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September 1993

Preferential Trade Arrangements:

Estimating the Effects on South Asia Countries

T. N. Srinivasan"-

Gustavo Canonero

I. Introduction

Measuring the impact of potential trading blocks involving South AsianI

countries is the aim of this paper. R. Safadi and A. Yeats (1993) have

~ already analyzed the likely impact of the formation of the North American Free

Trade Area on South Asia. We broaden this line of research to analyze the

impacts of other potential preferential trading arrangements. In particular,

we evaluate the effects of South Asia Regional Integration (among Bangladesh,

India, Nepal, Pakistan and Sri Lanka), as well as preferential trade

arrangements (PTA's) of individual South Asian countries with some non-

regional partners like USA, NAFTA as a whole, EEC or an Asian Group.

Our tool for empirical analysis is an extended version of the simple and

well-known Gravity Model of Bilateral Trade, such as the one used by J.

Frankel et al. (1993). We estimate bilateral trade using the standard

approach with explanatory variables like GNP, GNP per capita, and distance,

but we also attempt to quantify the impact of restrictions to trade, basically

represented by tariff barriers. The coefficients on these tariff barriers are

then used to quantify the outcome of the various PTA's mentioned before.

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II. Model Specification and Estimation Method

Based on the empirically well-behaved simple versions of the Gravity

Model of Bilateral Trade, our working equation is the following:

Log BT~,j,t=ag+a~ Log (GNPi,t*GNPj,t)+a~ Log (GNPPCi,t*GNPPCj,t) (1)

+a~Di,j+a~ Log T~,j+a~Log Tj,i+a~ Log REXRi,j,t+U~,j,t

where BTi,j,t is bilateral trade between countries--or group of countries--i

and j in the year t and the superscript c indicates the commodity group. GNP

and GNPPC are the respective Gross National Product and per capita National

Product of both partners in year t, Di,j is the distance in kilometers between

the relevant cities, TCi,j is (one plus) the tariff imposed by country i in its

trade on commodity c with j and REXR is a measure of real exchange rate

changes in any of the two trading partners. BT, GNP, GNPPC are measured in

thousand of 1987 US dollars, using the GDP deflator for the US to deflate

nominal values.

The expected results are that trade between two equal sized countries

should be greater than between one large and one small country, hence al is

likely to be positivel. Moreover, it is reasonable to think that GNP per

capita would also have a positive effect on trade (az > 0) since it is a proxy

l\See Frankel et a1. (1993) for a brief discussion and references about thisspecification. Deardorff (1984) points out that the early formulations of thegravity model by Tinbergen (1962) and Poyhonen (1963) were based on ad hoc butintuitive theorizing, and in his further elaboration of the model Linnemann(1966) derived an analogue of the gravity equation from a quasi-Wa1rasiangeneral equilibrium model under the rather implausible assumption of separatedemand functions for imports from each trading partner. Anderson (1979)derived an extremely simple version of the model by assuming that each countryis specialized in its own good and all countries have the same Cobb-Douglaspreferences. Bergstrand (1985, 1989) derives a generalized gravity model froma general equilibrium model with products differentiated by place ofproduction and place of sale. See Wang and Winters (1991) for a briefdiscussion of the theory of gravity models.

3

of degree of development and therefore of specialization. On the other hand,

the distance between two countries, like tariff barriers, should restrict

trade, one naturally (through transport cost) and the other artificially

(through policy). Thus we would expect a3' a4 and as to be all negative.

Finally, the effect of real exchange rate on total trade is ambiguous, but it

is included in equation (1) to serve as a "catch-all" proxy variable for other

fundamental changes not incorporated in the other variables, such as a maxi-

devaluation, for instance.

The utilization of the panel features of the data set required special

treatment of the error term in (1). A general expression for Uci.j,t could be

the following:

C C /}C C C (2)Ui.j,t= fi + Uj + JJt + 'Ii,j,t

where, f and 0 represent the individual effects, JJ a temporal effect and ~ a

purely random effect. Leaving aside the temporal disturbance for the moment,

we can always avoid consistency problems arising from individual effects

(though not necessarily achieve efficiency), by estimating of equation (1)

with its variables defined as deviations from their individual means. However,

this option is not attractive since for evaluating the effects of trade

liberalization we need to know values of the parameters associated with some

individual variables, viz. Ti and Tj' which are eliminated from the estimating

equation once the variables are expressed as deviations from their means.

Having to estimate all parameters in equation (1) leads us to the

standard variance-components model of (2). If f and 0 were fixed effects, then

by using country dummies the problem is easily solved. On the other hand, if

these effects were random, the best way to estimate equation (1) would be by

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using Generalized Least Squares, taking into account the contribution of the

variance of country effects to the variance of U~jt. In this case, a common

problem that arises is the possible correlation between the individual country

effects and the explanatory variables. This possibility should be tested

through an appropriate test for the exogeneity of explanatory variables in

order to see if instrumental variables are needed2.

A preliminary estimation of (1) using country dummies demonstrated that

the assumption of fixed effects is inappropriate: the residuals from this

estimation still showed the presence of individual effects (we tested for

homoscedasticity, see below). Therefore we reestimated equation (1) assuming

random effects, used the lags of GNP and GNPPC as instruments and tested for

their exogeneity.

The results of our estimation and associated tests are presented in

section IV, where we also report on our attempts to take into account the

possible presence of the temporal terms. We review the sources and

characteristics of our data in Section III and report the simulated effects of

various preferential trade arrangements in Section V.

III. The Data

The raw data on trade are from the United Nations' COMTRADE database (at

the International Computing Center in Geneva), where imports and exports are

l\Actually, instrumental variables should anyhow be used in this particularcase since variables like GNP or GNPPC are probably correlated with the errorterm, since the dependent variable, TB, is the sum of imports and exports. Inspite of this, it is indeed striking that the ruling practice is to use simpleOLS in estimating these equations!

5

reported in current USA dollars for each country and all its trading partners.

The data are available annually and by commodity using SITC classification

(Rev. 1 and 2).

With this basic information we calculate the bilateral trade (BT-

imports + exports) on 10 commodities for 21 trading partners. Both commodity

and country aggregation intend to enhance the most important features of the

actual trade pattern of South Asia Countries3.

The ten composite commodities are: Coffee and Tea, Textile Fibres,

Leather and Dressed Fur., Textile Yarn and Clothing, where South Asia

concentrates its exports, and other aggregates such as Non-Fuel Primaries,

Fuels, Machinery and Transport Equipment, Other Manufactures and Total Trade.

On the other hand, 13 individual countries were selected (the five from South

Asia and their major partners: USA, Canada, Mexico, Japan, Hong Kong, Korea, ,

Singapore and China) and 8 groups (EEC 6, EFTA, Africa, Asia, Middle East,

Latin America, OECD and European countries not considered elsewhere).

In order to calculate the relevant distance we use the major city for

the individual countries (mostly the capitals) and an average distance for the

groups of countries (usually taking two cities in the group or a city located

in the geographic center of the group). In particular, we use the 3 major

ports in India to calculate its distance with other partners since the size

(in area) of a country could greatly affect transport cost of internal trade

relative to international trade with its neighbors, and therefore it could be

an important factor in our simulations (see Srinivasan and Canonero (1993) in

l\A summary of actual trade in the South Asia Region is presented in TablesB.l to B.3 in Appendix B.

6

this context). A complete description of country groups and associated cities

is presented in Appendix A, Table A.I.

The information on tariffs used in the estimation is shown in Appendix

A, Table A.2. The figures for South Asian countries are the implicit effective

tariffs calculated from tariff revenues. These are average tariffs and include

import and export duties. The data on tariffs imposed by developed countries

and Mexico come from the UNCTAD-World Bank Software for Market Analysis and

Restrictions on Trade (SMART), where they are available desegregated by

commodity. The tariff structure in Table A.2 is the reported one for Japan and

Mexico (again effective tariff) but not for Canada, EEC and USA. Although the

actual tariffs imposed by the latter are not completely similar, these

probably are compensating differences in their NTB coverage. We therefore

decided their tariff structure to be the same as the average of their reported

tariffs4.

One final remark should be made about the series Ti and Tj. Although we

consider that tariffs affect traders uniformly, we nevertheless treat trade

not involving South Asian countries as unaffected by tariffs. In other words,

Ti and Tj are set at one whenever neither i nor j are Bangladesh, India,

Nepal, Pakistan or Sri Lanka. Basically this procedure implies that the

magnitudes of such tariffs, as compared to those associated with South Asia

countries, to be small enough to be negligible.

Finally the variable REXR aims to account for significant changes in

real exchange rate in the South Asia countries. REXR is an index with base

1965=1 for the real exchange rate of the domestic currencies of South Asian

~\. Information on NTB imposed by Canada, EEC, Japan, Mexico and USA on SouthAsia Countries in 1988 is revealed in Appendix C. Like tariffs data, thesource for NTB is SMART.

7

countries and the USA dollar and it is set at 1 for any other country. REXR is

calculated using the nominal exchange rate and GDP deflator from IMF's

International Financial Statistics (exchange rate is market value, period

average -series rf-). It is the source also for the GNP and Population

figures. In those observations where both the trading partners are from South

Asia an average of each REXR is used.

IV. The Results

Table 1 to 10 report the estimation results for equation (1), for the

ten commodities in our sample. We use annual data from 1968 to 1991, having

218 pairs of trading partners and 5175 observations.s Each Table has 3

different parts, which besides showing the final results, also reveal the

importance of handling the individual effects. The outputs are:

1) The results of the estimation using Instrumental Variables but

ignoring the panel feature of the data set, i.e. without variance

decomposition.

2) The results of standard procedures for testing for the presence of

individual effects.

3) The results of using Instrumental Variables, but also accounting for

the variance decomposition implied by the test in 2 above. In this final

output a WALD test for the significance of the whole equation is also

included (the null hypothesis is: Ql-QZ-Q3-a4-aS=Q6-0) , since the reported

2\The 218 pairs of trading partners arise from the following. By pairing eachcountry (or groups) from our total of 21 (13 countries and 8 groups) with adifferent country (or group), we get 210 pairs (i.e. (20*21)/2). In addition,since the countries within each of the 8 groups trade with each other, thereare 8 within group trade pairs, i.e. each group trading with itself. Althoughdata relate to a period of 24 years (1968-1991), because some data aremissing, the number of observations is 5175 instead of 5232 (i.e. 218 x 24).

8

R Square becomes uninformative whenever instrumental variables are used.

These final results are used in subsequent simulations.

Before turning to the estimation results, two comments are in order.

First, the instruments chosen were the second and third lags of GNP and GNPPC.

In order to check their merits as instruments, we ran an overidentified

restriction test on each of them. One way to run these tests is by

regressing ~ instrument on the residuals from an equation using all but

~ instrument. The results from this round of tests were satisfactory, all

instruments showing no correlation with the residuals. However, as is well

known, these tests are valid only when there is at least one "true"

instrument, which is seldom the case. Anyway, the lack of ideal instruments

left us with few alternatives. However our simulations are not likely to

depend heavily upon the quality of instruments used. We believe that the

consistency of al and az is more likely to be harmed by a bad instrument than

those that are critical to our simulations, viz. the parameters on tariffs (a4

and as).

The second comment, is about the temporal effect or the possibility of

serial correlation in the error term of equation (1). The D-W statistics in

our estimates are usually below the critical value of 1.7 (approximately the

lower bound at 0.05 level of significance, given the degree of freedoms we

have). Unfortunately we do not have time series data long enough to estimate

an appropriate structure of serial correlations. Economising on degrees of

freedom by not allowing each pair of trading partners in our panel to respond

differently to the same time disturbance led to implausible results.6

Q\For instance, we estimated equation (1) by Maximum Likelihood, correctingfor autocorrelation of order one, assuming the same time series model for all

(continued. ..)

",#1'),~;

9

The above discussion should make the reading of Tables 1 to 10 simpler.

We will avoid, therefore, repetition and only comment on the final output, the

one estimated by instrumental variables and accounting for the individual

random effects in the variance composition (the estimation method used is:

Generalized Least Squares-Instrumental Variables). Each Table presents the

result for each composite commodity, starting with Total Trade in Table 1 .

The remarkable explanatory power of this equation is the basic

conclusion of our exercises. This is no surprise since the gravity model has

been found by many earlier authors to explain bilateral trade flows

exceptionally well in spite of its weak grounding in theory. All variables

have the expected signs and are significant at 5% level for almost any

composite commodity. The bigger the trade partners, the more significant is

the bilateral trade, the greater the distance between the trading countries

and the higher the tariff barriers the smaller is the value of bilateral trade

too.

Only the coefficients of product of GNP per capita have ambiguous signs

(i.e. positive in some and negative in other equations). They are

statistically significant in some but not all cases. For example, GNPPC is

significantly positively correlated with trade in coffee, tea and spices,

machines and transport equipment and clothing, while being negatively or not

significantly correlated with trade on other composite commodities. However,

two of the three negative coefficients, i.e. those relating to trade in

textile fibres and non-fuel primary products, are statistically significant.

These findings are not inconsistent with received theory under which a higher

.§.\(...continued)individuals (i.e. the same value for Pl), and every coefficient a turned to benon significant.

10

GNP per capita indicates a higher degree of development, and therefore more

specialization, which in tern is more important for manufactured goods. That

both significant negative coefficients relate to trade in primary goods is

consistent with this hypothesis.

Since the dependent variable is the sum of imports and exports, the

effect of the real exchange rate on it is ambiguous in theory since its effect

is expected to be positive on exports and negative on imports (actually these

are the expected signs in the long run, while in an annual basis trade-

exchange rate relationship for some commodities may not always follow the

long-run direction, principally because of contractual rigidities). However,

in our estimations the real exchange rate shows positive correlation with

bilateral trade in every commodity, although this statistical relation is not

significant for the primary goods (i.e. coffee, tea, cocoa and spices, textile

fibres and fuels). Obviously, the explanations for the signs and sizes of the

estimated coefficients have to be found in the likely influence of the real

exchange rate on the demand and production of the relevant commodities. Be

that as it may, the significance of real exchange rate on total trade for non-

primary goods indicates that empirically this effect is positive.

Equipped with this set of results we simulate the effect of preferential

arrangements involving South Asian countries. In our framework, a simple way

to get an u~~er bound for this effect is to consider the total elimination of

tariff barriers between those countries or group of countries for which such

an arrangement is contemplated. The results of these simulations are

presented in the following section.

11

V. Numerical Simulations

In interpreting our simulation results, it is important to keep in mind

that the simulations are driven by the estimated coefficients a4 and as for

the tariff variables Tl and Tz respectively. We clarify their role while

stressing some of the limitations of our calculations.

Basically we measure the impact of preferential trade arrangements (PTA)

by the proportionate change in the USA dollar value of trade they create. The

higher the initial tariff level on trade between partners, the greater the

final effect of such arrangements since, by definition, they eliminate the

tariffs. We should, therefore, expect the impact to be increasing in the

estimated values of a4 and as. However, the tariff is only one among many

elements that determine the impact of PTA on trade. In assessing the impact

of preferential tariff reductions, two other features of our model have to be

kept in mind.

First, the series Tl mainly represent tariffs imposed by Canada, EEC,

Japan, Mexico and USA on imports from South Asian countries, while series Tz

are tariffs imposed by the latter on trade with the former group. Since

tariffs Tz are initially higher than Tl, the higher the coefficient of Tz, in

absolute values, the greater the impact of preferential arrangement.

Second, since a4 and as in equation (1) are elasticities indicating the

proportionate response of bilateral trade to changes in tariffs, the initial

tariff level as well as the initial trade level are relevant for determining

the absolute changes in trade following a PTA.

Once the forces driving our simulations are understood, their

limitations also become apparent. First, our approach does not take into

account possible terms of trade effects associated with the creation of trade.

12

As such, the simulated results almost certainly overestimate the true impact.

On the other hand, the static framework of our exercise does not consider many"

important dynamic aspects of trade liberalization and these could reinforce

the short-run trade creation thus underestimating the true long-run impact.

The long-run effect is very difficult to forecast without a more general

dynamic setup that generates terms of trade effects and allows for scale

economies, investment, and spillovers of technology etc. The basic

information to quantify all these is not available: for example, some price

elasticities could be approximated but information on scale economies simply

does not exist. However, experience with the few studies that tried to

estimate dynamic effects in trade, indicate that short-run effects are likely

to overestimate the true long-run effects. Second, our measure of trade

barriers, viz. an indicator for average tariffs, does not fully capture the

effect of many non-tariff barriers to trade. Third, even if our simulations

correctly measure the impact on trade creation, it should be realized that

this impact is not the only factor to take into account in evaluating PTA's.

For instance, the negative effect on bilateral trade with countries not

entering in the simulated arrangement is not assessed at all in our

simulations. Moreover, none of the indicators from the simulations could be

viewed as a welfare measure, thus making the comparison of different scenarios

rather incomplete. In other words, the results presented below serve the

limited purpose providing an estimate of the potential effects on bilateral

trade between each South Asian country and its partner in the simulated PTA.

The expected outcomes from different preferential trade arrangement are

presented in Tables 11 to 19. Each table represents the outcomes for each

composite commodity, except coffee, tea, cocoa and spices. The coefficients

13

for tariffs (as and a6) on the latter were not significantly different from

zero so that a tariff reduction does not affect trade flows.

The parameters in the estimated equation give us the expected

~ro~ortionate increase in bilateral trade. Using data for 1990 and 1991 for

all the countries (except Nepal, for which we had data only for 1989-1990), we

translate the magnitude of such effect into USA dollars. As mentioned earlier,

the exercise simply associates preferential arrangement with total elimination

of existing tariffs between the member countries.

Each column in each table corresponds to a South Asian country and each

row, its potential partners in preferential trading. They are: USA, Canada,

Mexico, EEC, Japan and SAS (i.e. other South Asian countries). The sum of the

effects on bilateral trade with USA, Canada and Mexico in each column would

correspond to the effect of the South Asian country represented by that column

joining NAFTA. The data in the cells are the basic indicators of trade effects

when the preferential arrangement is between a South Asian country (column)

and the partner (row). The cells in the last rows indicate the effects for

each South Asian country of its entering into a preferential trade arrangement

with the other South Asian countries.

The results are presented in tables 11 to 19 in two categories, those

contained in part A of each table show the expected value of the estimated

effects, and those in part B of each table refer to confidence intervals for

these expected values at 95% level of significance. The latter reveal the

relative precision of the simulated effects7.

More specifically, in each cell of each table's part A:

1\ For a technical description of the estimated expected value and itsconfidence interval, see Appendix D.

14

1) The first row indicates the proportional increase in bilateral trade

that would happen if the countries corresponding to the row and column

of the cell joined a preferential arrangement. It is calculated as:

exp(~4ln(Ti)+~5ln(Tj)+~ tl2(a4 In(Ti)+a5 In(Tj» » -1 (see Appendix D), where

Ti-(l+percentage tariffi). For instance, for i being USA and j being

India and the commodity being total trade, this value would be exp(3.9

In(1.13) + 4.65 In(1.42) + ~ 0.2325) -1- 8.2. Clearly, in order to

express this in percentage terms, this value should be multiplied by

100.

2) The value of such potential increase in bilateral trade denoted in

millions of USA $ (1991 prices) is given in the second row. This is the

value in 1) times the initial bilateral trade between i and j.

3) The value of potential increase in bilateral trade given in 2) as a

percentage of the initial value of total trade of the South Asian

country represented by the column is given in the third row.

4) The value of the potential increase in bilateral trade given in 2) as

a percentage of the initial value of GNP for the South Asian country

represented by the column is given in the fourth row.

The information on each cell of each table's part B is similar to the

one detailed above, but expressing the estimated effects by their estimated

upper and lower bound at 95% level of significance. Thus, row 1 in each

table's part B has the potential range of increase in bilateral trade and the

15

other rows just translate these magnitudes in terms of millions of USA $ (1991

prices), share of total trade and GNP, as it is done in part A of each table.

In short, in both parts of each table, the first two rows give the

proportionate and USA dollar value magnitudes of the expected increase in

bilateral trade when both partners reduced theirs tariffs to zero. The third

and fourth rows achieve comparability across countries by relating the

absolute magnitude to the value of total trade and of GNP respectively of the

relevant South Asian country.

Tables 11 to 19 reveal that the expected trade effect of preferential

arrangements is impressive. For all the countries in the South Asian region,

the most important potential effect is associated with textile fibres and

manufactures, specially textile yarn and clothing. This is not surprising

since in this case the initial trade is substantial, tariff barriers are high

and the estimated values of a4 and as are also high.

For the larger economies, like India and Pakistan, the principal gains

seem to come from preferential arrangements with the European Community and

USA. This is to be expected since a large share of their trade is with these

countries. On the other hand, regional integration leads to greater gains in

the value of trade for the small economies like Bangladesh and Nepal.

A summary indicator of all the results obtained are the effects on total

trade presented in Table 11.A. (Table 11.B shows the associated confidence

intervals). It is clear that the greatest ~ro~ortional increase expected in

bilateral trade would come from regional integration. The countries in the

region have all higher tariffs than other countries and therefore a larger

impact from tariff reduction in their tariff should be expected. For example

the trade between Bangladesh and the other South Asian countries is expected

""

16

to increase by 9.5 times and almost the same is the case of Pakistan (8.9).

For Sri Lanka the expected proportional increase is about 10.3 times, for

India 12.8 times, while for Nepal this increase is even higher, at 17.2 times.

However, given the initial trade pattern of these countries, regional

integration leads to a greater increase in trade for Bangladesh and Nepal than

for the other South Asian countries.

For the two small economies of Bangladesh and Nepal, regional

integration seems to be the most powerful preferential arrangement for trade

creation. The values of expected increases in trade are incredible.

Bangladesh's new trade with the region would account for US $ 4.6 billion,

exceeding its actual total trade of $ 3.8 billion by 17%, and accounting for a

whopping 21.1% of its GNP!.

In the case of Nepal the trade expected to be created in the region is

around US $ 1.7 billion. Though this is smaller than that for Bangladesh, it

is not less impressive considering the (economic) size of Nepal. The new trade

would be almost three times the actual total trade of Nepal and 58.5% of its

GNP!.

The effects of regional integration on the large economies in the

region, viz. India or Pakistan, are naturally very different from those of

Bangladesh and Nepal. For India and Pakistan a regional integration is

important, but their much larger trade with the European Communities and USA,

makes integration with the latter more attractive. Both countries would

achieve the greatest impact on their trade by integrating their economies with

EEC, for India bilateral trade would increase by 2 times its actual total

trade, while for Pakistan the corresponding figure is around 0.95. Translating

these effects into US dollars, new bilateral trade between India and EEC would

17

amount to US $ 85 billion, representing 30% of India's GNP!. For Pakistan

these figures would be US $ 13 billion and 30%.

The differential effects of the above scenarios on particular

commodities are interesting. For example, for all South Asian countries,

regional integration would clearly create more trade on textile fibres than

any other preferential trade arrangement. In this commodity the tariff

reductions would be significant and the actual trade within the region is

strong enough to foster likely future trade. On the other hand, the actual

trade pattern indicates that trade in clothing could receive an extraordinary

impulse if South Asian countries integrate with EEC or USA, with the increased

trade being similar for the large and small economies in the region.

In conclusion, one would have to recognize that the estimated effects

are sometimes too large to be believed, as it is the case of Nepal. However,

it is worth noting that the results of Tables A are estimated average effects.

Actually, the likelihood of the final outcome in any possible scenario is

better illustrated by the more comprehensive measure presented in tables B,

viz. the confidence intervals. Indeed, these tables show the wide range of

magnitudes that are possible as a final output in any of the exercises

presented above. Furthermore, as we emphasized earlier, some important

factors affecting trade have not been taken into account in our simulations.

Almost surely they could restrain significantly the numerical magnitudes of

the effects reported.

18

Table 1. TOTAL TRADE

Panel of Annual Data 1968-1991 N.Obs. 5175

INSTRUMENTAL VARIABLES -NO PANEL FEATURES

Variable Coeff Std Error T-Stat Signif

GNP 0.72735026 0.01715866 42.38968 0.00000000GNPPC 0.33445465 0.02252389 14.84888 0.00000000D -0.80508512 0.04660275 -17.27549 0.00000000T1 -5.74306686 0.41535882 -13.82676 0.00000000T2 -5.45346591 0.37926731 -14.37895 0.00000000REXR 0.78663776 0.09355628 8.40818 0.00000000Constant -21.99658331 0.82354954 -26.70948 0.00000000

R**2- 0.54 R**2 Adj.- 0.54 DW-0.74

ANALYSIS OF VARIANCE:

Source Sum of Squares Degrees Mean Square F-Statistic Sig.Leve1INDIV 15259.147996383 217 70.318654361 24.399 0.00000000ERROR 14286.355410242 4957 2.882056770TOTAL 29545.503406625 5174

INSTRUMENTAL VARIABLES -RANDOM EFFECTS

Variable Coeff Std Error T-Stat Signif==


R**2- 0.007 R**2 Adj.- 0.007 DW-1.23

WALD TESTF(7)- 2159 Significance Level 0.000

19

Table 2. TRADE ON COFFEE, TEA, COCOA AND SPICES


INSTRUMENTAL VARIABLES

Variable Coeff Std Error T-Stat Signif=

GNP 0.79439542 0.02194642 36.19704 0.00000000GNP PC 0.01387946 0.03054852 0.45434 0.64960219D -0.90546503 0.06031447 -15.01240 0.00000000T1 -6.19172416 0.64326633 -9.62544 0.00000000T2 -5.08781948 0.50324885 -10.10995 0.00000000REXR 0.56535654 0.12045507 4.69351 0.00000275Constant -24.82526493 1.09440921 -22.68371 0.00000000

R**2- 0.37 R**2 Adj.- 0.37 DW~0.35



RANDOM EFFECTS=INSTRUMENTAL VARIABLES

Variable Coeff Std Error T-Stat Signif= -=

GNP 0.62997118 0.08673594 7.26309 0.00000000GNPPC 0.43820868 0.16527136 2.65145 0.00803917D -1.17429747 0.30465940 -3.85446 0.00011739T1 5.81319206 3.38555079 1.71706 0.08602821T2 0.43351890 2.02020145 0.21459 0.83009396REXR 0.09776266 0.08004428 1.22136 0.22200652Constant -20.58091847 3.83574070 -5.36557 0.00000008

R**2~ -0.37 R**2 Adj.- -0.37 DW=1.32


L-- 20

Table 3. TRADE ON TEXTILE FIBRES



Variable Coeff Std Error T-Stat Signif--

GNP 1.03078944 0.01973066 52.24302 0.00000000GNPPC -0.37324564 0.02754701 -13.54941 0.00000000D -0.72120630 0.05431581 -13.27802 0.00000000T1 -10.96516064 0.60888777 -18.00851 0.00000000T2 -5.29645881 0.45778897 -11.56965 0.00000000REXR 0.12815142 0.10872116 1.17872 0.23856536Constant -32.13512929 0.98904687 -32.49101 0.00000000

R**2- 0.50 R**2 Adj.- 0.50 DW-0.47




Variable Coeff Std Error T-Stat Signif--====== =- GNP 0.87541551 0.06257944 13.98887 0.00000000

GNPPC -0.67710245 0.11648153 -5.81296 0.00000001D -0.61608358 0.20840770 -2.95615 0.00312918T1 -15.38539900 2.44383255 -6.29560 0.00000000T2 -7.78261940 1.46380592 -5.31670 0.00000011REXR 0.05893799 0.08126182 0.72529 0.46830997Constant -19.25591102 3.00374227 -6.41064 0.00000000

R**2= 0.06 R**2 Adj.- 0.06 DW=0.90

W'ALD TESTF(7)- 605 Significance Level 0.000I

21

Table 4 TRADE ON FUELS



Variable Coeff Std Error T-Stat Signif--========= =--======- --

GNP 1.00869377 0.02652873 38.02270 0.00000000GNPPC 0.00519856 0.03761178 0.13822 0.89007490D -1.90069876 0.07634009 -24.89778 0.00000000T1 -10.43882651 0.90322259 -11.55731 0.00000000T2 -10.59727703 0.61946772 -17.10707 0.00000000REXR -0.04427047 0.14706739 -0.30102 0.76341008Constant -25.90536596 1.36917193 -18.92046 0.00000000

R**2= 0.50 R**2 Adj.- 0.50 DW=0.48


Source Sum of Squares Degrees Mean Square F-Statistic Sig.LevelINDIV 43512.572213762 217 200.518765962 36.390 0.00000000ERROR 27314.322686216 4957 5.510252711TOTAL 70826.894899978 5174


Variable Coeff Std Error T-Stat Signif-====--- =--- --


R**2= -0.10 R**2 Adj.= -0.10 DW=1.24

W'ALD TESTF(7)- 346 Significance Level 0.000

22

Table 5. TRADE ON NON-FUEL PRIMARIES (EXCEPT COFFEE, TEA, COCOA AND SPICES,and TEXTILE FIBRES)



Variable Coeff Std Error T-Stat Signif=~ --=


R**2- 0.54 R**2 Adj.- 0.54 DW-0.59



RANDOM EFFECTS-INSTRUMENTAL VARIABLES

Variable Coeff Std Error T-Stat Signif-

GNP 0.83519564 0.05637340 14.81542 0.00000000GNPPC -0.20790273 0.10417567 -1.99569 0.04601967D -0.94289393 0.18906570 -4.98712 0.00000063T1 -4.75928644 2.20855807 -2.15493 0.03121342T2 -5.99544834 1.31178519 -4.57045 0.00000498REXR 0.64692815 0.07100743 9.11071 0.00000000Constant -18.88194872 2.67321171 -7.06339 0.00000000

R**2- 0.03 R**2 Adj.- 0.03 DW-1.17


23

Table 6. TRADE ON LEATHER, DRESSED FUR., ETC.



Variable Coeff Std Error T-Stat Signif=== ---== , -


R**2- 0.51 R**2 Adj.- 0.51 DW~0.66




Variable Coeff Std Error T-Stat Signif-==== =


R**2= -0.06 R**2 Adj.- -0.06 DW=1.27

WALD TESTF(7)= 522 Significance Level 0.000

24

Table 7. TRADE ON TEXTILE YARN, FABRIC, ETC.



Variable Coeff Std Error T-Stat Signif---


R**2= 0.45 R**2 Adj.- 0.45 DW=0.54




Variable Coeff Std Error T-Stat Signif--===== -====- GNP 0.75474111 0.05764655 13.09256 0.00000000

GNPPC -0.11040279 0.10228867 -1.07933 0.28049295D -1.39342456 0.19354345 -7.19954 0.00000000T1 -5.16942084 1.60014735 -3.23059 0.00124309T2 -4.30509003 1.32253598 -3.25518 0.00114051REXR 0.69107729 0.07049558 9.80313 0.00000000Constant -13.85127538 2.67484090 -5.17835 0.00000023

R**2= 0.02 R**2 Adj.= 0.02 DW=1.06


25

Table 8. TRADE ON MACHINES AND TRANSPORT EQUIPMENT



Variable Coeff Std Error T-Stat Signif==== =


R**2- 0.61 R**2 Adj.- 0.61 DW=0.82




Variable Coeff Std Error T-Stat Signif--======= =


R**2- -0.22 R**2 Adj.- -0.22 DW-1.50


26

Table 9. TRADE ON CLOTHING



Variable Coeff Std Error T-Stat Signif==


R**2= 0.52 R**2 Adj.- 0.52 DW=0.63




Variable Coeff Std Error T-Stat Signif-=- --=== -=


R**2= 0.04 R**2 Adj.= 0.04 DW=1.18


27

Table 10. TRADE ON OTHER MANUFACTURES



Variable Coeff Std Error T-Stat Signif-


R**2- 0.58 R**2 Adj.- 0.58 DY-0.74




Variable Coeff Std Error T-Stat Signif---=======--- --=


R**2= -0.06 R**2 Adj.- -0.06 DY=1.35

WALD TESTF(7)= 1651 Significance Level 0.000

28

Table 11.A. SIMULATED EFFECTS OF PTA -Expected Values -

Total Trade

Bangladesh India Nepal Pakistan Sri LankaPOTENTIAL PARTNERS

USA 3.3 8.2 6.1 4.4 3.82,496.8 46,140.9 436.2 7,619.3 2,855.6

65.4% 116.8% 69.3% 54.8% 51.9%11.4% 16.4% 15.0% 17.8% 34.2%

Canada 3.3 8.2 6.1 4.4 3.8352.4 3,803.6 20.1 714.8 194.6

9.2% 9.6% 3.2% 5.1% 3.5%1.6% 1.3% 0.7% 1.7% 2.3%

Mexico 3.3 8.2 6.1 4.4 3.86.1 569.5 1.5 26.2 79.10.2% 1.4% 0.2% 0.2% 1.4%0.0% 0.2% 0.1% 0.1% 0.9%

EEC 3.3 8.2 6.1 4.4 3.82,973.8 85,682.5 755.4 13,109.1 3,532.7

77.9% 216.8% 120.1% 94.2% 64.2%13.5% 30.4% 26.0% 30.7% 42.3%

Japan 1.8 5.0 3.7 2.6 2.2682.1 18,869.1 293.9 4,548.5 943.8

17.9% 47.7% 46.7% 32.7% 17.1%3.1% 6.7% 10.1% 10.6% 11.3%

SAS 9.5 12.8 17.2 8.9 10.34,642.0 8,528.9 1,696.6 3,002.8 2,998.1

121.7% 21.6% 269.7% 21.6% 54.5%21.1% 3.0% 58.5% 7.0% 35.9%

NOTE: The information on each cell is the following:

row 1: Proportional Increase of Bilateral Trade.row 2: Value of Such Increase in Millions USA $.row 3: Ratio of Row 2 over Total Trade.row 4: Ratio of Row 2 over GNP.

*Bi1atera1 Trade, Total Trade and GNP are average of 1990-91 values,except for Nepal where 1989-90 values were used.

29

Table 11.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -Total Trade

Bangladesh India Nepal Pakistan Sri LankaPARTNERS

USA 7 -1 20 -2 14 -2 10 -1 8 -15,065 -880 112,924 -12,301 999 -129 16,325 -2,492 5,951 -971132.8%- 23.1% 285.7%- 31.1% 158.8%-20.5% 117.4%- 17.9% 108.1%- 17.6%23.1%- 4.0% 40.0%- 4.4% 34.4%- 4.5% 38.2%- 5.8% 71.3%- 11.6%

Canada 7 -1 20 -2 14 -2 10 -1 8 -1715 -124 9,309 -1,014 46 -6 1,532 -234 406 -66

18.7%- 3.3% 23.6%- 2.6% 7.3%- 0.9% 11.0%- 1.7% 7.4%- 1.2%3.3%- 0.6% 3.3%- 0.4% 1.6%- 0.2% 3.6%- 0.5% 4.9%- 0.8%

Mexico 7 -1 20 -2 14 -2 10 -1 8 -112 -2 1,394 -152 4 -0 56 -9 165 -27

0.3%- 0.1% 3.5%- 0.4% 0.6%- 0.1% 0.4%- 0.1% 3.0%- 0.5%0.1%- 0.0% 0.5%- 0.1% 0.1%- 0.0% 0.1%- 0.0% 2.0%- 0.3%

EEC 7 -1 20 -2 14 -2 10 -1 8 -16,033 -1,048 209,698 -22,842 1,730 -224 28,088 -4,288 7,362 -1,201158.1%- 27.5% 530.6%- 57.8% 275.0%-35.6% 201.9%- 30.8% 133.7%- 21.8%

27.5%- 4.8% 74.3%- 8.1% 59.7%- 7.7% 65.7%- 10.0% 88.2%- 14.4%

Japan 3 -1 12 -1 8 -1 5 -1 4 -11,280 -261 43,678 -5,267 634 -91 9,113 -1,581 1,830 -344

33.6%- 6.8% 110.5%- 13.3% 100.8%-14.5% 65.5%- 11.4% 33.2%- 6.2%5.8%- 1.2% 15.5%- 1.9% 21.9%- 3.2% 21.3%- 3.7% 21.9%- 4.1%

SAS 27 -2 39 -2 54 -3 24 -2 29 -213,208 -887 25,692 -1,462 5,273 -285 8,213 -640 8,543 -584

346.2%- 23.3% 65.0%- 3.7% 838.3%-45.2% 59.0%- 4.6% 155.1%- 10.6%60.1%- 4.0% 9.1%- 0.5% 181.8%- 9.8% 19.2%- 1.5% 102.3%- 7.0%



*Bilateral Trade, Total Trade and GNP are average of 1990-91 values, except for Nepalwhere 1989-90 values were used.

30

Table 12.A. SIMULATED EFFECTS OF PTA -Expected Values -Textile Fibres


USA 6.8 27.0 17.2 10.6 8.6160.8 579.6 0.0 173.6 19.5

4.2% 1.5% 0.0% 1.2% 0.4%0.7% 0.2% 0.0% 0.4% 0.2%

Canada 6.8 27.0 17.2 10.6 8.67.4 22.3 2.5 10.0 0.00.2% 0.1% 0.4% 0.1% 0.0%0.0% 0.0% 0.1% 0.0% 0.0%

Mexico 21.3 79.7 51.3 32.3 26.30.0 1.0 0.0 59.1 0.00.0% 0.0% 0.0% 0.4% 0.0%0.0% 0.0% 0.0% 0.1% 0.0%

EEC 6.8 27.0 17.2 10.6 8.682.2 1,887.9 4.4 988.7 82.0

2.2% 4.8% 0.7% 7.1% 1.5%0.4% 0.7% 0.2% 2.3% 1.0%

Japan 3.9 16.6 10.4 6.3 5.018.3 857.3 11.3 512.2 40.5

0.5% 2.2% 1.8% 3.7% 0.7%0.1% 0.3% 0.4% 1.2% 0.5%

SAS 483.4 1,867.3 3,994.9 304.2 930.938,221.2 33,012.4 2,109.3 23,198.1 11,886.3

1001.9% 83.5% 335.3% 166.8% 215.9%174.1% 11.7% 72.7% 54.3% 142.4%




~!J}~

31

Table l2.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -

Textile Fibres


USA 13 -3 68 -7 40 -6 22 -4 17 -4303 -71 1,468 -161 0 -0 359 -66 39 -87.9%- 1.8% 3.7%- 0.4% 0.0%- 0.0% 2.6%- 0.5% 0.7%- 0.1%1.4%- 0.3% 0.5%- 0.1% 0.0%- 0.0% 0.8%- 0.2% 0.5%- 0.1%

Canada 13 -3 68 -7 40 -6 22 -4 17 -414 -3 56 -6 6 -1 21 -4 0 -0

0.4%- 0.1% 0.1%- 0.0% 0.9%- 0.1% 0.1%- 0.0% 0.0%- 0.0%0.1%- 0.0% 0.0%- 0.0% 0.2%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

Mexico 45 -8 222 -19 130 -14 75 -11 58 -90 -0 3 -0 0 -0 137 -20 0 -0

0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 1.0%- 0.1% 0.0%- 0.0%0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.3%- 0.0% 0.0%- 0.0%

EEC 13 -3 68 -7 40 -6 22 -4 17 -4155 -36 4,780 -523 10 -1 2,047 -378 162 -344.1%- 0.9% 12.1%- 1.3% 1.6%- 0.2% 14.7%- 2.7% 2.9%- 0.6%0.7%- 0.2% 1.7%- 0.2% 0.3%- 0.0% 4.8%- 0.9% 1.9%- 0.4%

Japan 7 -2 41 -5 24 -3 13 -2 10 -234 -8 2,127 -240 25 -4 1,047 -193 79 -16

0.9%- 0.2% 5.4%- 0.6% 4.0%- 0.6% 7.5%- 1.4% 1.4%- 0.3%0.2%- 0.0% 0.8%- 0.1% 0.9%- 0.1% 2.5%- 0.5% 0.9%- 0.2%

SAS 1,802 -65 7,931 -166 18,151 -287 1,014 -51 3,766 -99142,465 -5,112 140,209 -2,934 9,583 -151 77,305 -3,923 48,083 -1,265

3734.3%-134.0% 354.8%- 7.4% 1523.6%-24.1% 555.8%- 28.2% 873.3%- 23.0%648.8%- 23.3% 49.7%- 1.0% 330.4%- 5.2% 180.9%- 9.2% 576.1%- 15.2%




32

Table l3.A. SIMULATED EFFECTS OF PTA -Expected Values -

Fuels


USA 4.6 22.7 13.5 7.7 6.00.4 9,243.9 43.8 341.1 0.60.0% 23.4% 7.0% 2.5% 0.0%0.0% 3.3% 1.5% 0.8% 0.0%

Canada 4.6 22.7 13.5 7.7 6.00.0 83.7 0.0 102.9 0.00.0% 0.2% 0.0% 0.7% 0.0%0.0% 0.0% 0.0% 0.2% 0.0%

Mexico 4.6 22.7 13.5 7.7 6.00.0 55.6 0.0 0.0 0.00.0% 0.1% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

EEC 4.6 22.7 13.5 7.7 6.010.2 5,218.7 1.9 298.5 45.70.3% 13.2% 0.3% 2.1% 0.8%0.0% 1.8% 0.1% 0.7% 0.5%

Japan 4.6 22.7 13.5 7.7 6.014.7 982.2 2.5 219.7 36.00.4% 2.5% 0.4% 1.6% 0.7%0.1% 0.3% 0.1% 0.5% 0.4%

SAS 4.6 7.7 13.5 7.7 6.816.8 174.3 26.5 130.9 0.20.4% 0.4% 4.2% 0.9% 0.0%0.1% 0.1% 0.9% 0.3% 0.0%



*Bilateral Trade, Total Trade and GNP are average of 1990-91 values,except for Nepal where 1989-90 values were used.

33

Table l3.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95%Fuels


USA 11 -1 80 -3 42 -2 21 -2 15 -11 -0 32,659 -1,112 137 -7 934 -68 2 -0

0.0%- 0.0% 82.6%- 2.8% 21.8%- 1.1% 6.7%- 0.5% 0.0%- 0.0%0.0%- 0.0% 11.6%- 0.4% 4.7%- 0.2% 2.2%- 0.2% 0.0%- 0.0%

Canada 11 -1 80 -3 42 -2 21 -2 15 -10 -0 296 -10 0 -0 282 -20 0 -0

0.0%- 0.0% 0.7%- 0.0% 0.0%- 0.0% 2.0%- 0.1% 0.0%- 0.0%0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.7%- 0.0% 0.0%- 0.0%

Mexico 11 -1 80 -3 42 -2 21 -2 15 -10 -0 196 -7 0 -0 0 -0 0 -0

0.0%- 0.0% 0.5%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

EEC 11 -1 80 -3 42 -2 21 -2 15 -125 -2 18,438 -628 6 -0 817 -59 118 -10

0.6%- 0.1% 46.7%- 1.6% 0.9%- 0.0% 5.9%- 0.4% 2.1%- 0.2%0.1%- 0.0% 6.5%- 0.2% 0.2%- 0.0% 1.9%- 0.1% 1.4%- 0.1%

Japan 11 -1 80 -3 42 -2 21 -2 15 -136 -4 3,470 -118 8 -0 601 -44 93 -8

0.9%- 0.1% 8.8%- 0.3% 1.2%- 0.1% 4.3%- 0.3% 1.7%- 0.1%0.2%- 0.0% 1.2%- 0.0% 0.3%- 0.0% 1.4%- 0.1% 1.1%- 0.1%

SAS 11 -1 21 -2 42 -2 21 -2 18 -141 -4 482 -34 83 -4 358 -26 0 -0

1.1%- 0.1% 1.2%- 0.1% 13.2%- 0.7% 2.6%- 0.2% 0.0%- 0.0%0.2%- 0.0% 0.2%- 0.0% 2.9%- 0.1% 0.8%- 0.1% 0.0%- 0.0%



*Bilateral Trade, Total Trade and GNP are average of 1990-91 values, except for Nepal where 1989-90 values were used.

.

34

Table 14.A. SIMULATED EFFECTS OF PTA -Expected Values -Non-Fuel Primaries


USA 3.1 10.1 6.9 4.6 3.8419.4 7,463.7 27.9 1,604.8 344.411.0% 18.9% 4.4% 11.5% 6.3%

1.9% 2.6% 1.0% 3.8% 4.1%

Canada 3.1 10.1 6.9 4.6 3.8216.2 1,297.6 1.8 94.2 37.7

5.7% 3.3% 0.3% 0.7% 0.7%1.0% 0.5% 0.1% 0.2% 0.5%

Mexico 4.3 13.1 9.1 6.2 5.10.2 109.2 0.7 8.9 6.50.0% 0.3% 0.1% 0.1% 0.1%0.0% 0.0% 0.0% 0.0% 0.1%

EEC 3.1 10.1 6.9 4.6 3.8382.7 8,167.8 24.0 1,102.9 575.710.0% 20.7% 3.8% 7.9% 10.5%

1.7% 2.9% 0.8% 2.6% 6.9%

Japan 3.1 10.1 6.9 4.6 3.8119.3 11,316.7 4.4 267.4 145.1

3.1% 28.6% 0.7% 1.9% 2.6%0.5% 4.0% 0.2% 0.6% 1.7%

SAS 21.0 31.9 40.1 23.4 20.11,069.1 4,491.1 1,269.6 2,170.6 1,793.7

28.0% 11.4% 201.8% 15.6% 32.6%4.9% 1.6% 43.8% 5.1% 21.5%




35

Table l4.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -Non-Fuel Primaries


USA 6 -1 25 -3 16 -2 10 -2 8 -1807 -165 18,191 -2,063 63 -9 3,321 -569 687 -129

21.2%- 4.3% 46.0%- 5.2% 10.0%- 1.4% 23.9%- 4.1% 12.5%- 2.3%3.7%- 0.7% 6.4%- 0.7% 2.2%- 0.3% 7.8%- 1.3% 8.2%- 1.5%

Canada 6 -1 25 -3 16 -2 10 -2 8 -1416 -85 3,163 -359 4 -1 195 -33 75 -14

10.9%- 2.2% 8.0%- 0.9% 0.7%- 0.1% 1.4%- 0.2% 1.4%- 0.3%1.9%- 0.4% 1.1%- 0.1% 0.1%- 0.0% 0.5%- 0.1% 0.9%- 0.2%

Mexico 9 -1 34 -3 22 -3 14 -2 11 -20 -0 283 -27 2 -0 20 -3 14 -2

0.0%- 0.0% 0.7%- 0.1% 0.3%- 0.0% 0.1%- 0.0% 0.3%- 0.0%0.0%- 0.0% 0.1%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.2%- 0.0%

EEC 6 -1 25 -3 16 -2 10 -2 8 -1737 -150 19,907 -2,258 54 -8 2,283 -391 1,149 -215

19.3%- 3.9% 50.4%- 5.7% 8.6%- 1.2% 16.4%- 2.8% 20.9%- 3.9%3.4%- 0.7% 7.1%- 0.8% 1.9%- 0.3% 5.3%- 0.9% 13.8%- 2.6%

Japan 6 -1 25 -3 16 -2 10 -2 8 -1230 -47 27,582 -3,128 10 -1 553 -95 290 -546.0%- 1.2% 69.8%- 7.9% 1.6%- 0.2% 4.0%- 0.7% 5.3%- 1.0% Y.,!"'.-1.0%- 0.2% 9.8%- 1.1% 0.3%- 0.0% 1.3%- 0.2% 3.5%- 0.6% c~

""

SAS 79 -2 128 -3 161 -4 87 -3 72 -24,039 -104 17,965 -384 5,102 -116 8,073 -236 6,451 -211105.9%- 2.7% 45.5%- 1.0% 811.2%-18.4% 58.0%- 1.7% 117.2%- 3.8%

18.4%- 0.5% 6.4%- 0.1% 175.9%- 4.0% 18.9%- 0.6% 77.3%- 2.5%




36

Table IS.A. SIMULATED EFFECTS OF PTA -Expected Values -Leather, Dressed Fur., etc


USA 0.8 1.9 1.5 1.1 0.90.8 112.8 0.0 16.9 0.10.0% 0.3% 0.0% 0.1% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

Canada 0.8 1.9 1.5 1.1 0.90.2 8.7 0.0 1.1 0.00.0% 0.0% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

Mexico 0.8 1.9 1.5 1.1 0.90.0 0.3 0.2 0.2 0.00.0% 0.0% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

EEC 0.8 1.9 1.5 1.1 0.969.1 684.6 6.6 135.0 1.4

1.8% 1.7% 1.1% 1.0% 0.0%0.3% 0.2% 0.2% 0.3% 0.0%

Japan 0.8 1.9 1.5 1.1 0.99.6 37.3 0.1 15.0 0.30.3% 0.1% 0.0% 0.1% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

SAS 0.8 1.1 1.5 1.1 0.90.8 10.7 0.5 10.6 2.00.0% 0.0% 0.1% 0.1% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%




37

Table ls.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -Leather, Dressed Fur., etc


USA 2 -0 6 -0 4 -0 3 -0 2 -02 -0 338 -0 0 -0 45 -0 0 -0

0.1%- 0.0% 0.9%- 0.0% 0.0%- 0.0% 0.3%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0%

Canada 2 -0 6 -0 4 -0 3 -0 2 -01 -0 26 -0 0 -0 3 -0 0 -0

0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

Mexico 2 -0 6 -0 4 -0 3 -0 2 -00- 0 1- 0 1- 0 1- 0 0- 0

0.0%- 0.0% 0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

EEC 2 -0 6 -0 4 -0 3 -0 2 -0172 -0 2,049 -0 19 -0 356 -0 4 -04.5%- 0.0% 5.2%- 0.0% 3.0%- 0.0% 2.6%- 0.0% 0.1%- 0.0%0.8%- 0.0% 0.7%- 0.0% 0.6%- 0.0% 0.8%- 0.0% 0.0%- 0.0%

Japan 2 -0 6 -0 4 -0 3 -0 2 -024 -0 112 -0 0 -0 40 -0 1 -0

0.6%- 0.0% 0.3%- 0.0% 0.0%- 0.0% 0.3%- 0.0% 0.0%- 0.0%0.1%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0%

SAS 2 -0 3 -0 4 -0 3 -0 2 -02 -0 28 -0 1 -0 28 -0 5 -0

0.1%- 0.0% 0.1%- 0.0% 0.2%- 0.0% 0.2%- 0.0% 0.1%- 0.0%0.0%- 0.0% 0.0%- 0.0% 0.1%- 0.0% 0.1%- 0.0% 0.1%- 0.0%




38

Table l6.A. SIMULATED EFFECTS OF PTA -Expected Values -Textile Yarn


USA 4.7 10.8 8.2 6.1 5.4217.3 3,877.4 26.0 1,415.5 100.1

5.7% 9.8% 4.1% 10.2% 1.8%1.0% 1.4% 0.9% 3.3% 1.2%

Canada 4.7 10.8 8.2 6.1 5.420.1 384.0 1.0 290.9 15.1

0.5% 1.0% 0.2% 2.1% 0.3%0.1% 0.1% 0.0% 0.7% 0.2%

Mexico 3.5 8.3 6.2 4.6 4.03.0 17.1 0.2 5.4 0.00.1% 0.0% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

EEC 4.7 10.8 8.2 6.1 5.4396.6 7,924.9 540.8 3,106.4 199.8

10.4% 20.1% 86.0% 22.3% 3.6%1.8% 2.8% 18.6% 7.3% 2.4%

Japan 1.9 4.9 3.6 2.6 2.234.1 511.8 3.1 1,214.2 85.40.9% 1.3% 0.5% 8.7% 1.6%0.2% 0.2% 0.1% 2.8% 1.0%

SAS 14.2 17.2 27.0 9.9 13.12,682.7 2,981.4 140.6 844.8 737.1

70.3% 7.5% 22.4% 6.1% 13.4%12.2% 1.1% 4.8% 2.0% 8.8%




39

Table l6.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95%Textile Yarn


USA 10 -1 29 -2 20 -2 14 -2 12 -2471 -70 10,316 -886 64 -7 3,260 -414 224 -31

12.3%- 1.8% 26.1%- 2.2% 10.2%- 1.1% 23.4%- 3.0% 4.1%- 0.6%2.1%- 0.3% 3.7%- 0.3% 2.2%- 0.2% 7.6%- 1.0% 2.7%- 0.4%

Canada 10 -1 29 -2 20 -2 14 -2 12 -244 -6 1,022 -88 2 -0 670 -85 34 -5

1.1%- 0.2% 2.6%- 0.2% 0.4%- 0.0% 4.8%- 0.6% 0.6%- 0.1%0.2%- 0.0% 0.4%- 0.0% 0.1%- 0.0% 1.6%- 0.2% 0.4%- 0.1%

Mexico 7 -1 21 -2 15 -2 10 -1 9 -16 -1 44 -4 0 -0 12 -2 0 -0

0.2%- 0.0% 0.1%- 0.0% 0.1%- 0.0% 0.1%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

EEC 10 -1 29 -2 20 -2 14 -2 12 -2860 -127 21,084 -1,811 1,339 -140 7,154 -909 446 -61

22.5%- 3.3% 53.4%- 4.6% 212.9%-22.3% 51.4%- 6.5% 8.1%- 1.1%3.9%- 0.6% 7.5%- 0.6% 46.2%- 4.8% 16.7%- 2.1% 5.3%- 0.7%

Japan 4 -1 12 -1 8 -1 6 -1 5 -168 -11 1,282 -115 7 -1 2,598 -357 176 -27

1.8%- 0.3% 3.2%- 0.3% 1.1%- 0.1% 18.7%- 2.6% 3.2%- 0.5%0.3%- 0.1% 0.5%- 0.0% 0.2%- 0.0% 6.1%- 0.8% 2.1%- 0.3%

SAS 41 -3 52 -3 87 -4 26 -2 37 -37,785 -525 8,954 -542 454 -23 2,214 -201 2,082 -154204.1%- 13.8% 22.7%- 1.4% 72.2%- 3.6% 15.9%- 1.4% 37.8%- 2.8%

35.5%- 2.4% 3.2%- 0.2% 15.7%- 0.8% 5.2%- 0.5% 24.9%- 1.8%




40

Table l7.A. SIMULATED EFFECTS OF PTA -Expected Values -Machines and Transport Equipment


USA 0.9 2.4 1.8 1.3 1.122.3 1,936.2 6.3 354.0 23.5

0.6% 4.9% 1.0% 2.5% 0.4%0.1% 0.7% 0.2% 0.8% 0.3%

Canada 0.9 2.4 1.8 1.3 1.13.1 181.5 0.8 18.8 2.00.1% 0.5% 0.1% 0.1% 0.0%0.0% 0.1% 0.0% 0.0% 0.0%

Mexico 0.9 2.4 1.8 1.3 1.10.0 26.2 0.0 0.1 0.10.0% 0.1% 0.0% 0.0% 0.0%0.0% 0.0% 0.0% 0.0% 0.0%

EEC 0.9 2.4 1.8 1.3 1.1153.4 5,556.9 41.7 1,037.9 156.8

4.0% 14.1% 6.6% 7.5% 2.8%0.7% 2.0% 1.4% 2.4% 1.9%

Japan 0.9 2.4 1.8 1.3 1.1162.7 2,129.4 67.1 1,096.3 201.3

4.3% 5.4% 10.7% 7.9% 3.7%0.7% 0.8% 2.3% 2.6% 2.4%

SAS 0.9 1.1 1.8 1.1 1.154.9 144.3 29.5 7.6 59.21.4% 0.4% 4.7% 0.1% 1.1%0.3% 0.1% 1.0% 0.0% 0.7%




..

41

Table 17.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -Machines and Transport Equipment


USA 2 -0 7 -0 5 -0 3 -0 3 -052 -2 5,494 -134 17 -0 879 -31 57 -2

1.4%- 0.1% 13.9%- 0.3% 2.6%- 0.1% 6.3%- 0.2% 1.0%- 0.0%0.2%- 0.0% 1.9%- 0.0% 0.6%- 0.0% 2.1%- 0.1% 0.7%- 0.0%

Canada 2 -0 7 -0 5 -0 3 -0 3 -07 -0 515 -13 2 -0 47 -2 5 -0

0.2%- 0.0% 1.3%- 0.0% 0.3%- 0.0% 0.3%- 0.0% 0.1%- 0.0%0.0%- 0.0% 0.2%- 0.0% 0.1%- 0.0% 0.1%- 0.0% 0.1%- 0.0%

Mexico 2 -0 7 -0 5 -0 3 -0 3 -00 -0 74 -2 0 -0 0 -0 0 -0

0.0%- 0.0% 0.2%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

EEC 2 -0 7 -0 5 -0 3 -0 3 -0359 -15 15,767 -385 111 -3 2,578 -91 378 -149.4%- 0.4% 39.9%- 1.0% 17.6%- 0.5% 18.5%- 0.7% 6.9%- 0.3%1.6%- 0.1% 5.6%- 0.1% 3.8%- 0.1% 6.0%- 0.2% 4.5%- 0.2%

Japan 2 -0 7 -0 5 -0 3 -0 3 -0381 -16 6,042 -148 178 -5 2,723 -96 485 -18

10.0%- 0.4% 15.3%- 0.4% 28.4%- 0.8% 19.6%- 0.7% 8.8%- 0.3%1.7%- 0.1% 2.1%- 0.1% 6.2%- 0.2% 6.4%- 0.2% 5.8%- 0.2%

SAS 2 -0 3 -0 5 -0 3 -0 3 -0128 -5 352 -13 79 -2 19 -1 143 -53.4%- 0.1% 0.9%- 0.0% 12.5%- 0.4% 0.1%- 0.0% 2.6%- 0.1%0.6%- 0.0% 0.1%- 0.0% 2.7%- 0.1% 0.0%- 0.0% 1.7%- 0.1%



*Bi1atera1 Trade, Total Trade and GNP are average of 1990-91 values, except for Nepalwhere 1989-90 values were used.

42

Table 18.A. SIMULATED EFFECTS OF PTA -Expected Values -

Clothing


USA 7.1 40.7 23.0 12.5 9.53,385.3 29,699.0 1,227.3 3,685.0 4,823.8

88.7% 75.1% 195.1% 26.5% 87.6%15.4% 10.5% 42.3% 8.6% 57.8%

Canada 7.1 40.7 23.0 12.5 9.5155.8 3,272.0 32.8 458.4 211.5

4.1% 8.3% 5.2% 3.3% 3.8%0.7% 1.2% 1.1% 1.1% 2.5%

Mexico 7.1 40.7 23.0 12.5 9.54.7 110.5 0.0 3.7 3.40.1% 0.3% 0.0% 0.0% 0.1%0.0% 0.0% 0.0% 0.0% 0.0%

EEC 7.1 40.7 23.0 12.5 9.52,231.7 43,306.5 27.2 5,525.1 1,775.0

58.5% 109.6% 4.3% 39.7% 32.2%10.2% 15.3% 0.9% 12.9% 21.3%

Japan 7.1 40.7 23.0 12.5 9.56.2 3,798.8 5.7 134.4 112.80.2% 9.6% 0.9% 1.0% 2.0%0.0% 1.3% 0.2% 0.3% 1.4%

SAS 7.1 9.1 22.8 8.7 9.310.0 18.7 3.0 5.8 11.4

0.3% 0.0% 0.5% 0.0% 0.2%0.0% 0.0% 0.1% 0.0% 0.1%


row 1: Proportional Increase of Bilateral Trade.row 2: Value of Such Increase in Millions USA $.row 3: Ratio of Row 2 over Totel Trade.row 4: Ratio of Row 2 over GNP.


43

Table 18.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -

Clothing


USA 12 -4 92 -14 47 -9 23 -6 17 -55,769 -1,752 67,044 -10,292 2,522 -491 6,857 -1,697 8,591 -2,356151.2%- 45.9% 169.6%- 26.0% 400.9%-78.0% 49.3%- 12.2% 156.0%- 42.8%

26.3%- 8.0% 23.8%- 3.6% 87.0%-16.9% 16.0%- 4.0% 102.9%- 28.2%

Canada 12 -4 92 -14 47 -9 23 -6 17 -5265 -81 7,386 -1,134 67 -13 853 -211 377 -1037.0%- 2.1% 18.7%- 2.9% 10.7%- 2.1% 6.1%- 1.5% 6.8%- 1.9%1.2%- 0.4% 2.6%- 0.4% 2.3%- 0.5% 2.0%- 0.5% 4.5%- 1.2%

Mexico 12 -4 92 -14 47 -9 23 -6 17 -58 -2 249 -38 0 -0 7 -2 6 -2

0.2%- 0.1% 0.6%- 0.1% 0.0%- 0.0% 0.0%- 0.0% 0.1%- 0.0%0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.1%- 0.0%

EEC 12 -4 92 -14 47 -9 23 -6 17 -53,803 -1,155 97,762 -15,008 56 -11 10,281 -2,544 3,161 -867

99.7%- 30.3% 247.4%- 38.0% 8.9%- 1.7% 73.9%- 18.3% 57.4%- 15.7%17.3%- 5.3% 34.6%- 5.3% 1.9%- 0.4% 24.1%- 6.0% 37.9%- 10.4%

Japan 12 -4 92 -14 47 -9 23 -6 17 -511 -3 8,576 -1,317 12 -2 250 -62 201 -55

0.3%- 0.1% 21.7%- 3.3% 1.9%- 0.4% 1.8%- 0.4% 3.6%- 1.0%0.0%- 0.0% 3.0%- 0.5% 0.4%- 0.1% 0.6%- 0.1% 2.4%- 0.7%

SAS 12 -4 16 -4 47 -9 15 -4 17 -517 -5 33 -9 6 -1 10 -3 20 -6

0.4%- 0.1% 0.1%- 0.0% 1.0%- 0.2% 0.1%- 0.0% 0.4%- 0.1%0.1%- 0.0% 0.0%- 0.0% 0.2%- 0.0% 0.0%- 0.0% 0.2%- 0.1%



*Bi1atera1 Trade, Total Trade and GNP are average of 1990-91 values, except for Nepalwhere 1989-90 values were used.

44

Table 19.A. SIMULATED EFFECTS OF PTA -Expected Values -Other Manufactures


USA 3.0 7.5 5.6 4.1 3.5161.7 18,162.2 21.8 1,972.4 318.1

4.2% 46.0% 3.5% 14.2% 5.8%0.7% 6.4% 0.8% 4.6% 3.8%

Canada 3.0 7.5 5.6 4.1 3.522.2 919.2 4.9 106.9 34.00.6% 2.3% 0.8% 0.8% 0.6%0.1% 0.3% 0.2% 0.3% 0.4%

Mexico 3.0 7.5 5.6 4.1 3.50.8 315.2 0.0 3.5 0.40.0% 0.8% 0.0% 0.0% 0.0%0.0% 0.1% 0.0% 0.0% 0.0%

EEC 3.0 7.5 5.6 4.1 3.5348.5 34,950.3 136.9 2,845.3 1,148.8

9.1% 88.4% 21.8% 20.5% 20.9%1.6% 12.4% 4.7% 6.7% 13.8%

Japan 1.5 4.3 3.1 2.2 1.8192.2 6,017.5 124.5 580.5 241.3

5.0% 15.2% 19.8% 4.2% 4.4%0.9% 2.1% 4.3% 1.4% 2.9%

SAS 13.2 17.7 21.7 12.6 14.91,085.2 2,440.6 801.0 254.9 765.2

28.4% 6.2% 127.3% 1.8% 13.9%4.9% 0.9% 27.6% 0.6% 9.2%




45

Table 19.B. SIMULATED EFFECTS OF PTA -Confidence Interval at 95% -Other Manufactures


USA 6 -1 19 -2 13 -2 9 -1 7 -1331 -55 45,545 -4,498 51 -6 4,291 -613 671 -1038.7%- 1.4% 115.2%- 11.4% 8.1%- 1.0% 30.9%- 4.4% 12.2%- 1.9%1.5%- 0.3% 16.1%- 1.6% 1.8%- 0.2% 10.0%- 1.4% 8.0%- 1.2%

Canada 6 -1 19 -2 13 -2 9 -1 7 -146 -8 2,305 -228 11 -1 233 -33 72 -11

1.2%- 0.2% 5.8%- 0.6% 1.8%- 0.2% 1.7%- 0.2% 1.3%- 0.2%0.2%- 0.0% 0.8%- 0.1% 0.4%- 0.0% 0.5%- 0.1% 0.9%- 0.1%

Mexico 6 -1 19 -2 13 -2 9 -1 7 -12 -0 790 -78 0 -0 8 -1 1 -0

0.0%- 0.0% 2.0%- 0.2% 0.0%- 0.0% 0.1%- 0.0% 0.0%- 0.0%0.0%- 0.0% 0.3%- 0.0% 0.0%- 0.0% 0.0%- 0.0% 0.0%- 0.0%

EEC 6 -1 19 -2 13 -2 9 -1 7 -1714 -118 87,645 -8,656 320 -38 6,191 -884 2,424 -374

18.7%- 3.1% 221.8%- 21.9% 50.9%- 6.1% 44.5%- 6.4% 44.0%- 6.8%3.2%- 0.5% 31.1%- 3.1% 11.0%- 1.3% 14.5%- 2.1% 29.0%- 4.5%

Japan 3 -1 10 -1 7 -1 5 -1 4 -1375 -64 14,442 -1,476 278 -34 1,208 -177 486 -779.8%- 1.7% 36.5%- 3.7% 44.3%- 5.4% 8.7%- 1.3% 8.8%- 1.4%1.7%- 0.3% 5.1%- 0.5% 9.6%- 1.2% 2.8%- 0.4% 5.8%- 0.9%

SAS 42 -2 59 -2 74 -3 39 -2 48 -23,442 -164 8,139 -334 2,737 -107 787 -42 2,468 -113

90.2%- 4.3% 20.6%- 0.8% 435.1%-17.1% 5.7%- 0.3% 44.8%- 2.1%15.7%- 0.7% 2.9%- 0.1% 94.4%- 3.7% 1.8%- 0.1% 29.6%- 1.4%




46

APPENDIX A: VARIABLES IN THE GRAVITY MODEL

Table A.I. COUNTRY GROUPS AND CITIES USED IN THE GRAVITY MODEL'S ESTIMATION

I GROUP OF COUNTRIES I CITIES I

~ Bangladesh DaccaIndia Bombay-Calcutta-MadrasNepal KathmanduPakistan KarachiSri-Lanka Colombo

~ Canada OttawaEEC 6 Paris-RomeEFTA Vienna-StockholmJapan TokyoUsa ChicagoRest OECD Sydney

Other Groups AFRICA AlgiersASIA ManilaRest EUROPE Budapest-AlkaraLATIN AMERICA Caracas-Sao PauloMIDDLE EAST Riyadh

Other China BeijingCountries Hong Kong Hong Kong

Korea KoreaMexico Mexico CitySingapore Singapore

COUNTRY AGGREGATION: Analogies with IMF International Financial Statistics

AFRICA Africa (IFS)

ASIA Asia (IFS) excluding: SAS's Countries, China, Hong Kong,Korea and Singapore

MIDDLE EAST Middle East (IFS)

LATIN AMERICA Western Hemisphere (IFS) excluding Mexico

Rest EUROPE Europe (IFS)

Rest OECD Industrial Countries (IFS) excluding: Canada, EEC6,EFTA, Japan and USA

47

Table A.2. TARIFFS ON COMMODITY GROUPS USED IN THE ESTIMATION

SITC Rev. 1 TOTAL 070 260 300 TNFP 610 650 700 840 TM

Countries

Bangladesh 22% 22% 22% 22% 22% 22% 22% 22% 22% 22%India 42% 42% 42% 42% 42% 42% 42% 42% 42% 42%Nepal 35% 35% 35% 35% 35% 35% 35% 35% 35% 35%Pakistan 28% 28% 28% 28% 28% 28% 28% 28% 28% 28%Sri-Lanka 25% 25% 25% 25% 25% 25% 25% 25% 25% 25%

Canada 13% 2% 3% 0% 4% 10% 17% 6% 24% 10%EEC 6 13% 2% 3% 0% 4% 10% 17% 6% 24% 10%EFTA 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Japan 2% 7% 0% 2% 4% 14% 3% 0% 5% 0%Usa 13% 2% 3% 0% 4% 10% 17% 6% 24% 10%R. OECD 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

AFRICA 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%ASIA 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%R. EUROPE 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%L. AMERICA 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%MID. EAST 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

China 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%H. Kong 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Korea 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%Mexico 13% 14% 10% 0% 9% 10% 12% 14% 20% 10%Singapore 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

DESCRIPTION OF COMMODITIES

TOTAL Total Trade

070 Coffee, Tea, Cocoa and Spices

260 Textile Fibres

300 Fuels

TNFP Non-Fuel Primaries (except coffee, tea, cocoa and spices, andTextile Fibres)

610 Leather, Dressed Fur., etc.

650 Textile Yarn, Fabric, etc.

700 Machines and Transport Equipment

840 Clothing

TM Other Manufactures

,48

APPENDIX B. SUMMARY OF SOUTH ASIA ACTUAL TRADE PATTERN

Table B.l. BILATERAL TRADE IN SOUTH ASIA


Total Trade 3,815 39,520 629 13,910 5,506

USA 762 5,600 71 1,717 74620.0% 14.2% 11.3% 12.3% 13.5%

Canada 107 462 3 161 512.8% 1.2% 0.5% 1.2% 0.9%

Mexico 2 69 0 6 210.0% 0.2% 0.0% 0.0% 0.4%

EEC 907 10,399 123 2,954 92323.8% 26.3% 19.6% 21.2% 16.8%

Japan 378 3,748 80 1,776 4369.9% 9.5% 12.8% 12.8% 7.9%

SAS 489 664 99 337 29012.8% 1.7% 15.7% 2.4% 5.3%

NOTE: The information in each cell is the following:

row 1: Bilateral Trade, average 1990-91 in millions of dol1ars*.row 2: Share of Bilateral Trade on Total Trade.

*Nepa1 data is 1989-90.

49

Table B.2. SOUTH ASIA IMPORTS BY COMMODITY

Bangladesh India Nepal Pakistan Sri LankaCOMMODITY

Total Trade 2,274 21,654 465 7,916 3,247

Coffee, Tea, etc. 17 12 8 194 190.7% 0.1% 1.6% 2.5% 0.6%

Textile Fibres 200 230 23 195 388.8% 1.1% 5.2% 2.5% 1.1%

Fuels 517 6,168 49 1,529 38322.7% 28.6% 10.7% 19.4% 11.9%

Non-Fuel Primaries 395 2,274 45 1,248 60617.4% 10.4% 9.5% 15.9% 18.9%

Leather, Dressed Fur. 3 77 0 8 70.1% 0.4% 0.0% 0.1% 0.2%

Textile Yarn 195 186 29 128 5928.6% 0.8% 6.1% 1.6% 17.8%

Machines and T. Equip. 538 3,414 110 2,400 61923.7% 15.6% 23.7% 30.0% 19.0%

Clothing 43 2 7 0 141.9% 0.0% 1.4% 0.0% 0.4%

Other Manufactures 365 9,293 194 2,215 96816.1% 43.1% 41.7% 28.0% 30.0%


row 1: Imports for the specific commodity, average 1990-91 in millionsof do11ars*.

row 2: Ratio of row lover total imports.


50

Table B.3. SOUTH ASIA EXPORTS BY COMMODITY

Bangladesh India Nepal Pakistan Sri LankaCOMMODITY

Total Trade 1,541 17,866 155 5,993 2,259

Coffee, Tea, etc. 37 794 2 19 5812.4% 4.4% 0.9% 0.3% 26.1%

Textile Fibres 116 329 1 506 197.5% 1.8% 0.9% 8.5% 0.9%

Fuels 11 472 0 83 130.7% 2.6% 0.0% 1.4% 0.6%

Non-Fuel Primaries 228 3,134 16 646 25515.0% 17.5% 9.9% 10.8% 11.5%

Leather, Dressed Fur. 100 742 8 292 136.5% 4.2% 5.4% 4.9% 0.5%

Textile Yarn 310 2,167 72 2,865 3720.2% 12.1% 46.7% 47.8% 1.6%

Machines and T. Equip. 1 1,318 0 19 470.1% 7.4% 0.0% 0.3% 2.0%

Clothing 688 2,720 47 1,190 86044.4% 15.2% 30.1% 19.9% 37.5%

Other Manufactures 50 6,190 10 372 4353.2% 34.6% 6.1% 6.2% 19.4%


row 1: Exports for the specific commodity, average 1990-91 in millionsof do11ars*.

row 2: Ratio of row lover total exports.


51

APPENDIX C. SUMMARY OF NTB ON SOUTH ASIA TRADE -MOST AFFECTED COMMODITIES-

TOTAL TRADE

I % coverage I USA I CAN I MEX I EEC I JPN I

l%freq.-1 I I I I I

BGD large 97.2 0.0 62.0 89.9large 88.6 0.0 51.5 47.5

IND large 61.7 20.1 52.8 26.8large 46.1 9.8 29.6 39.4

NPL large 41.2 0.0 5.1 51.1large 38.5 0.0 9.1 7.1

PAK large 76.4 3.4 71.2 13.9large 64.7 1.5 48.5 33.3

LKA large 80.4 86.9 42.6 43.5large 53.5 25.0 41.8 35.8

COFFEE, TEA, COCOA AND SPICES


I%freq.~ I I I I I I

BGD 0.0 0.0 0.0 0.0 100.00.0 0.0 0.0 0.0 100.0

IND 0.0 98.4 100.0 0.0 100.00.0 95.1 100.0 5.6 100.0

NPL 0.0 0.0 0.0 0.0 100.00.0 0.0 0.0 0.0 100.0

PAK 0.0 98.9 0.0 1.5 100.00.0 94.1 0.0 9.1 100.0

LKA 0.0 100.0 100.0 0.0 100.00.0 100.0 100.0 3.6 100.0

52

TEXTILE FIBRES


I%freq.-' I I I I I

BGD 0.0 100.0 0.0 0.0 0.00.0 100.0 0.0 0.0 0.0

IND 0.0 12.1 0.0 1.5 0.00.0 25.0 0.0 7.5 0.0

NPL 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0

PAK 0.0 0.0 0.0 0.0 1.20.0 0.0 0.0 9.1 8.3

LKA 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0

NON-FUEL PRIMARY (EXCEPT COFFEE, TEA, COCOA AND SPICES)


l%freq.-1 I I I I I

BGD 0.0 99.7 0.0 12.2 100.00.0 66.7 0.0 36.2 91.7

IND 0.0 62.1 99.9 17.7 39.10.0 48.4 93.3 33.5 66.9

NPL 0.0 100.0 0.0 1.0 98.70.0 100.0 0.0 14.3 33.3

PAK 0.0 91.7 100.0 56.5 92.00.0 50.0 100.0 39.0 78.6

LKA 0.0 32.2 0.0 10.7 79.00.0 19.4 0.0 36.4 69.1

53

LEATHER, DRESSED FUR., ETC


l%freq.-1 I I I I I

BGD 0.0 100.0 0.0 99.8 68.70.0 100.0 0.0 90.9 63.6

IND 0.0 71.5 0.0 92.0 87.00.0 71.4 0.0 82.7 68.0

NPL 0.0 0.0 0.0 100.0 33.30.0 0.0 0.0 100.0 33.3

PAK 0.0 97.6 0.0 89.4 98.40.0 70.0 0.0 85.4 76.9

LKA 0.0 0.0 0.0 42.1 0.00.0 0.0 0.0 85.7 0.0

TEXTILE YARN, FABRIC, ETC


l%freq.-1 I I I I I

BGD 0.0 91.1 0.0 4.6 63.40.0 54.5 0.0 56.9 26.3

IND large 52.8 0.0 82.9 33.3large 79.1 0.0 86.5 49.0

NPL 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0

PAK large 62.9 0.0 99.9 2.5large 68.9 0.0 94.0 24.2

LKA 0.0 0.0 0.0 83.4 10.00.0 0.0 0.0 82.5 42.9

54

CLOTHING

, % coverage' USA I CAN I ME}{ I EEC I JPN I

I%freq.-' I I I I I

BGD large 100.0 0.0 99.6 0.0large 100.0 0.0 87.8 0.0

IND large 93.6 0.0 94.5 0.0large 89.7 0.0 84.7 0.0

NPL large 100.0 0.0 0.1 0.0large 100.0 0.0 1.9 0.0

PAK large 96.4 0.0 91.2 0.0large 88.4 0.0 84.9 0.0

LKA large 90.8 0.0 96.3 0.0large 91.7 0.0 89.0 0.0

..

,

55

APPENDIX D: Expected Value and Confidence Intervals of the Estimated

Bilateral Trade Increase

From equation (1) it follows (denoting the situation under a PTA with an

asterisk and omitting all subscripts for clarity) that

Log(BT)-Log(BT.) =Log(E-) =a4Log(T1) + a sLog (T2) +u-u' (A)BT.

If we assume u and u* to be independently normally distributed with means

zero and variance u2, then BT/BT* is lognormally distributed and hence, using

E to denote the expectation operator,

E(E-) =exp(a4Log(Tl) +asLog(T2)+u2)-rJ (B)BT.

since the variance for u-u* is 2u2.

It is natural to estimate the expectation in B by

~ -exp(a4Log(Tl) + asLog(T2) + u2) where the A over a parameter is its estimate

from regression (1). If the regression is complete (i.e. the right-hand side

includes all the relevant variables) and its error term is uncorrelated with

the explanatory variables, the estimates of the parameters would be consistent

and unbiased. As such, the estimate ~ would be a consistent though biased

estimator of rJ. In fact, if the sample size is large enough for a normal

approximation to the distribution of parameter estimates to be appropriate,

E(~) = 11 exp[iVariance (a4Log(Tl) + asLog(T2) + u2)]. Thus ~ would overestimate

rJ.

Our regression is certainly not complete, and we have already

instrumented some of the explanatory variables to take into account their

possible correlations between the error term. As such our parameter

56

estimates, while consistent, are certainly not unbiased; and the above theory

is not quite applicable to the ij computed from the parameter estimates of our

regression. We therefore took a pragmatic approach and used (C) below to

calculate row 2 in each cell of Tables 11 to 19 in part A.

BT ..1-2 (C)(-) =exp(Ct4Log(T1) + Ct5Log(T2) +"7\"U1)BT. L

-2 2 -2 2 .2 -..where u1=Log(T1) ua4 + Log(T2) ua5 + 2 Log(T1) Log(T2) ua4ua5Pa4a5

We are using this estimator since its bias appears to be less than that of the

consistent estimator.

Since Log(BT/BT*) is an increasing function of BT/BT*, if [I,m]

represents an interval that includes, say, 95% percent of the distribution of

10g(BT/BT*) , then [exp(l),exp(m)] will also include 95% of the distribution of

BT/BT*. Now, Log(BT/BT*) is normally distributed with mean Ct4 Log(T1) +a5

Log(T2) and variance u2. However, following the comment above, we have only

consistent estimates for Ct4, Ct5 and u1 and with them we can calculate a

"confidence" interval for mean prediction8. These limits are as follows:

1=a4Log(T 1) +a5Log(T2)-1. 96 (,1 (D)

m=a4Log(T1) +a5Log(T2) +1. 96 (,1 (E)

so that (exp t ,exp ill ) yields the required interval and defines the ranges in

tables 11 to 19 part B.

~\Strictly speaking, we need to construct tolerance intervals (see T.N.Srinivasan (1979», but our large sample allows us to use confidenceintervals.

57

REFERENCES

Anderson, J. E. (1979), "A Theoretical Foundation for the Gravity Equation,"

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Bergstrand, J. H. (1989), "The Genera1ised Gravity Equation, Monopolistic

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Bergstrand, J. H. (1985), "The Gravity Equation in International Tocoomic

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i

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