAj .157 0.50 .149 0.41 .818 2.85 *** .079 0.25

Creation/Diversion Effect

-1.925 8.75*** -5.444 94.06*** 0.733 2.31 0.617 1.87

R Squared 0.312 0.229 0.229 0.617 Observations 25668 25668 25668 25668

Notes: 1. The t-statistics are absolute values of t-ratios. Single, double and triple asterisks denote significance of coefficients at the 10 %, 5% and

1 % level, respectively. In case of joint hypothesis test for trade creation and diversion effect, test statistic is Chi Squared value. 2. Each PPML model is tested for overall model significance and statistically significant at any standard significant level and all PPML

model estimates are heteroskedastic corrected robust estimates 3. All variables except dummy variables are in logs and the dependent variable is total exports between country pairs

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32-42

www.setscholars.org/index.php/ijarbae

Pag

e 4

0

Pag

e 4

0

Table 3 summarizes the estimated gravity model results for selected eight agrifood commodities. The table does show only regression results for pooled regression to save the space. Overall, coefficients of GDP is highly significant for all the commodities and both exporters and importers. The positive signs of the GDP coefficients indicate that higher income encourage bilateral trade flows in each commodity. Distance between trading countries is assumed to be influential factor in determining commodity trade among countries in gravity model. The study found that for each commodity trade, distance is a statistically significant determinant on bilateral trade and has negatively influenced on trade flows. One of the key determinants of bilateral trade, previous studies showed that land locked countries are prone to be less traded with rest of the world due to absence of sea transport facility. The study found that the coefficient of land locked dummy variable is statistically significant and shows negative sign for all commodities except oil seeds and cereal exporter countries. The coefficients of contiguity shows statistically significant factor in determining bilateral trade of all selected commodities and positively influence on trade. Influence of Joint membership in AFTA on individual agrifood commodity trade is estimated by the coefficient of AFTAij. The coefficient of AFTAij is statistically significant and shows a positive sign with regard to trade in cereals, drinks, edible foods and sugar trade indicating that joint membership has increased trade of those commodities. However, joint membership has shown reverse effect on red meat trade in AFTA during the study period. Membership in AFTA demonstrates trade creation when the summed parameter of AFTAi and AFTAj is positive. Trade creation is thus supported in cereals, drinks, edible foods and fruits trade in AFTA during the study period. However, trade diversion is suspected in oil seeds and red meat trade of AFTA.

5.0 Discussion and policy implications The study used three variables in the gravity model to capture the size of the country which can significantly influence on bilateral trade between two countries. Among those three, it is found that income level of countries is most significant factor in determining trade flows. Land area was also included as an important factor which positively influence on agrifood exports and negatively on agrifood imports. This variable was included in the empirical model of the study since agrifood production is comparatively based on land intensive production technique. The coefficient of land area found to be significant and has expected signs for both exporter and importer in most cases. However, we expect that this coefficient would be more consistent if it is used the agricultural irrigated land area since yield of agrifood commodities is more dependent on the quality of agricultural land. However, due to the lack of data on the ideal variable for most of the sample countries, agricultural land was taken as an approximate variable. In determining trade bloc impact on agrifood trade, this study employed criteria of Soloaga and Winters. This approach is further used by Lambert McKoy (2008) and Clarete et al. (2003). The results of the study are more consistent with those previous researches.

6.0 Conclusion This study is carried out to empirically investigate the impacts of AFTA on agricultural food. With that objective, research was carried out to disentangle the impacts of AFTA on agrifood trade in total and individually. In estimating effects of AFTA on agrifood trade, the standard gravity model was employed with an extension to capture trade creation and diversion effects of AFTA. The study investigated trade effect of AFTA on eight agrifood commodities individually and as a whole. To disentangle the trade effect of AFTA on agrifood trade, the study accounted agrifood trade as the total exports of selected eight agrifood commodities. General conclusion regarding effects on agrifood trade is that joint membership has significantly enhanced the agrifood trade among member countries while creating agrifood trade with the formation of AFTA. Trade impacts on individual agrifood commodities shows that joint membership has created favorable environment to increase trade among members in cereals, drinks, edible foods , oilseeds, red meat and sugar products while making negative impact on fruits and vegetable trade. Trade creation is found in cereal, drinks, edible foods, fruits, vegetables and sugar with AFTA. Oil seeds and red meat found to be trade diverted by AFTA.

Herath H. M. S.P. et al. / IJAR-BAE (2014), 03(01): 32-42

www.setscholars.org/index.php/ijarbae

Pag

e 4

1

Pag

e 4

1

Acknowledgement This article is funded by the Research and Innovation Project of Graduate International Students of Zhongnan University of Economics and Law, Wuhan, P.R. China.

References Bezuneh, M., & Yiheyis, Z., 2009. Has trade liberalization improved food availability in developing

countries? An Empirical Analysis. International Association of Agricultural Economists Conference, Beijing, China.

Bhagwati, J.N. 1978. Anatomy and consequences of exchange control regimes. Cambridge, Massachusetts: Ballinger Pub.Co.

Bhagwati, J. N., 1991. The world trading system at risk. NJ: Princeton University Press. Clarete, R., Edmonds, C. & Wallack, J.S. (2003) Asian regionalism and its effects on trade n the 1980s and

1990s. Journal of Asian Economics, 14:91-129. Ethier, W., 1998. The new regionalism. The Economic Journal, 108: 1149 -1161. Gulati, A., & Narayanan, S., 2003. Rice trade liberalization and poverty. Economic and Political Weekly,

38(1):45-51. Jayasinghe, S., & Sarker, R., 2004. Effects of regional trade agreements on trade in agrifood products:

evidence from gravity modeling using disaggregated data. Center for Agricultural and Rural Development, Lowa State University, Working Paper 04-WP374.

Koo, W.W., Kennedy P.L., & Skripnitchenko, A., 2006. Regional preferential trade agreements: trade creation and diversion effects. Review of Agricultural Econoomics, 28(3):408-415.

Lambert, D., & McKoy S., 2009. Trade creation and diversion effects of preferential trade associations on agricultural and food trade. Journal of Agricultural Economics, 60(1):17-39.

Matthews, A., 2003. Regional integration and food security in developing countries. [Accessed 22nd January 2013] available from World Wide Web: http://www.fao.org/docrep/004/y4793e/y4793e00.html.

Panagariya, A., 2000. Preferential trade liberalization: traditional theory and new developments. Journal of Economic Literature, 38:287 -331.

Santos Silva, J.M.S., & Tenreyro, S., 2006. The log of gravity. Review of Economics and Statistics, 88(4): 641-658.

Soloaga, I., & Winters, L.A., 2001. Regionalism in the nineties: What effect on trade? North American Journal of Economics and Finance, 12:1-29.

Summers, L., 1991. Regionalism and world trading system: In policy implications of trade and currency zones: A summary of the bank’s 1991 symposium, Kansas: Federal Reserve Bank of Kansas City.

Sun, L., & Reed, M. R., 2010. Impacts of free trade agreements on agricultural trade creation and trade diversion. American Journal of Agricultural Economics, 92(5):1351-1363.

WTO, Regional trade agreements, [Accessed 2nd March 2014] available from World Wide Web: http://www.wto.org/english/tratop_e/region_e/region_e.htm

Herath H. M. S. P. et al. / IJAR-BAE (2014), 03(01): 32-42

www.setscholars.org/index.php/ijarbae

Pag

e 4

2

Pag

e 4

2

IJAR-BAE

Currency Indexed and Hosted in following Digital Libraries

Upcomming Indexing for IJAR-BAE