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R EVENUE AND WELFARE IMPLICATIONS OF SAFTA: PARTIAL EQUILIBRIUM ANALYSIS 55<br />
To model the behaviour of the consumer, SMART<br />
relies on the Armington assumption. In particular, the<br />
adopted modelling approach is based on the assumption<br />
of imperfect substitution between different import<br />
sources (different varieties). That is, goods (defined at<br />
the HS 6-digit level) imported from different countries,<br />
although similar, are imperfect substitutes. In the<br />
SMART modelling framework, a change in trade policy<br />
(tariff liberalisation) affects not only the price index/<br />
level of the composite good but also the relative<br />
prices of the different varieties. SMART reports the<br />
results of any trade policy shock on a number of<br />
variables, including tariff revenue, consumer surplus,<br />
and welfare.<br />
Tariff revenue change on a given import flow is<br />
computed simply as the final advalorem tariff multiplied<br />
by the final import value minus the initial advalorem<br />
tariff multiplied by the initial import value. Using<br />
SMART simulations, an attempt has been made to<br />
estimate revenue effect of tariff reduction by 100%.<br />
Revenue effect of 100% tariff reduction are also cross<br />
checked by multiplying simple and weighted average<br />
tariff with total import value of the country. Welfare<br />
and trade creation effect of 100% tariff reduction has<br />
also been estimated by SMART modelling.<br />
REVENUE, WELFARE AND TRADE EFFECTS:<br />
RESULTS<br />
Impact on Bangladesh<br />
Tables 7.1 and 7.2 indicate bilateral revenue, welfare<br />
and trade effects of zero tariffs under SAFTA for<br />
Bangladesh.<br />
• Revenue losses to Bangladesh are estimated to be<br />
about $0.9 billion on the basis of SMART<br />
simulation while it is $0.5 billion on the basis of<br />
weighted average tariff and 0.23 billion in case of<br />
simple average. Maximum revenue loss of $0.88<br />
billion is due to tariff cut by 100% on imports<br />
from India (Table 7.1).<br />
• There are welfare gains for Bangladesh and other<br />
SAARC countries from 100% tariff reduction by<br />
Bangladesh. Welfare gains for all seven countries<br />
amounts to $0.8 billion and maximum welfare<br />
gains are due to tariff cuts by India and Bangladesh<br />
(Table 7.2).<br />
• 100% tariff reduction by Bangladesh creates trade<br />
of approximately $0.27 billion (Table 7.2).<br />
Table 7.2 Effect of 100% Tariff Cuts on Welfare<br />
and Trade: Bangladesh<br />
($ ‘000)<br />
Partner Country Welfare Effect Total Trade Effect<br />
India–Bangladesh 766,355 269,669<br />
Bhutan–Bangladesh 0 0<br />
Pakistan–Bangladesh 29,299 6,173<br />
Sri Lanka–Bangladesh 127 3,593<br />
Nepal–Bangladesh 32 133<br />
Total effect 795,813 279,569<br />
Impact on India<br />
Tables 7.3 and 7.4 indicate bilateral revenue, welfare<br />
and trade effects in case India cuts tariff by 100% for<br />
SAFTA member countries.<br />
• Revenue losses to India are about $0.12 billion on<br />
the basis of SMART simulation while it is also<br />
approximately $0.1 billion on the basis of simple<br />
and weighted average tariff. Results are quite<br />
consistent in both the approaches (Table 7.3).<br />
• There are welfare gains for India and other SAFTA<br />
member countries from 100% tariff reduction by<br />
India. Welfare gains for all seven countries amount<br />
to be $0.8 billion (Table 7.4).<br />
• In case of 100% tariff reduction by India, trade<br />
increased by approximately $0.7 billion (Table 7.4).<br />
Table 7.1 Effect of 100% Tariff Cuts on Revenue: Bangladesh<br />
($ ‘000)<br />
Partner Country Revenue Effect: Using Revenue Effect Revenue Effect Bangladesh Imports<br />
SMART Simulations (using simple average) (using weighted average)<br />
India–Bangladesh –887,442 –202,676 –505,603 1,278,712<br />
Bhutan–Bangladesh 0 0 0 0<br />
Pakistan–Bangladesh –25,858 –29,672 –26,098 142,379<br />
Sri Lanka–Bangladesh –1,954 –1,910 –2,094 9,567<br />
Nepal–Bangladesh 0 –28 –7 129<br />
Total effect –915,254 –234,286 –533,802 10