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44 QUANTIFICATION OF BENEFITS FROM ECONOMIC COOPERATION IN SOUTH ASIA<br />
would enhance intra-regional trade by 160%. Ghosh<br />
(2003) has estimated the Gravity Model to capture the<br />
nature of India’s trade with respect to different regions<br />
(NAFTA, EU, ASEAN, SAARC, etc.). The model is used<br />
for total trade, export and import functions separately.<br />
For export equation, NAFTA, EU, ASEAN as a region<br />
produced a positive and significant coefficient. For<br />
other countries and regions it has registered a negative<br />
sign, implying that those regions produce some kind<br />
of trade barrier for India’s export to these countries.<br />
With respect to South Asia and SAFTA, studies have<br />
argued that the potential trade is much higher than<br />
that what has been harnessed by South Asian countries.<br />
Frankel (1997) opined that South Asia behaves against<br />
the natural blocs’ argument and may be the only<br />
exception. The estimates of Frankel and Wei (1995)<br />
indicate that trade between India and Pakistan is 70%<br />
lower than two otherwise identical economies. The issue<br />
of ‘inverse regionalism’ has been raised by Lahiri (1998)<br />
with respect to trade between India and Pakistan<br />
considering the role played by quantitative and<br />
administrative restrictions and political process. This<br />
notion has also been supported by ADB (Development<br />
Outlook, 2002, www. adb.org). According to it, intra-<br />
SAPTA exports were only 4.8% in 2000, the lowest of<br />
any of the PTAs. The model estimated by ADB was<br />
with respect to intra-SAPTA exports.<br />
METHODOLOGY<br />
The approach adopted in this study is to estimate a<br />
standard gravity model by including tariff faced by the<br />
exporting member country of SAFTA vis-à-vis the<br />
bilateral member partner country. The model is<br />
estimated with tariffs included. Given high tariff rates<br />
in South Asian countries, especially in their agriculture<br />
sector it becomes important to include tariff to explain<br />
bilateral trade flows. The coefficient of tariffs estimates<br />
the impact of tariffs on bilateral trade among SAFTA<br />
member countries. The potential bilateral trade is<br />
estimated without tariffs.<br />
To estimate the effects of SAFTA among its members,<br />
we build on a standard gravity model, specified<br />
as follows:<br />
Log T ijt<br />
= β 0<br />
+ β 1<br />
log (GDP it *<br />
GDP jt<br />
) + β 2<br />
log D ijt<br />
+ β 3<br />
log (POP it<br />
* POP jt<br />
)<br />
+ β 4<br />
log (1 + Tariff jit<br />
)<br />
+ β 4<br />
log (1 + Tariff ijt<br />
) + e ijt<br />
(1)<br />
where T ij<br />
is the bilateral trade between countries i and<br />
j at time t, GDP it<br />
and GDP jt<br />
are the gross domestic<br />
product at constant prices of countries i and j. D is the<br />
ij<br />
distance between the two partners, POP it<br />
and POP jt<br />
are<br />
the populations in country i and country j at time t.<br />
Tariff ji<br />
is the tariff rate faced by the exporting country<br />
in the partner importing country while Tariff ijt<br />
is the<br />
tariff imposed by importer country i to country j.<br />
Panel data for seven countries for a ten-year period,<br />
i.e. 1995–2005 is used to estimate the model, the<br />
utilisation of which requires special treatment of the<br />
error terms shown in equation (1). A more general<br />
expression of the error term can be written as follows:<br />
e ijt<br />
= u i<br />
+ v j<br />
+ w t<br />
+ µ ijt<br />
(2)<br />
where u and v are country specific effects; w is temporal<br />
effect and ì is random effects.<br />
The first two explanatory variables capture the idea<br />
that larger and richer countries trade more than small<br />
and poor countries. The product of GDP of trading<br />
countries measures the size of the economy of trading<br />
partners while the product of populations captures the<br />
per capita purchasing power in the country. This reflects<br />
the country’s stage of development. According to<br />
Martinez-Zarzoso and Nowak-Lehmann (2003) the<br />
coefficient of population of the exporters may have<br />
negative or positive sign depending on whether the<br />
country exports less when it is big (absorption capacity)<br />
or whether a big country exports more compared to a<br />
small country (economies of scale). For similar reasons<br />
the coefficient of importer population may have<br />
negative or positive sign. Distance indicates the impact<br />
of geographical proximity on bilateral trade flows lesser<br />
the distance lower will be the transport and information<br />
costs. A negative coefficient is expected in the distance<br />
variable and positive coefficients in all other variables.<br />
Annual data on bilateral trade flows for the period<br />
1995–2005 for SAARC countries has been collected<br />
from WITS data set (World Bank). GDP (at constant<br />
prices) data has been gathered from World<br />
Development Indicators (World Bank). The information<br />
on distance variable has been downloaded from http:/<br />
/www.cepii.com/distance. We estimate an augmented<br />
gravity equation in (1) using panel data for year 1995<br />
–2005. Matyas (1997), Matyas (1998) and Egger<br />
(2000) demonstrated that a panel data approach<br />
obtains better results compared to a cross-section<br />
approach since the former allows capturing business<br />
cycle phenomenon faced by the trading partners, and<br />
helps to disentangle time-invariant country-specific<br />
effects. Incorporation of country-pair specific fixed<br />
effect is the best way to control for heterogeneity in<br />
gravity model (Cheng and Wall 2005). Accordingly, the