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ANALYSES
216
Determinants of Bilateral
Agricultural Trade of SAARC
Region: a Gravity Model
Approach
1 Department of Economics, University of Kashmir, Hazratbal, Srinagar, 190006, India.
2
Department of Economics, University of Kashmir, Hazratbal, Srinagar, 190006, India.
3
Department of Economics, University of Kashmir, Hazratbal, Srinagar, 190006, India. Corresponding author: e-mail:
sarfraz.eco@gmail.com.
Abstract
e South Asian Association for Regional Cooperation (SAARC) region is an important player in the world
agriculture trade. ey have vast potential to strengthen their position in global agricultural trade due to
theregion's opportunities to increase agricultural production combined with growing global demand.
To discover the SAARC potential of agricultural trade patterns, the present paper examines the determinants
of bilateral agricultural exports from 2000 to 2019. e gravity model was estimated by employing the Poisson
Pseudo Maximum Likelihood (PMML) technique, including zero trade ows for panel data. e results
conrm the positive and signicant impact of exporter gross domestic product (GDP), importer GDP, Broder,
common language, South Asian Free Trade Area (SAFTA), and India-Sri Lanka Free Trade Agreement (ISFTA)
on bilateral agricultural trade inthe SAARC region. On the other hand, distance and development levels
signicantly negatively impact bilateral agricultural trade. Lastly, the study showed an insignicant impact
of the bilateral exchange rate.
Keywords
SAARC, agricultural trade, SAFTA, gravity model, ISFT
JEL code
C23, F15, F43
INTRODUCTION
Globalisation has evolved dramatically aer World War Second. is trend occurred due to the increased
international trade and investment activities (Urata, 2002). is was evident becausethe global trade growth
outpaced the global output growth (Feenstra, 1998). In 2018, the international merchandise trade climbed
Tariq Ahad Nengroo1 | University of Kashmir, Srinagar, India
Imtiyaz Ahmad Shah2 | University of Kashmir, Srinagar, India
Md. Sarafraz Equbal3 | University of Kashmir, Srinagar, India
DOI
https://doi.org/10.54694/stat.2022.40
Received 7.9.2022 (revision received 24.10.2022), Accepted (reviewed) 28.11.2022, Published 16.6.2023
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by 3.0% and the global gross domestic product (GDP) increased by 2.9%. Such a pace of globalisation
has gradually increased the trend toward regionalism. Countries worldwide developed regional agreements,
whether bilateral, regional, or multinational, to speed up trade and, therefore, their integration into
the global economy (Joshi, 2010). Over the past several decades, both developed and developing countries
have signicantly lowered trade restrictions, bringing a paradigm shi in the international trade patterns.
Such a shi in trade patterns has been attributed to how globalisation gained momentum and its impact
on speeding up regionalism.
Regionalism is referred to when a group of countries form regional blocks on a regional basis
(Carbaugh, 2006). It is being found that both developed and developing countries have shied towards
and formed dierent regional trading blocs to meet their developmental agendas. Asa result, regional
trading agreements worldwide have increased rapidly. Keeping in view the success of major regional
trading blocs like encouraged the Indian sub-continent to form a regional trading block, namely
e South Asian Association for Regional Cooperation (SAARC), an association of 8 South Asian nations,
of which Indiais one of the primary founding members apart from Sri Lanka, Bhutan, Bangladesh,
Pakistan, Nepal, Maldives and Afghanistan which joined later in 2008. To promote trade and commerce
within the region, these countries entered into a preferential trade agreement SAPTA (South Asian
Preferential Trade Agreement) in 1992, which fully came into eect in 1993 as the rst level of trade
arrangement among SAARC members. Moving ahead for further integration, SAPTA was transformed
into a South Asian Free Trade Area (SAFTA) in 2006. One of the most common features of south
Asian countries is that they are primarily agrarian in nature. However, over the years,the contribution
of the agriculture sector to the GDPs of these countries has declined, but still, a good chunk
of the population derives employment from the agriculture sector. Regarding regional trading agreements,
the agriculture sector has not been covered under these trading agreements till 2000. However, since
the Doha Round of Development in 2001, agriculture has become part of many foreign trade agreements
(FTA) negotiations.
Unlike developed countries, the agricultural sector exceeds most of the economic activity
in developing countries. anks to their structural nature, agriculture contributes to economic development
as a continuous process of improving the standard of living of the population. In fact, agriculture
is the rst economic activity without which life cannot subsist. It is also responsible for the provision
of food and clothing for the population of other non-agricultural economies. Likewise, it’s capable
of supplying a large part of the production materials, such as capital, raw materials and human material
for other economic sectors. Many economic indicators and criteria are used to judge the eciency
of the performance of the agricultural sector, which mainly depends on the value of GDP, the volume
of production, investments and exports. In this context, agricultural exports are regarded as one
of the main means of economic growth and sustainable development of the countries. ey are seen
as a crucial means of acquiring currency, stimulating agricultural investment, increasing the employment
rate, reducing the number of the unemployed and eliminating the poverty rate.
So this backdrop, the present study will try to find out the impact of various determinantson
the regional agriculture trade ow among SAARC. Such an analysis will benet policy-related issues to
promote agriculture trade in this region. To the authors’ best knowledge, this research is the rst attempt
to employ the gravity model in determining the major determinants of bilateral agricultural exports
of the SAARC region using the PPML econometric technique. For this purpose, a well-known gravity
methodology will be employed. Unlike supply-side models such as the Ricardian and Heckscher-Ohlin, the
gravity model of trade considers both supply and demand factors (GDP and population) as well as trade
resistance factors (geographical distance, trade policies, uncertainty, and various bottlenecks) and trade
preference factors (preferential trade agreements, monetary unions, political blocs, common language,
and common borders) (Bacchetta et al., 2012; Benedictis and Vicarelli, 2004). erefore, the research
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218
will help to identify the major determinants of bilateral agricultural exports of the SAARC region,which
will allow this research to contribute to the existing literature with the necessary information during the
decision-making processes of both public and private policymakers.
e present study has two-fold novel contributions to the analysis of bilateral agricultural exports
of the SAARC region. e rst is due to methodological concerns. In contrast to the previous studies
of SAARC agricultural trade data, such as Dembatapitiya (2015), we apply Poisson Pseudo Maximum
Likelihood (PPML) to generate our parameter estimates rather than the more conventional Ordinary
Least Squares (OLS) technique. PPML has shown fewer bias estimators than conventional OLSas
an estimation method of choice. e second novel contribution of the present research is the inclusion
of both SAFTA and the India-Sri LankaFree TradeAgreement (ISFTA) trade agreements as control
variables on agricultural trade of the SAARC region.
e paper is structured as follows: current section introduces background, importance and novelty
of the theme; rst section presents the literature review; second section discusses methodological
aspects of the article like the identication of variables through the gravity model, data and data sources
and the PPML econometric technique for the gravity model; third section presents empirical results;
and the nal section concludes the paper with policy implications and future research gaps.
1 LITERATURE REVIEW
This part highlights the review of previous studies that have used the gravity model to examine
the fundamental determinants of trade and its potential. International trade ows were studied using
the gravity equation for the rst time in 1962 by Nobel Prize-winning economist Jan Tinbergen. Using
the data covering 18 countries in his rst study in 1958, he found that the trade ow between the countries
was proportionate to the product of an index of their economic sizes, and the factor of proportionality
was dependent on the measures of trade resistance between them. Anderson (1979) was the rst who
tried to provide the theoretical underpinnings to the gravity equation based on the Armington (1969)
assumption. He argued that the nation of origin differentiates goods, consumers have established
preferences for all the dierentiated goods assumption, and consumers have established preferences for
all of the dierentiated items. Later it was found that a multitude of international trade theories, such
as the Ricardian model, Heckscher–Ohlin model, and new theories of economies of scale, monopolistic
competition, and intra-industry trade, can be used to derive the gravitation equationBergstrand (1985),
Helpmann and Krugman (1985), Helpmann (1981), Alan (1995), and Anderson and Van Win coop (2003).
Srinivasan (1994) employed the gravity model to examine the impacts of SAPTA, and they found
that smaller countries have more chances to get beneted from SAPTA. Rajapakse and Arunatilleke
(1997), while examining the trade between Sri Lanka and its major trading partners through
the gravity model approach, found that the abolition of restrictive trade policies has the ability to boost
trade potential between Sri Lanka and its trading partners. Examining the trade among south Asian
countries, Samaratunga et al. (2001), employing the gravity model, came up with the result that there
is a potential for southAsian exports to expand and increase their volume. For the period 1996–2002,
Shukar and Hassan (2001) used both panel and cross-sectional data to assess trade creation and trade
diversion eects under the current SAFTA system by applying the gravity model to the panel data.
According to a study, there was no indication of trade diversion between SAARC countries and other
countries. Rahman, Shadat and Das (2006) studied additional regional trading blocs with an extended
gravity model. SAPTA was proven to have considerable intra-regional trade creation, according to their
ndings. In contrast, Rodrguez-Delgado (2007) modied the gravity equation and found SAFTA’s trade
liberalisation programme to have limited eects on regional trade ows. Dayal et al. (2008) found that
estimated trade is much higher than actual trade, indicating a huge potential for intra-regional trading
in South Asia.
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Using an augmented gravity model with trade costs as an additional variable, Banik and Gilbert
(2008) investigated whether the presence of trade costs influences trade flows in the South Asian
region. According to the report, South Asia has greater trade costs because of a lack of physical and
service-related infrastructure, government regulation, port ineciency, and corrupt customs ocials.
Jeevika’s (2009) studies have shown that South Asia had only a moderate success in liberalising regional
trade because of the leover trade barriers, the lack of complementary production and consumption,
and political friction between countries. This was demonstrated through sectoral gravity models
of exports of ve product categories related to food and agriculture: livestock, vegetables, processed
foods, and manufactured goods. It was concluded in Moinuddin’s (2013) study that lowering
taris and non-tari obstacles will have a positive impact on intra-bloc trade among South Asian
economies.
Recently Kahaer and Buwajian (2020) used the gravity approach to determine the impact of international
logisticsfrom the 22 countries of western and central Asia on the agricultural export growth of China
during 2012–2019. e results of the study reveal that the population, GDP, mutual memberhood
of the member countries and performance of international logistics signicantly impacted the country’s
agricultural export growth. Similarly, González et al. (2018) conrm that population, GDP per capita,
real exchange rate, and free trade agreements positively impact Nicaragua’s agricultural exports, while
distance signicantly negative impacts agricultural exports. Fiankor, Haase and Brümmer (2021) applied
the translog gravity model to determine the heterogenous eects of food standards on agricultural
trade. The study confirms the negative impact of importer standards on agricultural trade flow.
By using the two-step system generalised moment methods (two-step sys GMM) on agricultural exports
on agricultural exports, Eshetu and Mehari (2020) conrms the GDP, road connectivity, exchange rate,
domestic savings, tax revenue, and lagged agricultural exports as a major determinant of agricultural
exports of Ethiopia. Bakari and Zidi (2021) found the positive impact of GDP in the agricultural sector,
bank loans to the agricultural sector, agricultural imports, and imports of agricultural machinery
on agricultural exports in the long run of Tunisia agricultural exports. On the other hand,the exploitation
of agricultural land and domestic investment tothe agricultural sector harms Tunisia’s agricultural
exports.
2 METHODOLOGY AND DATA
e methodology of the paper is as; rst, the determinants of bilateral agriculture of the SAARC region
are identied using the gravity model of agricultural trade; second, the impact of identied determinants
on agricultural exports is estimated using the PPML econometric technique and lastly, the methodology
provides the data sources of all interesting variables.
To account for a comprehensive analysis and to examine the determinants of agricultural exports
within the south Asian block. We have employed a widely celebrated gravity model technique derived
from Newton’s universal law of gravitation, which states that trade between two countries is directly related
to the GDP of two countries and inversely determined by the distance between the countries. e model
was rst used by Tinbergen (1962), Pöyhönen (1963), and Pulliainen (1963) to explain bilateral trade.
e theoretical model of gravity model is borrowed from Newton’s law of gravity which assumes that
the attraction between two bodies in the universe is directly proportional to their masses and inversely
related to the distance between them. In international trade economics, the gravity model suggests that
trade between two countries is proportional to their national incomes and inversely related to the distance
between them. erefore, the gravity model predicts that economically rich and closer countries trade
more than developing countries. Braha et al. (2017) suggest that the gravity model eciently explains
a large proportion of international trade. Following Braha et al. (2017), the gravity equation of international
trade is expressed as follows:
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220
(1)
where Xij,t is the dependent variable representing the bilateral trade ow from country i to country j.
The independent variables includes the GDPi (economic size of the exporter country), GDPj (economic
size of the importer country), and Disij (distance between the exporting and importing countries).
Traditionally, the gravity model is estimated through log-linearisation and is estimated through linear
estimators like OLS. Taking the logarithm on both sides of Formula (1) as:
12 3
,
ijt it ij ij ijt
lnXlnGDP lnGDPlnDis
(2)
where β1, β2 and β3 are elasticity coecients showing the impact of export’s GDP. Importer’s GDP
and bilateral distance on trade ow between countries.
However, Formula (2) is subject to two econometric issues which have received attention in recent
methodological development. First, the original gravity model omits the multilateral resistance terms
which are correlated to trade costs. Ignoring the multilateral trade costs could lead to biased estimators.
To capture the trade costs a number of control variables are used as a proxy to capture the trade costs that
are specic to our theme, including the binary variable, the border between trading countries, a binary
variable of common language and others. e augmented gravity model with multilateral resistance
variables is shown in Formula (3) as:
12 34 56
78910
.
ijtitijijiji ij ij ij ij ijt
lnXlnGDP lnGDPlnDis lnExclnGDPClnGDPCj LngBor SAFTAISFT
12 34 5678
910
.
ijtitijijiji ij ij ij ij ijt
lnXlnGDP lnGDPlnDis lnExclnGDPClnGDPCj LngBor SAFTAISFT
(3)
e second issue is the treatment of zero trade ow years between the trading countries. Ignoring
the zero trade years may lead to another source of bias if ignored. The log-linearised model leads
to a truncated dependent variable due to the non-existence of a natural log of zero observations.
To address this issue, we apply Santos-Silva and Tenreyro’s (2006) so-called PPML estimator. Not only
does it capture the useful information contained in zero trade ows, but the PPML estimation technique
is also a suitable method in the presence of heteroscedasticity. With a solid theoretical foundation
and substantial empirical evidence, the PPML approach has been regarded as one of the most eective
techniques for calculating gravity equations. e PPML equation of the augmented gravity model is as:
12 34 56
78910
,
ijt it ij ij ij iijijijijijt
X lnGDPlnGDP lnDislnExc lnGDPClnGDPCj LngBor SAFTAISFT
12 34 5678
910
,
ijt it ij ij ij iijijijijijt
X lnGDPlnGDP lnDislnExc lnGDPClnGDPCj LngBor SAFTAISFT
(4)
where GDPCi is the income-eecting variable measured by the GDP per capita of the exporting country,
GDPCj is the income-eecting variable of importing country. Excij, presents the bilateral exchange rate.
Lngij, is a dummy variable showing whether exporting and importing country has a common primary
language. e dummy variable, Borij, if countries i and j share a common land border. SAFTAij and ISFTij,
are free trade agreements.
e fundamental premise of the model is that trade ow between countries increases with the increase
in their GDPs and decreases with the distance between countries. So the GDP of both the countries that
is exporting and importing countries is expected to have a positive impact on trade ows.GDP is taken
as a proxy for income. e higher the income level, the higher thecountry’s productive capacity, which
means a greater amount of goods available for export. So, the coecient of exporter GDP is expected
to have a positive sign which means an increase in the goods available for exports. In the same way,a higher
level of GDP of an importing country means a higher level of income, which means a higher absorptive
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capacity for importing country. e coecient of importer GDP is also expected to have a positive sign
(Harris and Matyas, 1998; Rahman, 2005; Jayasinge and Sarker, 2008). e distance, which is expressed
in km, is the distance between two economic centres/capitals.
e distance, which is a proxy for transportation and other transaction costs,negatively impacts trade
ows between countries. erefore, it is expected to have a negative sign (Zorzoso and Lehman, 2000;
Abraham and Hove, 2005; Rahman, 2005). e border, which we take as a dummy variable between
country pairs. Countries which share borders carry the value1 and 0 otherwise. Common border reduces
transaction costs, increasing trade volumes and is expected to have a positive sign. Language is another
dummy variable expected to inuence trade ows positively, therefore its coecients are expected
to have a positive sign. It takes the value 1 if counties have the same language and 0. It captures cultural
characteristics and similarities/dierences between two countries (Zorzoso and Lehman, 2000; Abraham
and Hove, 2005; Rahman, 2005).
e coecient of the per capita dierence variable can be both positive or negative. e positive sign
of this variable indicates that the trade pattern follows the H-O theory, which says countries that are
similar trade less than those that are not. e negative coecient of this variable indicates that the trade
pattern follows the Linder demand hypothesis, which postulates that countries which are similar tend
to trade more than otherwise.
e agricultural gravity trade model is frequently augmented by using the impact of the exchange rate.
In our study annual exchange rate is determined by the export’s currency units per unit of the importing
currency. An increase in the exchange rate would be expected to devaluate the exporter currency,
and exports would become cheaper. erefore, the expected sign of the exchange rate is positive (Hatab
et al., 2010).
e eects of trade liberalisation on agricultural exports are observed by using dummy variables
of free trade agreements (FTAs). We incorporated two dummies, SAFTA and ISFTA, to observe
the impact of free trade agreements in the SAARC region. SAFTA is a 2006 regional trade agreement among
SAARC countries, and ISFT is a bilateral free trade agreement between India and Sri Lanka. Economists
have debated the impacts of FTAs through trade creation and diversion eects. Trade diversion leads
the ineciency due to trade preferences for higher costs of member countries with FTA. On the other
hand, the trade creation benets of FTAs depend the initial structural conditions of member countries.
In order to estimate the panel gravity equation, the study uses the bilateral agricultural trade
of seven SAARC countries, namely Bangladesh, Nepal, Sri Lanka, India, Pakistan, Bhutan and Maldives,
for the time 2000–2019. Data on agriculture trade ows were extracted from the UNCOMTRADE
database4 at SITC code (0–12 and 4–27–28 dene the agriculture sector under such classication,
data in annual terms). Data regarding dummy variables like a common language, common border
and distance are obtained from the CEPPI database.5 SAFTA and SFT are self-constructed dummy
variables. e data of macroeconomic variables of GDP, GDP per capita and exchange rate of importing
and exporting countries are obtained from the World Bank database.6 e description of variables
is presented in Table 1.
3 EMPIRICAL RESULTS
In the presence of zero trade ows and heteroscedastic, OLS estimates will be inconsistent andbiased.
To avoid such inconsistency and bias, the study estimates the gravity equation using the PPML
and the OLS techniques. So here, we will discuss the results of the PPML estimation technique. We have
4 <https://comtrade.un.org>.
5 <http://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele.asp>.
6 <https://data.worldbank.org>.
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Dependent Variable Description Expected sign
lnXit Log of bilateral agricultural exports
Independent variable
lnGDPit Log of the gross domestic product of reporter country i+
lnGDPjt Log of the gross domestic product of partner c country ij +
lnGDPCit Log of GDP per capita of reporter country i+/–
lnGDPjt Log of GDP per capita of partner country j +/–
lnDisij Log of Distance between country i and j–
lnExcij Log of relative exchange of ratio of export currency and import currency –
Borij Dummy variable = 1 if country i and j have a common border; 0 otherwise +
Lngij Dummy variable = 1 if country i and j have a common ethnic language; 0 otherwise +
ISFTij Dummy variable = 1 if country i and j are members of ISFT; 0 otherwise +
SAFTAij Dummy variable = 1 for the year 2006; 0 otherwise +/–
Source: Authors´ calculation
estimated two PPML models. First, we include only basic fundamental variables in the model second
of PPML, we introduce trade agreements. SAFTA is a regional trade agreement, and ISFT is a bilateral
free trade agreement between Sri Lanka and India.
Table 1 Description of variables
Table 2 Estimates of the Gravity model
OLS
(1)
PPML
(1)
PPML
(2)
lnGDPit
0.582***
(0.259)
0.683***
(0.0333)
0.534***
(0.0342)
lnGDPjt
0.444
(0.258)
0.619***
(0.0339)
0.473***
(0.0378)
lnDisij
0.460
(0.887)
0.259*
(0.129)
0.230*
(0.145)
lnExcij
0.576
(0.701)
0.0381
(0.0929)
0.0489
(0.0727)
lnGDPCit
0.0659
(0.675)
0.115
(0.0826)
0.200**
(0.0757)
lnGDPCjt
2.737***
(0.677)
0.271**
(0.0926)
–0.360***
(0.0836)
Borij
1.360
(1.792)
0.363*
(0.142)
1.368***
(0.136)
Lngij
1.360
(2.871)
0.363*
(0.156)
1.368***
(0.156)
SAFTAij
1.380*
(0.666)
0.553***
(0.142)
ISFTij
0.787
(.696)
1.650***
(0.146)
Cons –23.38*
(10.01)
–9.894***
(1.592)
–2.558
(1.816)
N 280 280 280
R-sq 0.713 0.643 0.752
Notes: Standard errors in parentheses, * p<0.05, ** p<0.01, *** p<0.001.
Source: Authors´ calculation
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e PPML (2) conrms that the results of the baseline gravity model are persistent with the theoretical
framework. e coecient of GDP of exporter and importer countries are positive and signicant.
e coecient of the exporter GDP suggests that a 1 percent increase in GDP will increase the agricultural
trade ow by 0.53 per cent. Results also suggest that trade ow will increase with the increase in importing
country’s purchasing power. Importer country GDP coecientsuggests that the increase 1 percent
of the trade ow will improve by 0.47 per cent. Results indicate that the SAARC region’s agricultural
exports proportionally with an increase in the sizeof member countries. As expected, our results conrm
that distance signicantly negatively impacts agricultural exports. As expected, our results conrm
the negative and signicant impact of distance on agricultural exports. Increasing distance between
the capital city of SAARC member countries proxies higher transport costs.
In addition to the classical variables, we adjust the basic model with the variables of GDP per capita
as a proxy for the level of development and relative exchange rate. However, the estimates of the model
nd the negative and signicant impact of the level of development on bilateral agricultural exports.
This reason may be due to the fact negative elasticity of agricultural exports. The other variable
of the relative exchange rate has an insignicant impact on bilateral agricultural exports. e reason may
be that the consumption of agricultural goods is based on the customs and habits of people.
Results of the model, augmented with the eect of the common border and common language, conrm
the validity ohe theoretical foundation of the gravity model. e signicant and positive coecient
of these variables depicts that SAARC agricultural exports are strongly influenced by transaction
and transportation costs. Indeed results predict higher bilateral agricultural exports between countries
that share a common language and broader.
e nding of the study conrms the agricultural export creation impact of trade agreements. SAFTA
conrms the signicant and positive impact on bilateral agricultural exports of the SAARC region. Many
studies found that SAFTA has not led to any trade creation among member nations. However, the present
study nds that in the case of agricultural trade, it has signicantly improved or led to trade creation
among member countries. Also, the bilateral free trade agreement ISFT between India and Srilanka has
enhanced agricultural trade between these counties as its coecient is positive and statistically signicant.
CONCLUSION
e study investigates the nature of fundamental determinants of agricultural trade ow among SAARC
nations through the gravity model approach. It utilises the econometric approach using PPML for
bilateral agricultural exports, involving seven SAARC nations, excluding Afghanistan, for the period
2000–2019. Since most of the SAARC nations are agrarian, it is imperative to analyse the nature of trade
ow among SAARC nations. e study reveals that the GDP of both exporting and importing countries
positively impacts the agriculture trade within the SAARC region. e results indicate that increase
in the region’s GDP will enhance interregional agricultural trade, revealing a higher impact of member
SAARC countries absorbing potential for enhancing bilateral agricultural trade. From the results, it can
also be inferred that geographical distance impedes the trade between costs, resulting in higher trade
costs. It indicates that geographical proximity and transport cost costs are the key drivers of agricultural
exports. Such an outcome is further supported by the signicant positive impact of a common language
and common border on the bilateral trade of SAARC members. e results also reveal that SAFTA has
been a trade-creating agreement and has improved the intra-regional agricultural trade in the SAARC
region. erefore, the present study prompts a need for deeper integration in South Asia to improve
trade relations and tackle the poverty and unemployment this region has been facing for decades. Deeper
integration will help in the development of better infrastructure, higher productivity and sustainable
growth in GDP. e nature of agricultural commodities is such that they are mostly perishable and less
durable. For such trade, distance plays an important role, and, therefore, steps must be taken to increase
ANALYSES
224
the connectivity channels and invest in roadinfrastructure is important in this region. Further special
attention should be given to the competitiveness of farmers. Public investment in irrigation should be
accompanied by direct support to farmers.
While our study examined the determinants of bilateral agricultural trade in the SAARC region beyond
the traditional gravity model, it would be worthwhile to raise some of the limitations of the present study
that should be incorporated into further research to improve our understanding of the central theme.
Some of the issues are: rst future research should use the sectoral GDP rather than the overall national
income, second other factors, including taris, geopolitical concerns, import substitution policy and
pricing that inuence the bilateral trade in the SAARC region should be included in future research,
thirdly, the authors recommend the future studies with a larger dataset about these variables and also
comparison between dierent regions for better results and fewer errors. However, from this research
point of view, it has some interesting findings that can help policymakers achieve a better view
of the SAARC region’s bilateral agricultural trade.
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