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ESTIMATION OF POTENTIAL TRADE UNDER SAFTA WITH THE GRAVITY MODEL 45<br />
gravity model developed in this paper is estimated by<br />
using panel data approach with country-pair specific<br />
as well as year-specific fixed effects.<br />
We estimate Fixed Effect Model (FEM) and<br />
Random Effect Model (REM). According to the<br />
literature (Egger 2000) FEM is better suited to when<br />
estimating typical trade flows between ex-ante<br />
predetermined selections of countries. We also use<br />
Hausman Test to arrive at the more appropriate model.<br />
The test is undertaken for the null hypothesis of no<br />
correlation between the individual effects and the<br />
regressors. The test also indicates FEM to be more<br />
appropriate. Likewise results of FEM are reported.<br />
Since the effect of variables that do not change<br />
overtime, e.g. distance cannot be estimated directly by<br />
FEM as inherent transformation drops the variable,<br />
we use two step method, i.e. we estimate in second step<br />
using individual effects as dependent variable and<br />
distance and other dummies as explanatory variables.<br />
THE EMPIRICAL RESULTS FROM<br />
THE GRAVITY MODEL<br />
This section examines whether the factors indicated in<br />
the gravity equation make a significant contribution<br />
to an explanation of the trade flows in the SAFTA<br />
region. The gravity model has been tested both for the<br />
aggregate bilateral trade and also for product level<br />
trade. In the literature, aggregate gravity model has been<br />
estimated using different data set by Wang and Winters<br />
(1991), Hamilton and Winters (1992), Baldwin (1994),<br />
Breuss and Egger (1999), etc. Bergstand (1989) and<br />
Feenstra et al. (2001) has estimated product specific<br />
models. In the present study, it was not possible to<br />
estimate gravity model for all sectors due to nonavailability<br />
of sectoral data for the period under<br />
consideration. Therefore, the gravity equation is fitted<br />
on aggregate data and on some selected sectors. The<br />
results for gravity equation are presented in the Table<br />
5.1.<br />
The results presented in Table 5.1 (Column 1) show<br />
that the coefficient of GDPs in both the exporting<br />
country and the importing country has a significant<br />
positive impact on the bilateral trade between the two<br />
countries. The coefficient of the product of populations<br />
of the two countries captures the per capita purchasing<br />
power in the country and reflects the country’s stage of<br />
development. The coefficient is positive and significant<br />
indicating that countries with higher level of development<br />
are trading more with each other. Weighted tariffs<br />
are found to have a statistically significant negative<br />
effect as expected. Column 2 of Table 5.1 is estimated,<br />
which includes tariffs as one of the explanatory<br />
variable. The results with respect to GDP and population<br />
are found to be robust as they do not change with<br />
specification.<br />
Using the two-step approach, distance dummy is<br />
found to have a significant negative impact on bilateral<br />
exports (Column 3). Distance is indicative of transport<br />
cost between the partner countries. The coefficient for<br />
the land border dummy (D4) is not found to be<br />
significant. It implies that common border is not an<br />
important variable in the SAFTA region in explaining<br />
the existing trade flows. A number of political and<br />
economic explanations can be attributed to this effect.<br />
It is also well known that there is huge informal trade<br />
between adjoining countries but has not taken formal<br />
route due to political differences in major economies.<br />
Table 5.1 Gravity Model Estimates for member countries in SAFTA<br />
Dependent Variable: Ln Exports ijt<br />
Column 1 Column 2 Column 3 (Fixed Effects<br />
from Equation 2)<br />
Ln (GDP i<br />
* GDP j<br />
) 0.44*** (3.45) 0.41** (2.82)<br />
Ln (Pop i<br />
* Pop j<br />
) 0.34** (2.79) 0.34** (2.06)<br />
Ln (Tariffs weighted) ji – –0.22** (–2.18)<br />
Ln (Tariffs weighted) ij – –0.22** (–2.18)<br />
Distance dummy – – –0.001* (–1.89)<br />
Border dummy – – –1.37 (–0.64)<br />
Language dummy – – 3.22** (2.22)<br />
Constant – – 13.29*** (4.90)<br />
Number of observations 231 198 42<br />
Log Amemiya prob cr –1.96 1.97<br />
R–sq (between) 0.71 0.59 0.20<br />
Akaike Info. Crt. 1.25 1.36<br />
* is significant at 10%; ** significant at 5%; *** significant at 1%.