Report

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