Regional Integration and Free Trade Agreements

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CHAPTER 4 Trade Agreements and Intra-regional Exports

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correlation in two governments’ decision to form an FTA, but may not have correlation with their exports. As they point out, past studies, such as by Jaggers and Gurr (1999), Mansfield et al. (2002), and Kaufmann et al. (2003) reflect the fact that two countries are more inclined to form FTAs if their governments are more democratic. Therefore, three governance indicators15 have been selected as z variables from the World Bank’s Governance and Anti-Corruption, as F Test suggests that they are jointly significant. These are: (i) Voice and Accountability (VOICE), (ii) Rule of Law (RULAW), and (iii) Control of Corruption (NOCORPT). VOICE represents citizens’ democratic rights, and it measures the extent to which citizens of a country can participate in the decisionmaking process of the government. RULAW is the principle that governmental authority is legitimately exercised in accordance with written, publicly disclosed laws adopted and enforced in accordance with established procedure. The principle is intended to be a safeguard against arbitrary governance. NOCORPT is a concept that is meant to repress any organized, interdependent system in which part of the system is either not performing duties it was originally intended to, or performing them in an improper way, to the detriment of the system’s original purpose. The variables are measured in terms of percentile rank (0 to100)—zero representing as the lowest and 100 as the highest.

4.5 EMPIRICAL RESULTS A.

Pooled OLS Estimation (Benchmark)

Table 4.2 presents the estimation results for pooled OLS data model. The following findings emerge from the estimation. The estimated coefficients values are conventional and quite stable across all sub-periods. The coefficients for gross domestic product (GDP), population (POPN), depreciation rate of real bilateral exchange rate (DREX), distance (DIST), border (BORD), currency (CURR), trade agreement (TRAG), conflict (CONF) and membership in other regional trade group (MEMB) are statistically significant and in general the signs are as expected. While countries with seaports (PORT) have positive and significant coefficient, landlocked (LLOCK) and island countries (ILAND) show negative and/or insignificant impacts on exports in general, which are also as expected. The negative coefficient for MEMB is as expected and supports the findings of Pitigala (2005, p. 42), since SAARC members are not so much considered as “natural trading partners” because most SAARC members demonstrate a tendency to trade outside the region. In contrast to our expectations, the coefficient for DIST is counterintuitive and statistically significant at 10 percent significance level in the sub-period 1. One principal reason for this can be attributed to the fact that the closest neighbors, particularly India, Pakistan and Bangladesh were hostile to each other during the period on account of the 1971 Bangladesh Liberation War (Mukti Juddho) or what is also 15 The governance indicators data is obtained from The World Bank official site, Governance and Anticorruption. Available from http://info.worldbank.org/governance/kkz2005/tables.asp (retrieved June 20, 2006).

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PART III Gravity Model

known as the Indo-Pakistan War of 1971. Hence, trade plummeted sharply among these countries during the period. The only two countries that were engaged in formal trade were Nepal and Sri Lanka, which are the most distant countries in the region. Therefore, in this case, the result cannot be interpreted in a causal manner. TABLE 4.2 GRAVITY EQUATION ESTIMATES FOR POOLED OLS MODEL (BENCHMARK) Specification

Sub-period 1 Pre-SAARC I (1971-1979) 2.51*** (4.68) 2.91*** (4.57) 1.99*** (3.04) 1.59* (1.85) 0.77 (1.37) 1.65 (1.57)

Sub-period 2 Sub-period 3 Sub-period 4 Sub-period 5 Pre-SAARC Post-SAARC Post-SAARC SAARC Regressors II I II I+II (1980-1984) (1985-1994) (1995-2005) (1985-2005) 1.84*** 1.86*** 2.23*** 1.74*** ln(GDPjt) (11.86) (25.01) (24.48) (29.28) ln(POPNit) 2.37*** 1.77*** 1.54*** 1.43*** (8.84) (15.53) (11.99) (15.06) ln(DREXijt) 0.65 0.45 1.19*** 1.47*** (1.00) (0.21) (2.30) (3.50) ln(DISTij) -2.63*** -3.654*** -4.52*** -3.42*** (-2.73) (-9.21) (-10.89) (-11.07) BORDij 0.61 0.28* 0.37*** 0.41*** (1.52) (1.71) (2.11) (3.15) LANGij -2.17* -2.13 -1.45 -1.55* (-1.72) (-1.57) (-1.24) (-1.85) CURRijt 2.99*** 2.40*** 2.14*** 2.02*** (4.40) (6.89) (8.99) (8.61) TRAGijt -0.39*** -0.99*** 0.14 0.57*** 0.59*** (-3.31) (-2.01) (0.73) (3.86) (5.11) CONFijt -0.86*** -0.61*** -0.75** -0.61** -0.67** (-4.02) (-6.09) (-2.10) (-1.99) (-2.45) MEMBijt 0.03 -0.88*** -1.19*** -0.81*** -0.84*** (0.04) (-2.05) (-7.05) (-3.98) (-5.71) LLOCKij 0.62 -3.01*** -0.30 -1.06*** -0.38 (0.72) (-3.72) (-0.83) (-2.64) (-1.29) ILAND ij 0.46 0.71 0.04 0.14 -0.05 (0.57) (1.05) (0.14) (0.50) (-0.20) PORTij -1.88*** 3.10*** 2.02*** 2.61*** (-3.76) (14.49) (8.60) (14.56) Constant -59.19*** -24.27*** -20.55*** -20.57*** -18.59*** (-4.80) (-5.56) (-14.31) (-12.90) (-14.91) Observations 180 210 462 420 882 RMSE 2.23 2.05 1.30 1.38 1.41 2 R 0.50 0.67 0.81 0.80 0.77 Note: The regressand is the natural log of exports, ln(Xijt). Numbers in parenthesis are t-statistics. *, ** and *** indicate statistical significance at 10%, 5% and 1%, respectively. Source: Author’s estimation.

The coefficients for LLOCK and PORT are also more or less consistent to our expectations. However, the negative coefficient for PORT in the sub-period 2 could appear as result of repercussions of the war among three major nations in the earlier sub-period 1, as noted above. One result that attracts our attention is the negative and statistically significant coefficient for language (LANG) for the sub-period 2 through 5, while conventional wisdom tells us that it should have a positive coefficient. The reason goes back to none other than the above-mentioned interpretation, wherein most of the trading partners in the region with similar languages have been exhibiting animosity against each other due


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to history of war and contentions, thereby resulting in less trade and nullifying the positive impact. Another parameter of interest is the coefficient for CONF, which is negatively associated with exports at 1 percent significance level against a two-sided alternative across all periods. In sub-period 1, the coefficient for CONF is -0.86, suggesting that the presence of conflict between two trading partners decreases exports by about 58 percent ( e −0.86 ).16 Similarly, in sub-period 2, sub-period 3, sub-period 4 and sub-period 5, the negative impact of CONF is reflected by the decrease in exports between the trading partners by about 46 percent, 53 percent, 46 percent and 49 percent, respectively. Given the scenario of hostility and incessant discord among SAARC members as already discussed above, this result is not surprising. Of special interest in this regression result is the coefficient for TRAG, which is negative and statistically significant at 1 percent level of significance for the sub-period 1 and sub-period 2, insignificant in the sub-period 3, and then positive and highly significant again in the sub-period 4 and sub-period 5. It is quite evident that before SAARC came into existence, intra-regional trade was much lower among the South Asian nations. Even after the inception of SAARC in 1985 (sub-period 3), the impact is not significant. However, the impact of TRAG can be clearly observed in the sub-period 4 and sub-period 5, i.e., after the SAPTA came into operation in 1995. For instance, in the case of sub-period 2, which is the period just before SAARC came into force, the coefficient of TRAG is -0.99, and the coefficient in sub-period 4, i.e., the period after SAPTA came into operation is 0.57. It implies that two trading partners had about 63 percent ( e −0.99 ) less exports in the sub-period 2. However, in the sub-period 4, exports increased by about 77 percent ( e 0.57 ). There is a further increase in exports in the subperiod 5 by about 80 percent ( e 0.59 ). These results indicate that the impact of trade agreements is time-dependent, and there is a time lag for actual effects to transpire, which is largely consistent with the findings by Baier and Bergstrand (2005). Furthermore, this strongly supports the case for deeper trade integration in South Asia. B.

Robustness Checks

Thus far, strong support for positive impact of trade agreements on exports was observed. This evidently supports the first hypothesis. However, one legitimate question that needs to be addressed is how robust our findings are. To check the robustness of the benchmark results, Table 4.3 through Table 4.5 presents estimates of some sensitivity analyses. In so doing, country and time dummies, first differencing, IV, and fixed as well as random effects models are tested.

16

Since [exp(-0.86)-1*100] = -57.68 percent.

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Country Dummy Effects With the introduction of country dummies in Table 4.3, GDP and POPN are still strongly associated with exports. The impact of DREX is found to be largely insignificant. This is because almost all SAARC countries follow a fixed exchange rate system and so the depreciation rates among these countries are negligible. Following the trend in Table 4.2, the coefficient for DIST is positive in the sub-period 1, but predominately negative and significant, which is typical as expected. The coefficient for BORD turns out to be insignificant and even negative in the sub-period 1. The estranged relationship between India and Pakistan two major countries in the region could yet again explain this phenomenon. Besides, although Bangladesh, Bhutan, and Nepal also share a common border, the gravity effect of these smaller countries have so little impact relative to huge economies of India and Pakistan. The coefficients for LANG, CURR, TRAG and CONF as well show very similar patterns as illustrated in the benchmark results in Table 4.2. The interpretation for this is not very different from what has been deliberated above. Nevertheless, a careful scrutiny demonstrates that the positive impact of TRAG has slightly weakened in the sub-period 4 and sub-period 5. In these specifications, the coefficient of TRAG is 0.34 and 0.38. This quantitative estimate suggests that after controlling for individual country effects, the impact of TRAG has about 41 percent ( e 0.34 ) and 46 percent ( e 0.38 ) increase in exports between country pairs in sub-period 4 and sub-period 5, respectively. On the other hand, CONF has a further negative impact on exports, as the exports during the sub-period 4 and sub-period 5 decreased by almost 59 percent ( e −0.90 ) and 51 percent ( e −0.72 ) , respectively. Except for the sub-period 2, PORT is statistically significant in the sub-period 3, sub-period 4 and sub-period 5. Interestingly, economically larger countries, particularly India, Pakistan and Bangladesh did not fare well in sub-period 1, displaying clearly the backlash of the war during the period. Nevertheless, beginning from the sub-period 3, i.e., soon after SAARC came into being, exports volume of these three countries picked up momentum with positive significant impacts on the regressand. As expected and characteristic to gravity effects, these two large countries (India and Pakistan) have strong positive impacts on exports, while small economies like Bhutan, Maldives and Nepal have negative impacts. This supports our second hypothesis. It is also interesting to note that during the sub-period 1 when all major players were at conflict, only Nepal and Sri Lanka fared well as they continued to have good terms of trade. During the period, DIST though positive, is not statistically different from zero, which further justifies this observation.


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TABLE 4.3 POOLED OLS WITH COUNTRY DUMMY EFFECTS Specification

Sub-period 1 Pre-SAARC I (1971-1979) 1.99*** (4.09) 1.07*** (5.62) 0.65 (0.94) 0.94 (1.61) -0.47 (-0.86) 1.30 (1.48)

Sub-period 2 Sub-period 3 Sub-period 4 Sub-period 5 Pre-SAARC Post-SAARC Post-SAARC SAARC I II I+II II Regressors (1980-1984) (1985-1994) (1995-2005) (1985-2005) ln(GDPjt) 1.89*** 1.86*** 2.37*** 1.89*** (11.96) (24.06) (24.51) (29.88) ln(POPNit) 1.01 -1.13** 1.77*** 1.42*** (1.04) (-1.96) (3.11) (6.08) ln(DREXijt) 2.87 0.28 0.07 0.24 (-0.51) (0.13) (-0.02) (0.13) ln(DISTij) -0.88*** -0.73*** -0.79*** -0.64*** (-2.99) (-9.60) (-12.64) (-12.17) BORDij 0.64 0.13 0.07 0.09 (1.37) (0.72) (0.32) (0.60) LANGij -2.23*** -2.10*** -1.33*** -1.59*** (-3.73) (-8.61) (-4.82) (-8.26) CURRijt 2.62*** 2.54*** 2.23*** 2.40*** (4.98) (6.49) (7.18) (9.18) TRAGijt -0.86*** -0.86** -0.01 0.34*** 0.38*** (-4.27) (-1.73) (-0.02) (2.12) (3.39) CONFijt -0.75*** -0.72*** -0.79*** -0.90*** -0.72*** (-4.04) (-6.74) (-10.79) (-11.51) (-12.06) PORTij -2.55*** 3.22*** 1.84*** 2.75*** (-5.26) (12.60) (5.80) (13.32) Bhutan -3.96 -5.49*** -2.98*** -4.80*** (-1.32) (-2.35) (-3.68) (-7.31) India -3.12*** 4.03 4.70*** 5.15*** 6.53*** (-5.22) (1.30) (2.89) (4.04) (7.91) -1.86*** -6.14*** -7.57*** Maldives -3.17 (-1.18) (-2.47) (-3.45) (-7.24) Nepal 6.60*** -3.43 -1.860 -7.67*** -4.15*** (5.31) (-1.58) (-1.303) (-3.61) (-5.457) Pakistan 0.17 1.59*** 1.17*** 1.32*** 1.03*** (0.30) (3.20) (6.91) (6.04) (7.02) Sri Lanka 2.54*** -2.57 -3.94*** -3.28*** -5.64*** (5.29) (-1.10) (-2.70) (-3.52) (-7.37) Constant -32.34*** 89.52 17.27 61.89*** 35.11*** (-6.03) (0.97) (1.20) (2.68) (4.76) Observations 180 210 462 420 882 RMSE 2.07 2.03 1.22 1.33 1.35 R2 0.57 0.68 0.82 0.81 0.79 Note: The regressand is the natural log of exports, ln(Xijt). Numbers in parenthesis are t-statistics. *, ** and *** indicate statistical significance at 10%, 5% and 1%, respectively. Source: Author’s estimation.

Time Dummy Effects Adding time dummies has little material effect on the estimated results.17 However, a few points worth mentioning are the insignificant impacts of DREX and BORD; and the inclusion of time lag for TRAG has a stronger positive impact especially in the subperiod 5. Inclusion of one lag for TRAG has an increased impact on coefficient from 0.47 to 0.49. With two lags, the coefficient value leaps from 0.49 to 0.50. In regards to coefficients of the CONF variable, the trend is very similar with significantly negative impacts on exports for all sub-periods, as was also the case in Table 4.2 and Table 4.3. 17

The result is not shown here for the purpose of brevity.

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The coefficient for DIST is positive and significant during the sub-period 1. The reason is that when the closest neighbors such as India, Pakistan and Bangladesh were estranged due to war during the period, these countries traded more vigorously with their distant neighbors instead, such as with Sri Lanka and the Maldives in order to compensate for the decreased trade. For instance, Pakistan’s exports to the Maldives increased threefold, and to Sri Lanka by almost twofold during the sub-period 1 as compared to the sub-period 2. In reciprocation, Sri Lanka’s exports to Pakistan increased by more than one and half times during the same period. Bangladesh’s exports to Sri Lanka grew by about three times during this period as compared to sub-period 2. Likewise, even India’s exports to Sri Lanka and Nepal rose comparatively higher during this period. Conversely, there was virtually no trade between India and Pakistan, nor between Pakistan and Bangladesh during the sub-period 1 until the beginning of the sub-period 2, while trade between the distant partners like Nepal and Sri Lanka were not affected.18 Country and Time Dummy Effects There is not much variation in the results even after controlling for both country and time dummy effects (not shown for brevity). In general, it is observed that the effects retain similar trend as seen in the earlier tables. GDP, POPN, DIST, LANG, CURR, TRAG and CONF are significant at conventional levels. However, the intensity of TRAG weakens slightly in comparison to the results in the previous tables. Also, India, Pakistan and Sri Lanka are significantly associated with exports especially in the subperiods 4 and 5, which further support the hypothesis number 2. First Differencing First differencing is one of many ways to eliminate fixed effects. It is a method where the regressand and the regressors are first-differenced by taking the difference of adjacent time periods, i.e., the earlier time period is subtracted from the later time period. This method is particularly useful when the unobserved factors that change over time are serially correlated. If ε ijt follows a random walk, meaning that there is very substantial positive serial correlation, then the difference Δε ijt is serially uncorrelated, and therefore, first differencing is a good alternative to solve this problem.19 In Table 4.4, focusing again on the TRAG and CONF variables, the coefficients retain the expected signs. TRAG has a highly significant positive impact on exports in the post-SAARC periods, i.e., in the sub-period 4 and sub-period 5. The estimates suggest that TRAG increases partner countries’ exports by about 127 percent ( e 0.82 ) in the sub-period 4 and about 132 percent ( e 0.84 ) in the sub-period 5. CONF, on the other hand, has a negative impact in all periods, and the coefficients are statistically 18

A careful examination of raw data clearly points out this fact. First differencing the panel data yields some potential advantages over fixed effects. See Wooldridge (2003, pp. 467-468) for further details. 19


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significant from the sub-period 3 through sub-period 6, but the impacts are lesser as compared to the earlier OLS estimates. TABLE 4.4 FIRST-DIFFERENCED GRAVITY EQUATION ESTIMATES Specification

Sub-period 1 Pre-SAARC I (1971-1979) 4.08*** (3.37) 0.76*** (2.14) 1.44*** (2.61) 0.76 (0.68) -2.15* (-1.88) 3.16 (0.93)

Sub-period 2 Sub-period 3 Sub-period 4 Sub-period 5 Pre-SAARC Post-SAARC Post-SAARC SAARC II I II I+II Regressors (1980-1984) (1985-1994) (1995-2005) (1985-2005) ln(GDPjt) 1.72*** 1.53*** 1.89*** 1.29*** (4.68) (6.28) (6.10) (5.37) ln(POPNit) 0.53* 0.70*** 0.34*** 0.85** (1.71) (3.22) (2.51) (1.97) ln(DREXijt) 4.51* 0.39 2.10*** 0.43 (1.90) (0.55) (2.64) (0.53) ln(DISTij) -2.07 -3.11*** -4.67*** -2.72* (-1.06) (-2.58) (-2.93) (-1.85) BORDij 0.56 0.78 1.56*** 2.10*** (0.37) (0.88) (2.23) (2.77) LANGij -0.97 -1.35* -1.59*** -1.43* (-1.20) (-1.76) (-2.44) (-1.77) CURRijt 0.29 0.44*** 0.51*** 0.14*** (0.36) (3.42) (2.54) (2.15) TRAGijt -0.46 -1.86* 0.69 0.82*** 0.84*** (-0.24) (-1.72) (1.18) (3.77) (2.87) CONFijt -0.71 -0.32 -0.66*** -0.68*** -0.54*** (-1.59) (-0.65) (-2.24) (-3.06) (-2.93) MEMBijt 1.25 -3.87* -2217*** -1.81 -2.58* (0.87) (-1.91) (-2.38) (-1.37) (-1.85) LLOCKij 0.02 -6.69*** -3.56** -3.41*** -3.20*** (0.05) (-3.17) (-1.97) (-3.16) (-2.11) ILAND ij 0.46 0.92* 0.97 0.06 0.51 (1.53) (1.83) (1.07) (0.53) (1.32) PORTij -2.22 2.22*** 1.89** 3.22*** (-0.85) (2.15) (1.97) (5.88) Constant 0.19 0.05 0.01 0.01 0.01 (1.00) (0.46) (0.38) (0.22) (0.20) Observations 180 210 462 420 882 RMSE 2.29 1.61 0.75 1.05 0.84 2 R 0.32 0.57 0.62 0.54 0.45 Note: The regressand is the natural log of exports, ln(Xijt). Numbers in parenthesis are t-statistics. *, ** and *** indicate statistical significance at 10%, 5% and 1%, respectively. Source: Author’s estimation.

IV and 2SLS Estimation Table 4.5 provides the results of the estimation using IV. A set of three z variables has been used that is likely to influence the formation of FTA and less likely to be correlated to the error term, ε ijt . The method of IV can be used to solve the problem of endogeneity of one or more explanatory variables. This method applies two staged least squares (2SLS or TSLS), which is second in popularity next to OLS for estimating linear equations in applied econometrics. Here, the author uses the IV method to solve two kinds of endogeneity problems: omitted variables and measurement error. In the omitted variables case, variables are held fixed when estimating the ceteris paribus effect of one or more of the observed explanatory variables. In the measurement error case, the effect of certain explanatory

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variables is estimated on the regressand, provided that one or more variables have been mismeasured. In both cases, the parameters of interest can be estimated by OLS. Moreover, the application of IV methods to independently pooled cross sections does not pose much difficulty. As with models estimated by OLS, time period dummy variables can be included to allow for aggregate time effects. These dummy variables are exogenous as time trend is exogenous, and they act as its own instruments. An additional advantage is that IV estimation can be combined with panel data methods, particularly first differencing, to consistently estimate parameters in the presence of unobserved effects and endogeneity in one or more time-varying explanatory variables (Wooldridge, 2003, pp. 511-512). TABLE 4.5 GRAVITY EQUATION ESTIMATES USING IV Specification

(1) (2) (3) (4) (5) (6) With First With First With With no With With Country Differencing Differencing Country Regressors Time Country and Time and no Time and Time and Time Dummies Dummies Dummies Dummies Dummies Dummies ln(GDPjt) 2.23*** 2.37*** 2.28*** 2.37*** 1.90*** 1.89*** (24.48) (23.55) (23.62) (23.49) (6.17) (6.10) ln(POPNit) 1.54*** -8.77*** 1.64*** -10.25*** -0.32 -0.34 (11.99) (-3.55) (11.73) (-2.04) (-0.47) (-0.51) ln(DREXijt) 7.19*** -0.07 -1.64 0.27 5.78*** 6.10*** (2.30) (-0.02) (-0.41) (0.06) (2.46) (2.64) ln(DISTij) -4.52*** -4.79*** -4.59*** -4.79*** -4.70*** -4.67*** (-10.89) (-11.67) (-11.20) (-11.65) (-2.95) (-2.93) BORDij 0.37*** -0.07 0.24 -0.07 1.55*** 1.56*** (2.11) (-0.21) (0.81) (-0.20) (2.21) (2.23) LANGij -1.45*** -1.33*** -1.44*** -1.33*** -1.61*** -1.60*** (-5.24) (-5.25) (-5.65) (-5.25) (-2.46) (-2.44) CURRijt 2.14*** 2.23*** 2.26*** 2.22*** 0.51 0.51 (8.99) (5.04) (5.21) (5.03) (0.54) (0.54) TRAGijt 0.57*** 0.34* 0.50*** 0.34* 0.81* 0.82* (3.87) (1.78) (2.73) (1.79) (1.78) (1.77) CONFijt -0.81*** -0.90*** -0.83*** -0.90*** -0.69*** -0.69*** (-10.66) (-8.24) (-7.67) (-8.23) (-3.08) (-3.06) MEMBijt -0.81*** -1.32*** -0.82*** -1.37*** 1.78 1.81 (-3.98) (-4.916 (-3.86) (-4.45) (1.34) (1.37) LLOCKij -1.06*** -8.99*** -0.94 -10.10*** -3.43*** -3.41*** (-2.64) (-4.52) (-1.64) (-2.63) (-3.23) (-3.16) ILAND ij 0.14 -27.46*** 0.27 -31.40*** -4.04*** -4.06*** (0.50) (-4.11) (0.85) (-2.33) (-2.53) (-2.53) PORTij 2.02*** 1.84*** 2.06*** 1.84*** 1.86 1.89 (8.60) (3.57) (4.16) (3.57) (1.47) (1.47) Constant -20.57*** 63.22*** 19.08*** 36.39 -23.89 0.01 (-12.90) (3.13) (3.40) (0.44) (-0.63) (0.22) Observations 420 420 420 420 420 420 RMSE 1.38 1.33 1.35 1.34 1.05 1.05 2 2SLS R 0.80 0.81 0.81 0.81 0.54 0.54 Note: The regressand is the natural log of exports, ln(Xijt). Numbers in parenthesis are t-statistics. *, ** and *** indicate statistical significance at 10%, 5% and 1%, respectively. Instrument variables used are: (i) Voice and Accountability (VOICE), (ii) Rule of Law (RULAW), and (iii) Control of Corruption (CORRP). Source: Author’s estimation.


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Six different scenarios are tested, and it is found that the results obtained by using these z variables do not deviate much from the earlier results. The impact of TRAG is still positive and significant at conventional levels, while the impact of CONF is also negative and statistically significant. Nevertheless, in specification 5 with the first differencing, TRAG has a significant impact on exports yielding an increase in exports by about 125 percent ( e 0.81 ) and in specification 6 by almost 127 percent ( e 0.82 ), as analogous to first differencing. Hausman Test is applied to compare the OLS and 2SLS estimates and to determine whether the differences are statistically significant. This procedure tests the null hypothesis ( H 0 ) that the error term ε ijt of the OLS and the error term of the 2SLS (say, v2 ) are not correlated. The test fails to reject the H 0 concluding the exogeneity of z variables because ε ijt and v2 are not correlated. In addition, the testing of overidentifying restrictions used in the 2SLS suggests that the model is just identified (see Appendix A10 for procedure). Thus, the impact of TRAG in the last two specifications is consistent and reliable. 20 Similar results from the first differencing method further justify the consistency of the estimates. Fixed and Random Effects Models F Test was conducted to test whether or not the fixed effects coefficients were equal by comparing the sum of squared residuals (SSR) from the fixed effects and random effects

models. The computed p-value of zero soundly rejected the H 0 of equal intercepts. In addition, p-values of the Hausman Test were essentially zero for almost all periods, and so the H 0 of the random effects model in favor of the fixed effects model is rejected. As in the pooled OLS model, one could observe that almost all the coefficients of GDP and POPN for both the fixed effects model and random effects model were statistically significant and positive, while the coefficient of DIST is statistically significant with a conventional negative coefficient. The coefficient of DREX was largely insignificant in the case of both fixed and random effects. Which Estimation to Choose? All the same, it is indeed not easy to compare statistically the validity and preference for all these models. For basic determination of the results, the OLS and 2SLS estimations are considered, as our principal interest lies in observing the impact of TRAG and CONF on exports. Moreover, the OLS and 2SLS models embraces all the explanatory variables that are likely to affect exports, and considering exogeneity with country and time fixed effects, first differencing and the use of z variables, the results are consistent.

20 Wooldridge (2003, p. 514) notes that when instruments are poor, which means that they are correlated with the error term, and only weakly correlated with the endogenous explanatory variables or both, 2SLS estimates can be even worse than OLS. However, if the variables are exogenous, then the results of both OLS and 2SLS are consistent.

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Even though quantitative differences among these models may be observed, they are not very different in qualitative terms. Finally, in this study, the preference for panel estimation is given to fixed effects rather than random effects for two reasons, as also noted by Baier and Bergstrand (2007). First, it is on conceptual economic grounds that the source of endogeneity bias in the gravity equation is believed to be unobserved heterogeneity. That is, in economic terms there are unobserved effects or time-invariant variables (say, aij ) that influence simultaneously the presence of FTA and volume of exports. These variables are best controlled for using bilateral fixed effects, as such an approach allows for arbitrary correlations of aij with TRAGij. In contrast, under random effects, no correlation is assumed between unobserved effects aij with the explanatory variable TRAGij in each time period, which seems much less likely. Second, recent econometric evaluations of the gravity equation with panel data using the Hausman Test to test for random effects against fixed effects find high evidence for rejection of random effects model relative to fixed effects model. C.

Effects of SAPTA vis-à-vis TRAG

More often than not, analysts have raised debates that SAPTA has not been the main vehicle for enhancing intra-SAARC trade. This conflicting argument calls for further empirical testing. As the earlier tests showed that the impact of TRAG is mostly seen in the sub-period 4 and sub-period 5, i.e., the period after SAPTA came into operation. This gives high possibility for us to conclude that the major impact on exports could have arisen because of the SAPTA, and at the same time, undermine the role of TRAG as such. In order to unravel this paradox, three more tests were carried out using SAPTA as one of the dummy regressors (see Table 4.6). Interestingly, the coefficient for SAPTA (despite being positive) does not show significant impact at conventional significance levels even in the post-SAARC periods,21 and as we move backwards in time, the effect of SAPTA is further diluted or rather negatively associated with exports especially for those countries that do not have bilateral trade agreements. On the other hand, it is fascinating to observe that TRAG has a significant impact during the later two periods from 1985-2005 and from 1980-2005. Although the impact of TRAG also diminishes as we move backwards towards the earlier period, the impact is not statistically significant. This could simply mean that the impact of TRAG increased over time during the later sub-periods not specifically because of the inception of the SAPTA. In other words, SAPTA has not been the main vehicle for amplifying the impact of TRAG on exports in the later periods. Therefore, the increased intra-regional exports in the post-SAARC periods could have apparently

21 SAPTA, however, is significantly different from zero at 20 percent significance level during the period 1985-2005.


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stemmed from the delayed impact of the existing bilateral trade agreements among SAARC countries—besides the catalytic effect of SAPTA to some extent. TABLE 4.6 EFFECTS OF SAPTA VIS-A-VIS TRAG Impact of SAPTA Countries Countries with Trade without Agreements Trade Agreements (1985-2005) (1985-2005) Significant at Significant at 20% level of 1% level of significance, significance, e 0.11 = 11.63% e −0.84 = -56.83% (1980-2005) (1980-2005) Significant at Not 1% level of significant, significance, positive coefficient e −0.79 (1971-2005) Significant at 1% level of significance,

e −0.35

= -54.62% (1971-2005) Significant at 1% level of significance,

(1971-2005) Significant at 1% level of significance,

Impact of TRAG Countries Countries with and without without Trade Agreements Agreements (1985-2005) (1985-2005) (1985-2005) Significant at 1% level of significance, e 0.56 = 75.07% (1980-2005) (1980-2005) (1980-2005) Significant at 1% level of significance, e 0.41 = 50.68% (1971-2005) (1971-2005) (1971-2005) Significant at 1% level of significance,

e −0.95

e −0.25

e −0.56

Countries with and without Agreements (1985-2005) Not significant, negative coefficient (1980-2005) Not significant, negative coefficient

Countries with Trade Agreements

= -29.53% = -22.12% = -61.33% = -42.71% Note: Intercepts and coefficients for all standard covariates not reported for brevity and ease of presentation. SAPTA variable for certain periods are omitted to avoid collinearity. Source: Author’s estimation.

The above observation is further elaborated in the following Table 4.7. TABLE 4.7 COMPARATIVE LAGGED EFFECTS OF TRAG

Year

(1) With no Country and Time Dummies (1985-2005) Percent Increase

No. of Times Increase

Specification (2) (3) With Time With Country Dummies Dummies (1985-2005) (1985-2005) Percent Increase


Percent Increase

1986 4.73 1.05 6.19 1.06 6.79 1987 6.07 1.06 9.46 1.09 10.00 1990 15.75 1.16 22.86 1.23 27.27 1995 40.99 1.41 55.39 1.55 65.54 2000 36.67 1.37 57.66 1.58 52.45 2004 81.85 1.82 118.72 2.19 121.37 Note: The percentage increase is measured from the base year 1985. Source: Author’s estimation.

(4) With Country and Time Dummies (1985-2005)


Percent Increase


1.07 1.10 1.27 1.66 1.52 2.21

6.21 9.51 23.68 57.1 58.59 127.84

1.80 1.10 1.24 1.57 1.59 2.28

The table shows the comparative lagged effects of TRAG using country and time dummy effects, and the results are impressive. For instance, in specification (1), the impact of TRAG after 15 years (i.e., in 2000) from the inception of SAARC in 1985 is

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about 37 percent increase, which is an increase by 1.37 times. During the same year, the impact of TRAG is nearly 58 percent, 52 percent and 59 percent in specification (2), (3) and (4), respectively. However, in 2004, there is a dramatic increase in the TRAG effects from about 81 percent in specification (1) to almost 119 percent in specification (2), 121 percent (3) and 128 percent (4). That is, after 19 years, the impact of TRAG is more than twofold country and time dummy effects. 22 These results are indeed evocative and consistent with the findings of Baier and Bergstrand (2005), whose estimates suggest that an FTA on average doubles two member countries’ bilateral trade after 10 years.23 It may be noted that the overall percentage increase of intra-regional exports during the same time is 885 percent, i.e., after 19 years from 1985 to 2004 (see Table 4.8). If the cumulative average impact of TRAG during this time period in specifications (1), (2), (3) and (4) in 2004 is considered as 113 percent, then the independent role of the TRAG among different variables in enhancing intra-regional exports works out to be approximately 13 percent. Understandably, it took nearly two decades for TRAG to show reasonably clear impacts, which illustrates the sluggish nature of SAARC’s progress in terms of regional trade integration. TABLE 4.8 INTRA-SAARC EXPORTS, 1985-2004 Year US$ Million Elapsed No. of Years Percentage Increase 0.00 0 600.83 1985 -7.86 1 553.61 1986 2.32 2 614.76 1987 30.93 3 786.67 1988 43.51 4 862.25 1989 43.63 5 862.96 1990 68.63 6 1,013.15 1991 106.17 7 1,238.75 1992 98.31 8 1,191.47 1993 138.59 9 1,433.54 1994 236.81 10 2,023.65 1995 256.92 11 2,144.45 1996 261.82 12 2,173.94 1997 310.48 13 2,466.26 1998 262.83 14 2,180.00 1999 331.63 15 2,593.37 2000 370.47 16 2,826.68 2001 398.97 17 2,997.97 2002 694.46 18 4,773.32 2003 885.20 19 5,919.36 2004 Note: The percentage increase is measured from the base year 1985. Source: Author’s calculation using data from the UNCTAD Handbook of Statistics 2005.

22 One caveat is that this percentage increase measures only the cumulative effect of TRAG on total volume of exports. In other words, it is only that part of the role played by TRAG in enhancing exports, among many other variables. It should not be confused with an overall increase in the volume of intra-SAARC exports. 23 Using panel data, Rose (2004) estimated the impact of FTA using fixed effects and found 0.94 e or 156 percent, while Tomz (2004) estimated the FTA effect of e 0.76 or 114 percent.


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4.6 CONCLUSION This chapter focused on estimating a generalized form of gravity model to determine the impact of trade agreements on exports in the SAARC region. The gravity model performed well empirically yielding reasonably precise and good estimates, which are largely consistent with results of the earlier studies employing a gravity model and pooled trade data. For instance, the coefficients for GDP, POPN, DREX, DIST, BORD, CURR, TRAG, CONF and MEMB are statistically significant and in general the signs were as expected. While PORT has a positive and significant coefficient, LLOCK, ILAND and MEMB have negative and/or insignificant impacts on exports, which were also as anticipated. The fundamental question that arose from the results was whether trade agreements have had a significant positive impact on the volume of intra-regional exports of SAARC countries. The answer is yes, but one should interpret it with caution. This is because the empirical tests have found no evidence of the impact of trade agreements on exports in pre-SAARC I (sub-period 1 from 1971 to 1979), pre-SAARC II (sub-period 2 from 1980 to 1984), and SAARC I (sub-period 3 from 1985 to 1995). However, a significant positive impact of trade agreements on exports is observed in SAARC II or post-SAPTA period (sub-period 4 from 1995 to 2005) and for SAARC I+II (sub-period 5 from 1985 to 2005), even amidst sustained significant negative impact of CONF in all sub-periods. The phenomenon is observable irrespective of which estimation methods are applied. Thus, the results implicate positive benefits of having trade agreements and preferential arrangements among SAARC countries given that there is serious efforts from the SAARC members to palliate conflicts. The next important question was to ascertain whether the signing of the SAPTA has stimulated intra-SAARC trade in the region. Empirical tests find little evidence of the impact of SAPTA signaling a very modest role played by SAPTA in inducing trade creation within the region. This is because the test results show that the positive impact appears to have emanated not exclusively from the SAPTA per se, but as a coalesced effect arising from the delayed impact of the existing trade agreements among SAARC countries. The findings in this chapter definitely support the case for FTAs and further trade integration among SAARC member nations—clearly signalized by the positive impact of trade agreements seen in the post-SAARC periods. With the growing interest of observers around the world, SAARC will certainly find new opportunities, but one can only become more optimistic as SAFTA assuages SAPTA, and matures through further dismantling of both tariff and non-tariff barriers. The weakness of SAPTA can be compensated by shaping and sharpening the influence of SAFTA. Although there are undoubtedly good prospects to boost up future exports, this would, however, entail concerted efforts of the member nations to mitigate conflicts, evolve new comparative advantages and complementarities, aggregate with other regional blocs, and eliminate

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the existing impediments to intra-regional trade with the right perspective and affirmative political will. In addition, SAARC should take a more holistic and forward-looking approach by including deeper forms of integration in other trade facilitation measures, such as services, energy, institutional and infrastructure development, monetary and investment cooperation. In sum, it can be recapitulated that the future of SAARC countries depends, inter alia, not only on the level of economic integration, but it is also largely dictated by the political soundness in the region. Without easing political tensions, conflicts, and mistrust among the member nations, it is quite unlikely to hope for any substantive trade integration in the region. Thus, the growth of regional economic cooperation in South Asia calls for committed efforts and strong political will from all leaders to bring about peace, harmony, and social security in the region.

CHAPTER

5

Trade Creation and Trade Diversion Effects

L

ike in the previous chapter, this chapter also estimates the gravity model, but in a standard and augmented form. One of the main purposes of this chapter, among others, is to evaluate the trade creation and trade diversion effects of the bilateral trade agreements among SAARC countries. The chapter is organized as follows: Section 5.1 provides a brief introduction, and Section 5.2 explicates the theoretical considerations of the gravity model, trade creation and trade diversion effects, and pooled panel data framework. Section 5.3 discusses the empirical methodology, while Section 5.4 provides the sources of data. The regression results are examined in Section 5.5, and then Section 5.6 summarizes the key findings and concludes.

5.1 INTRODUCTION As mentioned earlier, the main aim of this Chapter is to reexamine using a standard and augmented gravity model to find any inconsistency in results by addressing the two analogous key questions as posed in the previous Chapter. That is, to gauge the extent of impact consequent to bilateral trade agreements among SAARC countries; and to investigate whether SAPTA has been a catalyst in enhancing the intra-bloc export creation, and if so, to what extent. The focus is, yet again, to recount the findings after testing the effects of trade agreements before the inception of SAARC and SAPTA in comparison with the post-SAARC and post-SAPTA periods. However, besides the variation in the model per se, the analysis in this part varies in three other respects. First, the data is harmonized for all sub-periods from 1980 to 2005. Second, an additional variable on the impact of India’s trade liberalization (INDLIB) is introduced. Third, this chapter also evaluates the trade creation and trade diversion effects of the bilateral agreements.

5.2 THEORETICAL FRAMEWORK Econometric studies that seek to investigate the impact of regional trade agreements (RTAs), preferential trading arrangments (PTAs), or free trade agreements (FTAs) are generally based on gravity models. The application of gravity equations to empirical analysis of international trade was pioneered by Tinbergen (1962), Linneman (1966), and Anderson (1979) among others. Over the last 40 years, the gravity model has been 127

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regarded as the “workhorse” and is at the core of international trade analyses, particularly in estimating the effects of FTAs on trade flows (Baier & Bergstrand, 2007, p. 73; Eichengreen & Irwin, 1998, p. 33). Despite criticisms for its lack of theoretical foundation, in many instances, gravity models have displayed significant explanatory power insofar as being referred to as a “fact of life” (Deardorff, 1995, p. 8). The gravity model in international trade utilizes the gravitational force concept as an analogy to predict the bilateral trade flows in terms of economic sizes (using GDP measurements) and distance between two countries. The relationship between the two countries is seen as corresponding directly to GDP, and inversely to distance. Using logarithms, the archetype formulation of the gravity model for an econometric analysis is given by

ln Tijt = β 0+ β1 ln Yit + β 2 ln Y jt + β 3 ln Dij + β 4 K ijt + λt + ε ijt ,

(1)

where Tijt is the bilateral trade flow between country pairs i and j at time t, Yit and

Y jt represent the national incomes of country i and country j at time t, and Dij is the bilateral distance. The equation can be handily augmented by adding policy variables, K ijt to account for other extraneous factors including in geography, history, culture, and economic policy variables that affect trade.1 The inclusion of time fixed effects, λt is fairly standard in use to eliminate bias resulting from aggregate trade shocks over time. The effect of policy covariates on trade flows is then assessed by estimating deviations from the baseline flows. The theoretical gap of the gravity equation has been opportunely filled by researchers who have incorporated various alternative trade theories and trade models. Subsequently, Anderson and van Wincoop (2003) took to refining the gravity equation by incorporating the concept of relative distance effect, while Evenett and Keller (2002) showed the versatility of the equation based on data availability. Many empirical applications have indeed preceded a solid theoretical explanation of why gravity equations seem to fit the data so well. Today, the model is being efficaciously applied in various fields of international relations to evaluate the impact of treaties and alliances on trade, or to test the potentiality of trade agreements and organizations, such as the NAFTA and the WTO. A.

Theory and Accounting for Trade Creation and Trade Diversion

Recent enhancement of the basic gravity model have given rise to extension with a series of dummy variables that capture the national welfare by way of trade creation and trade diversion effects of RTAs as auspicated by the signs and magnitudes of its coefficients. Trade creation and trade diversion effects are indeed part and parcel of all RTAs and one of the core discussions in international trade theory ever since Viner 1 Some examples of augmented gravity model include Boisso and Ferrantino (1997), Eichengreen and Irwin (1998), Frankel et al. (1995), Oguledo and MacPhee (1994), Rahman et al. (2006), and Rose (2004).

CHAPTER 5 Trade Creation and Trade Diversion Effects

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(1950) coined the two terms. Of late, trade theories covering RTAs, trade creation and trade diversion are as diverse as the literature and researchers in their quest for new empirical approaches. An earlier work of Pelzman (1977) demonstrates the combined effect of trade creation and trade diversion effects resulting in gross trade creation increasing from year to year with no reversals. He applied a modified gravity trade-flow model introducing a dummy preferential variable reflecting membership in the Council of Mutual Economic Assistance (CMEA). The value 2 is assigned to intra-CMEA trade while the value 1 is assigned to inter-CMEA trade flows. The difference between the actual intra-CMEA trade flows and the hypothetical inter-CMEA trade flows of CMEA’s preintegration structure is taken to be indicative of the gross trade creation effects. Subsequently, others in line suggest a two-stage estimation procedure. For example, Coulibaly (2004) exploits the cross-section dimension by using the pooled data and including the RTA variable in the second stage estimation. Pusterla (2007) goes a step further by introducing additional regional integration dummy variables as general indicators of membership. Ghosh and Yamarik (2004) provide the most extensive robustness analysis on trade creation/diversion effects of RTAs including dummies that capture PTA effects on bilateral trade. What follows in equation (2) is Eicher et al.’s (2008) work, which is a development upon Ghosh and Yamarik’s intuition. In equation (2), the matrix K ijt is split up into RTA dummies and other covariates, Z ijt .2 Hence, the new equation takes the form

ln Tijt = β 0 + β1 ln Yit + β 2 ln Y jt + β 3 ln Dij + β 4 Z ijt + β 5RTAijt + β 6 RTAit + λt + ε ijt , (2) wherein two sets of zero-one dummy variables are included to indicate whether two trading partners are members of the same RTA in year t ( RTAijt ), or whether only one trading partner has joined in year t ( RTAit ). These dummies enable us to isolate the three distinct RTA effects that may influence trade flows. A positive coefficient on

RTAijt captures trade creation among RTA members, while a negative coefficient on RTAit registers trade diversion. Next, an extra-bloc trade creation is simply given by the reverse of trade diversion, characterized by positive RTAit coefficients. In equation (3), country-pair fixed effects are supplemented in order to account for the possibility of natural trading partners. ~ ln Tijt = β 0 + β1 ln Yit + β 2 ln Y jt + β 3 ln Z ijt + β 4RTAijt + β 5 RTAit + δ ij + λt + ε ijt , (3) The term natural trading partner goes beyond the frontier of pure distance or trade costs arguments. It captures any of the similarities among trading partners that are 2

This is further discussed in Section 5.3.B using the actual dummies.

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constant over time. The country-pair fixed effects model is the most preferable formulation of the gravity equation that accounts for unobservable heterogeneity because omission of country-pair fixed effects would likely cause the PTA coefficients to be biased up, if they are prone to pick up trade creation due to deep-rooted endogeneity. Further, it may be noted that by controlling for country-pair fixed effects, one loses the ability to estimate the direct effect of time-invariant variables such as distance, but it is recaptured in a two-stage procedure where the first stage is given by (3) and the second stage uses the estimated fixed effects as regressand and time~ invariant variables as regressors. The matrix of controls is reduced to Z ijt , since the explanatory power of time-invariant regressors is absorbed by the country-pair fixed effects. The effect of the time-invariant, country-pair regressors is sacrificed temporarily to obtain less “contaminated” PTA coefficients (see Eicher, 2008, p. 7). An interesting aspect on the determination of trade welfare is that most of the studies show strong evidence of trade creation as opposed to trade diversion (e.g., Ghosh and Yamarik, 2004; Susanto et al., 1994). Baier and Bergstrand (2007) notably reason that countries with FTAs tend to have more trade generating welfare-enhancing net trade creation. Nevertheless, the model is not free from contradictions. For instance, Coulibaly (2004) find that all the developing RTAs in general are intra-bloc trade creating; some are net trade creating while others are net trade diverting, but in none of them did they find an irrefutable evidence of a net trade creation effect on all members. While Ghosh and Yamarik (2004) find that closely integrated RTAs generate more total trade creation, Pusterla (2007) find that complete integration agreements in particular were not the most trade-creating ones. Földvári (2006) observes that since gravity models are capable of capturing only the shift in the volume (or value) of trade flows which mostly consist of trade creation, this is a significant limitation. Eicher et al. (2008, p. 18) show that large trade creation is often an “artifact” of natural trading partners that join to form PTAs. An interesting account by Pusterla (2007) is that most empirical studies demonstrate strong evidence of trade creation customarily by way of varying specifications used in the gravity model equation, and much of the trade creating effects of RTAs corresponds to individual authors’ belief. Hence, this calls for a caveat that deserves bigger attention while interpreting results.

B.

Preference for Pooled Panel Data

Notwithstanding the potential weakness, investigators have oftentimes employed the gravity equation to measure trade creation and trade diversion effects with high satisfactoriness. Gravity models on international trade are indeed powerful in their ability to elucidate the correlation and explain a relatively large fraction of variations in the observed volume of trade (Subramanian and Wei, 2007). Structural gravity models are appealing because empirical estimation not only produces a well-fitting regression, but often provides robust econometric estimates. To all intents and purposes, many studies show that gravity models perform better with pooled panel dataset. Egger (2000)


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and Cheng and Wall (2005) assert that there is a potential advantage and preeminence of a panel framework over a cross-sectional analysis, for panels make it possible to capture the relationships among the relevant variables over a period of time, and to identify the role of overall business cycle trend. More significantly, they make it possible “to disentangle the time invariant country-specific effects” (Egger, 2000, p. 25). In principle, the use of panel data allows researchers to separate out the impact of scale economies from the impact of technological change. Two key advantages of the use of panel data make it prominent. First, panel data sets usually provide an increased number of data points, and that generates additional degrees of freedom. Second, incorporating information relating to both cross-section and time-series variables can substantially diminish the omitted-variables problem. Therefore, from a methodological perspective, the standard model augmented with a set of apposite control variables using a pooled panel data yields more robust results.

5.3 EMPIRICAL METHODOLOGY In light of the points as discoursed, a pooled panel data framework is adopted to estimate the parametric variations of coefficients in a set of regressors with a special focus on three RTA variables. Regressions are designed in such a way as to capture the effects of trade agreements with respect to pre-SAARC/pre-SAPTA and postSAARC/post-SAPTA periods. The sample is divided into five sub-periods in view of the variability of coefficients over time, because one would expect that, in such a long span of time there will be detectable period-specific changes in the results.3 A.

Model Specification

Accordingly, the augmented gravity equation in this paper assumes the following form:

ln( X ijt ) = β 0 + β1 ln GDPit + β 2 ln GDPjt + β 3 ln POPN it + β 4 ln POPN jt + β 5 ln DISTij + β 6 ln DREX ijt + β 7 BORDij + β 8 LANGij + β 9CURRijt + β10TRAG _ bijt + β11SAPTA _ iijt + β12ORTA _ eit + β13CONFijt

(4)

+ β14 LLOCKij + β15 ILANDij + β16 PORTij + β17 INDLIBijt + δ 2 C2 ... + δ 7C7 + λTt + ε ijt where, i, j and t stands for exporting country, importing country and time (year), respectively; Xijt denotes real exports from i to j at year t; GDPjt and GDPjt are the real GDP of country i and j in year t; 3 Pusterla (2007) notes that it is prudent to check for this variability by dividing the sample into different sub-periods (as in Rose 2004), or by plotting year by year all coefficients of at least regional integration dummy variables in order to observe their evolution through time.

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-

-

POPNit and POPNit are the population of i and j in year t; DISTij is the distance between the trade centres of i and j; DREXijt is the depreciation rate of the real bilateral exchange rate of i with respect to j in year t; BORDij is a binary/dummy variable which is unity if i and j share a common border and zero otherwise; LANGij is a binary variable which is unity if i and j share a common language and zero otherwise; CURRijt is a binary variable which is unity if i and j have a common currency in year t and zero otherwise; TRAGijt_b is a binary variable which is unity if there is a bilateral trade agreement between i and j in year t and zero otherwise; SAPTAijt_i is a binary variable for South Asian Preferential Trading Arrangement, which is unity if i and j are part of this agreement in year t and zero otherwise; ORTAit _e is a binary variable for membership in other RTA, which is unity if only one trading partner i is a member of an RTA in year t and zero otherwise; CONFijt is the index value for conflict between i and j in year t; LLOCKij is a binary variable which is unity if a country is landlocked and zero otherwise; ILANDij is a binary variable equal which is unity if a country is an island and zero otherwise; PORTij is a binary variable which is unity if a country has access to seaports and zero otherwise; INDLIBijt is a binary variable which is unity from the year India embarks on its trade liberalization policy and zero otherwise; C and T denote set of country-pair and time fixed dummies, respectively; β , δ and λ are vectors of nuisance coefficients; and

-

ε ijt represents the error term or any other omitted influences.

-

More specifically, a pooled OLS data model is estimated, but for robustness checks, the author also performs various sensitivity analyses employing country and time dummy effects, first differencing, instrument variables (IV hereafter), fixed effects, and random effects models. The parameters of interest are β10 , β11 , β12 , β13 and β17 . With the exception of binary variables, all other variables take on natural log values so as to narrow the range of variable and make the estimates less sensitive to outlying or extreme observations on the regressand and regressors. The novelty in this paper can be seen in that the effect of RTA variables are measured vis-à-vis conflict variable that might likely offset the frailty of results obtained by using simply the RTA variables as part of the regressors.


B.

133

Some Methodological Considerations

Since a comprehensive dataset is used over a long period of time, missing data is common concern. In order to overcome this problem, Pusterla (2007) notes the general practice is to assume all missing values to very small quantities of trade and thus transform them into zero trade values. But since a logarithmic transformation of the data leads all the zero values to again become the missing values, the new transformed dataset has a potential selection bias problem. To deal with this, the author follows Pusterla’s (2007) approach. After assuming that all missing value as equal to a very small number ( ≈ 0), first, one is added to the export variable ( X ij ) and then take its logarithmic form. In other words, the variable is log( X ijt ҙ+1), which means that it is equal to zero if X ij =0. Another typical issue is a measurement error problem in the regressors. This implies that OLS will no longer yield consistent estimators. To resolve this, the author resorts to IV that might likely have a correlation in two governments’ similar decision to form an FTA, but may not have correlation with their exports. The idea is evoked by Baier and Bergstrand (2007) and Coulibaly (2004). Kaufmann et al. (2003) also show that two countries are more inclined to form FTAs if their governments are more democratic. Hence, three governance indicators are selected as IV from the World Bank’s Governance and Anti-Corruption: (i) Voice and Accountability, (ii) Rule of Law, and (iii) Control of Corruption. F Test suggests that they are jointly significant confirming the validity as instruments.4 Earlier works point out that the most complete way to characterize RTA dummies is to differentiate intra- and extra-regional trade, i.e., to use dummies that can capture both trade-creating and trade-diverting effects (Cheng & Wall, 2005; Coulibaly, 2004; Pusterla, 2007). Eicher et al. (2008) also provide ample evidence that the analysis of preferential trade agreements must include dummies that pick up both trade creation and trade diversion to gauge the exact effects across PTAs. Hence, a set of three zero-one RTA dummy variables is introduced, viz., TRAGijt_b, SAPTAijt_i and ORTAit_e.5 These

( SSRr − SSRur ) / q , where SSRr is the sum of SSRur /(n − k − 1) squared residuals from the restricted model and SSRur is the sum of squared residuals from the unrestricted model; q = numerator degrees of freedom = df r − df ur ; n − k − 1 = denominator degrees of freedom = df ur ; and F ~ Fq ,n −k −1 . We reject the null 4

The F statistic (or F ratio) is defined by F =

β k − q+1 = 0,..., β k = 0) in favor of alternative hypothesis H 1 when F > c (c=critical value) depending upon the chosen significance level. The computed F = [(767.46-755.48)/3]/(755.48/402) ≈ 2.12; since this is above 10% critical value in the F distribution

hypothesis ( H 0 =

with 3 and 402 degrees of freedom, and so we reject the hypothesis that VOICE, RULAW, and CORRP have no effect on exports. 5 TRAG_b represents all bilateral FTAs that liberalize trade on a selective basis between a subset of SAARC members, while SAPTA_i is a plurilateral PTA among the members. ORTA_e represents extra-bloc trade creation (diversion) variable for those SAARC members having membership in other RTAs. ORTA_e under consideration are Bangkok Agreement, 1975; Bay of Bengal Initiative

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variables stand as proxies for both intra-regional trade and inter- or extra-regional trade effects, and they distinctly segregate RTA effects. TRAGijt_b and SAPTAijt_i can pick up the intra-bloc export creation effects, while ORTAit_e captures the extra-bloc export creation effects in all other RTAs. A positive coefficient of TRAG_bijt and SAPTAijt_i registers trade creation, whereas the negative coefficient of ORTAit_e suggests trade diversion. Finally, the net trade creation effect is arrived at by deducting the trade diversion effects of ORTAit_e, if any, from the gross trade creation given by the sum of the effects of TRAGijt_b and SAPTAijt_i. These variables are time-variant, meaning that they take the value of one from the year in which the country enters into the agreement. C.

Treating Endogeneity Bias and Heteroskedasticity

A growing body of literature on PTAs and FTAs examines the issue of endogeneity bias. Most of the studies use instruments that are controlled for by country-pair fixed effects (e.g., Anderson & van Wincoup, 2003; Cheng & Wall, 2005; Mátyás, 1997; Rose, 2004). Baier and Bergstrand (2007) distinguish a standard problem in cross-section empirical work—the potential endogeneity of right-hand side (RHS) variables. If, for instance, any of the RHS variables in equation (3) is correlated with the gravity equation error term, ε ijt , the variable is considered econometrically endogenous and the OLS may yield biased and inconsistent coefficient estimates. This simply means that TRAGijt and the intensity of domestic regulations could be positively correlated, but ε ijt and the intensity of domestic regulations could be negatively correlated. As a result, TRAGijt and ε ijt can be negatively correlated, and the TRAGijt coefficient will tend to be underestimated. Mátyás (1997), and Harris and Mátyás (1998) suggest a pooled timeseries of cross-sections or panel data in order to identify these biases and correctly specify the econometric model. They advocate the panel data to increase degrees of freedom, to enable identification of business cycle, and correctly account for exporting and importing country effects. Such effects can be treated as constants and estimated by fixed effects model in which one is able to identify separately the unobserved effects of those countries that have strong propensities to export and import, once divergences in other factors such as GDP, population, distance, etc. have already been accounted for. Cheng and Wall (2005) as well regard the introduction of the fixed effects model as a preferred candidate to avoid the long-standing measurement problem. These reasons underscore the importance of addressing the endogeneity bias. The author therefore recourses to a sequence of corrective procedures to mitigate the potential endogeneity bias resulting from a possible correlation between trade agreements and unobserved characteristics by introducing IV and fixed effects, and first differencing. The author additionally estimates a random effects model as considered in Harris and Mátyás’ work, where the unobserved effects is assumed to be uncorrelated

for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC), 1997; Economic Cooperation Organization, 1985; EU-India Cooperation Agreement, 1993.


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with the explanatory variables in each time period. All estimations employ heteroskedasticity consistent covariance matrix estimator derived by White (1980), which provides correct estimates of the coefficient covariances in the presence of heteroskedasticity of unknown form. D. Coefficient Predictions The basic premises of the model are as follows. The trade between two countries is proportionate to an increase in their real GDP. The real GDP of the country of origin (destination) is expected to have a positive effect on exports, as larger economy means larger demand (production). As such, β1 and β 2 are expected to be positive. The signs of the coefficients of the populations of the exporter, β 3 and importer, β 4 could be either positive or negative. In the past, they were expected to be positive because it was believed that larger countries, in general, trade more. However, it has more recently been shown that if the exporter is big in terms of population, it may either need its production to satisfy domestic demand so that it exports less (absorption effect), or it may export more as domestic enterprises achieve economies of scale. The same reasoning is applied in the case of the importing country. If a country is big, it may either import less because it is more self-sufficient or it may import more because it cannot satisfy its domestic demand with its own production (see Pusterla, 2007). The farther the distance between two countries, the higher the transportation costs and thus, lower the exports. Theoretically, distance is a proxy for transport costs, but it may also be that greater geographic distances are correlated with other costs such as synchronization costs, transaction costs, communication costs, and larger cultural differences. Cultural differences can impede trade in many ways, such as inhibiting communication, generating misunderstandings, clashes in negotiation styles, etc. This means that β 5 will be negative. In contrast, an increasing function when two countries share a common land border, common language and common currency means that, β 7 ,

β 8 and β 9 are expected to be positive. If the depreciation rate of the currency value of exporting country rises, then exports of that country are expected to increase implying that the sign of β 6 is predicted to be positive. Bilateral trade agreements and SAPTA may stimulate exports due to various tradeboosting efforts, such as reduction of tariff and non-tariff barriers among member countries. Accordingly, β10 and β11 are generally expected to be positive. However, some earlier studies showed mixed results. For example, Aitken (1973) finds the European Community (EC) to have an economically and statistically significant impact on trade flows among members, but Frankel et al. (1995) find insignificant impacts. Frankel (1997), too, shows flimsy results with positive significant impact from Mercosur bloc, insignificant impact from the Andean Pact, and significant negative impact from membership in the EC. Membership in other RTAs could affect negatively on intra-regional exports since countries are prone to trade outside the trade bloc, while

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conflict among trading partners is most likely to weaken trade; hence β12 and β13 are expected to be negative. Exports may fall for landlocked countries if they are disadvantaged by topography, remoteness or isolation, such that β14 is expected to be negative. Contrarily, island countries with access to seaports generally enhance trade, so

β15 and β16 are predicted to be positive. Lastly, India’s trade liberalization is likely to boost trade. Thus, β17 is expected to take a positive sign.

5.4 THE DATA The data for seven SAARC countries over a period from 1980-2005 comes from many sources. 6 Annual exports data (in million US$) have been compiled from various issues of the IMF’s Direction of Trade Statistics (DOTS). Missing exports data have been supplemented from the UN Comtrade. GDP (in million US$) and population data (in million) were obtained from the World Bank’s World Development Indicators (WDI). The distance data (in km) was calculated using the Great Circle Distance Between Capital Cities and time and date.com.7 The conflict data comes from Conflictbarometer 2005, Heidelberg Institute for International Conflict Research, University of Heidelberg. Other country-specific variables were taken from the CIA’s The World Factbook, SAARC’s official homepage, and relevant websites (see Appendix 11 for details of data source).

5.5 EMPIRICAL RESULTS A.

Pooled OLS Estimation

The benchmark results for pooled OLS data is reported in Table 5.1. There are not many surprises as far as the signs and magnitudes of the coefficients are concerned. The estimated coefficients values are conventional and stable across sub-periods. The signs of the coefficients for the countries’ GDPs, POPNs, DIST, BORD, CURR, and other geography and infrastructure variables, such as ILAND and PORT are as expected and are statistically significant; PORT has in fact a very significant impact on exports. The impact of DREX is found to be positive but largely insignificant. An interesting outcome is the LLOCK variable that has a significant positive impact – counterintuitive to our assumption. This may be explained by the fact that the two landlocked countries in the region, Bhutan and Nepal, which are contiguous with India, remain strongly dependent on the Indian economy and are much benefited by virtue of having a bilateral trade agreement with India, and access to a huge Indian market both in proportion of supply 6 The start year is taken as 1980 due to non-availability of data prior to 1980 for two countries, viz., Bhutan and the Maldives. 7 The geographical distance is the theoretical air distance or the great circle distance. Unless the capital cities of two trading countries are the major trade hubs or city centers, this paper considers the distance between the two trade hubs of the trading partners. This is because if the distance is considered only between the capital cities of two countries, it could likely underestimate or overestimate the actual gravity factor between two trading partners.


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source and export destination. Although being landlocked, they are not isolated in the intra-regional context. For example, Bhutan and Nepal’s intra-regional exports stood as high as 81.9 percent and 36.2 percent, respectively in 1998—the highest of all other members in the region (see Pitigala 2005, p. 8, Table 2).8 TABLE 5.1 GRAVITY EQUATION ESTIMATES FOR POOLED OLS MODEL (BENCHMARK) Specification

Sub-period 1 Pre-SAARC (1980-1984) 0.20 (0.16) 3.51*** (4.16) 1.55 (1.24) 2.24*** (2.62) -1.05** (-2.25) 0.03 (0.05) 1.05** (2.09) 0.73 (1.14) 3.58* (1.86) 0.15 (0.17)

Sub-period 2 Sub-period 3 Sub-period 4 Sub-period 5 Post-SAARC Pre-SAPTA Post-SAPTA Pre+Post Regressors (1985-2005) (1980-1994) (1995-2005) (1980-2005) 0.51*** 0.66*** 1.05** ln(GDPit) 0.75 (2.28) (2.95) (2.53) (0.99) 1.35*** 1.43*** 1.24*** ln(GDPjt) 3.31*** (10.29) (10.78) (8.34) (9.28) 1.05*** 0.92*** 0.43 ln(POPNit) 0.95 (5.35) (4.30) (1.09) (1.44) 0.25** 0.26*** 0.48*** ln(POPNjt) 1.77*** (2.15) (2.23) (3.27) (5.27) -0.88*** -1.24*** -1.53*** -0.91*** ln(DISTij) (-3.49) (-4.39) (-3.83) (-2.95) 0.76 0.96 0. 54 0.55 ln(DREXijt) (1.26) (0.59) (1.02) (0.96) 1.05** 0.32 0.59 1.09** BORDij (3.91) (1.03) (1.30) (2.07) 0.34* 0.54 0.75** 0.03 LANGij (1.99) (1.63) (2.20) (0.09) 2.48*** 2.16*** 2.14*** 2.12*** CURRijt (5.04) (4.12) (3.36) (2.63) 0.76** 0.85** 0.88** 0.40 TRAG_bijt (2.87) (3.07) (2.21) (1.54) -0.10 -0.31 SAPTA_iijt (-0.50) (-1.25) -1.37*** -1.26* -1.34*** -2.31*** -2.16*** ORTA_eit (-2.87) (-1.94) (-2.60) (-3.33) (-4.50) -1.86*** -1.96*** -1.08*** -1.76*** -1.98*** CONFijt (-16.48) (-14.57) (-10.36) (-4.92) (-13.26) 2.42** 1.73** 2.59* 2.17*** 2.90*** LLOCKij (2.95) (2.23) (1.86) (2.97) (2.64) 0.64*** 0.18 1.55* 3.42*** 1.67*** ILAND ij (0.31) (1.68) (2.70) (2.58) (1.20) 6.97*** 6.64*** 9.36*** 8.40*** PORTij 7.57*** (13.63) (8.33) (7.40) (14.03) (16.54) 0.32 0.62** 0.02 INDLIBijt 0.05 (0.85) (2.16) (0.05) (0.17) -19.52*** -25.67*** -41.66*** -35.71*** Constant -21.29*** (-6.71) (-5.27) (-4.14) (-6.47) (-7.56) 882 462 210 630 Observations 1092 2 0.76 0.76 0.68 0.74 0.73 R Note: Regressand (ln(Xijt) is the log real exports. Numbers in parenthesis are t-statistics. *, ** and *** indicate significance level at 10 percent, 5 percent and 1 percent, respectively. White HeteroskedasticityConsistent Standard Errors & Covariance used. Source: Author’s estimation.

8 A caveat in this regard is to note that the data in this model encompass only intra-regional trade among seven South Asian nations, as a result of which limits the sample size. This provides a plausible explanation for why few variables produce counterintuitive results as opposed to idiosyncratic behavior of that particular variable, e.g., LLOCK in this case. However, with a large representative sample, we would generally expect this variable to behave more appositely. As such, consideration of a representative enough sample encompassing both intra- and inter-regional data could be one of the directions for future research.

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The other result that attracts our attention is the statistically insignificant coefficient for BORD in sub-period 2 and 4, and for LANG in the first three sub-periods, while our conventional wisdom suggest that it should have a significant impact. There are certainly other forces at work. Prior to 1947, Bangladesh, Pakistan and India were (in fact) a single country; but even as independent countries now, the common attributes that exist among the trio is of little advantage when it comes to formal trade relations. Firstly, this can be ascribed to repercussions of the estranged relationship after the 1971 Bangladesh Liberation War (Mukti Juddho) or the so-called Indo-Pakistan War of 1971. Hence, the formal trade plummeted since then and worsened further in the ensuing period, particularly during the eighties. Two countries that were engaged in formal trade during the period in question were Nepal and Sri Lanka, which are ironically, the most distant neighbors and do not share a common language. 9 Secondly, and most importantly, the volume and magnitude of formal border trade is ably marred by the existence of a very efficient informal channel along the borders of India-Bangladesh, India-Nepal, India-Pakistan, and India-Sri Lanka. This informal trade is efficiently sustained due to the traditional, historical and ethnic ties that help significantly in guaranteeing payments. Another factor for this effectiveness is the preference given by traders with lower education, who have less access to formal channels, and also because the cross-border trading procedures is highly cumbersome and complex, which further increases the possibility of corruption (see De & Bhattacharyay, 2007; Pohit & Taneja, 2000; Taneja, 2002, 2006). There also exists informal trade between India and Bhutan due to a porous border, which is not reflected in the data due to factors like under-invoicing of goods, non-declarations of business purchases and smuggling to avoid tax.10 Of special interest in this regression result is the coefficient for TRAG_b, which is not significantly different from zero in the pre-SAARC (sub-period 1) and pre-SAPTA (sub-period 3) periods, but is positive and statistically significant at conventional levels in the post-SAARC (sub-period 2) and post-SAPTA (sub-period 4) periods. The coefficient of TRAG_b in sub-period 2 is 0.85, while in the sub-period 4 is 0.88. This implies that the impact of TRAG_b for the post-SAARC and post-SAPTA periods accounted for almost 134 percent and 141 percent more exports than its normal level, respectively.11 By the same token, this is explicated by actual statistics in Figure 5.1: before the inception of SAARC or SAPTA, intra-regional exports are recorded much lower at about US$631 million in 1980. However, there is a sharp increase in 2000 accounting for US$2,691 million.

9

A close scrutiny of the actual data confirms this fact. See http://www.kuenselonline.com/modules.php?name=News&file=article&sid=10326 (retrieved May 4, 2008). 11 Since [exp(0.85)-1*100] = 133.96%, and [exp(0.88)-1*100] = 141.09%. 10


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FIGURE 5.1 INTRA-SAARC EXPORTS, 1980-2004 5,767.3

6,000.0

Million US$

5,000.0 4,000.0 3,000.0

2,691.4 2,089.0

2,000.0 1,000.0 0.0

912.4 630.5

1980

637.0

1985

1990

1995

2000

2004

Year

Source: Author, based on data from DOTS, IMF.

The negative and statistically insignificant impact of SAPTA_i in sub-period 2 and 5 is an antithesis to our expectations, as any PTA is likely to enhance intra-regional trade. However, this point will be further discussed later in Section 5.5.C, wherein the results based on segregation is reported that underlies eventual divergences in impacts between those countries with and without the trade agreements. The negative impact of ORTA_e is expected. The propensity to trade more outside the bloc and less within the bloc is evident in all sub-periods, recording as high as 90 percent less trade than its normal level in sub-period 1. Pitigala (2005, p. 42) plausibly characterize SAARC members as “only moderately” natural trading partners, for most SAARC members demonstrate “an increasing tendency to trade relatively intensively with partners outside the region, due to either pure endowment differences – that is, visà-vis industrial countries – or due to long-standing cultural, ethnic, and/or religious affiliations.” Another parameter of interest is the coefficient for CONF, which is negatively associated with exports at a high level of significance against a two-sided alternative across all periods. Given the scenario of hostility and incessant discord among SAARC members as discoursed above, this result is also not at odds. Lastly, it may be noted that INDLIB has a significant impact only in the post-SAPTA period registering an export increase of about 86 percent. The impact of this variable is further discussed in the succeeding section. B.

Robustness Checks

So far, it has been observed the expected signs of traditional gravity variables, as well as significant positive impact of TRAG_b in sub-period 2 and 4, but are the findings robust? In order to test this, the author develops various sensitivity scenarios employing country and time fixed effects, first differencing, IV technique, fixed and random effects models.

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Controlling for country-pair fixed effects (Table 5.2, Specification 1), GDPs and POPNs are still positively associated with exports. As expected, the coefficient for DIST is negative and significantly different from zero. The impact of DREX retain analogous pattern. The coefficients for BORD, LANG, CURR, TRAG_b, SAPTA_i, ORTA_e and CONF are largely as expected. Nevertheless, a careful scrutiny demonstrates that the negative impact of ORTA_e has slightly reduced registering about 84 percent less trade. Adding year dummy has little material impact (Table 5.2, Specification 2). In Table 5.2, Specification 3, controlling for both country and time fixed effects show slight variations in the intensity of TRAG_b, ORTA_e and CONF coefficients. While the impact of TRAG_b increases, the impact of ORTA_e and CONF slightly reduces. Here the author also applies the Generalized Least Squares (GLS) with cross section weights to account for various patterns of correlation between the residuals, i.e., weights estimated in preliminary regression with equal weights and then applied in weighted least squares in the second round. Assuming the presence of cross-section heteroskedasticity, and using GLS weighting and White cross-section standard errors to allow for general contemporaneous correlation between the residuals show that the impact of India alone accounts for about 326 percent, followed by Pakistan at about 112 percent. As is characteristic of gravity phenomenon, the impact of smaller economies of Bangladesh, Bhutan, Maldives and Nepal are less apparent, but the large economies of India and Pakistan evince a significant impact.12 This is an implicit indication that there is a significant trade potential for South Asia, if only India and Pakistan have the earnest desire to exploit each other’s potentiality wholly and unfeignedly. Estimation results in Table 5.2, Specification 4 and 5, further buttress the given assertion. For example, the impact of Indian trade liberalization is quite strongly associated with exports generating an increase of about 95 percent and 103 percent, respectively. Clearly, India’s trade liberalization policy in the early nineties, accompanied by increasing interactions with its trading partners; the dismantling of state-erected barriers; and more importantly, its accommodating behavior to smaller neighbors, such as Bangladesh, Bhutan, the Maldives, Nepal and Sri Lanka that gave growing access to their products inside the large Indian market; its financial liberalization and structural adjustment since 1991, coupled with the fast developing IT industry have brought about a pronounced impact on exports (see Gilani, 2005).13 One may also note that in Specification 4 of Table 5.2, the author introduces IV to the first-differenced data and employ two-stage least squares (2SLS) estimation. A set of three IV (as described earlier) are used that is likely to influence the formation of FTA and less likely to be correlated to the error term, ε ijt . The IV method is widely used in economics and finance to deal with problems of endogeneity and measurement error of one or more explanatory variables. This method applies two-stage least squares (2SLS/TSLS), which is second in popularity next to OLS for estimating linear equations 12 Intercepts and coefficients for country effects are not reported for brevity and space considerations. 13 It should be noted here that the period under consideration is post-SAPTA.


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in applied econometrics. Additionally, first differencing is particularly useful when the unobserved factors that change over time are serially correlated. If ε ijt follows a random walk, meaning that there is very substantial positive serial correlation, then the difference Δε ijt is serially uncorrelated. This feature makes it an effective tool to rectify the problem. TABLE 5.2 ESTIMATES USING COUNTRY AND TIME EFFECTS, IV, GLS, FIRST DIFFERENCING, AND GMM Specification Regressors ln(GDPit) ln(GDPjt) ln(POPNit) ln(POPNjt) ln(DISTij) ln(DREXijt) BORDij LANGij CURRijt TRAG_bijt SAPTA_iijt ORTA_eit CONFijt

(1) With countrypair fixed effects (1980-2005) 0.33* (1.68) 1.36*** (10.06) 2.77** (2.20) 0.22* (1.77) -1.04*** (-4.01) 0.71 (1.19) 1.00 (1.51) 0.28 (1.00) 2.83** (5.17) 0.81** (3.11) -0.55 (-1.44) -1.35* (-1.69) -1.83*** (-15.32)

LLOCKij ILAND ij PORTij INDLIBijt Constant Observations R2 2SLS/GMM R2

7.95*** (14.98) 0.35 (0.92) -24.10*** (-5.61) 1092 0.74

(2) With year fixed effects (1980-2005) 0.46 ** (2.10) 1.33*** (10.17) 1.11*** (5.65) 0.26** (2.21) -0.89*** (-3.57) 0.76 (1.28) 1.09** (4.05) 0.63* (1.69) 2.50*** (5.07) 0.72*** (2.74) -0.55 (-1.37) -1.55*** (-3.20) -1.86*** (-16.53) 2.34** (2.90) 0.67** (1.84) 7.63*** (16.50) 0.18 (0.55) -33.86** (-2.06) 1092 0.73

(3) With GLS, country and year effects (1980-2005) 0.14* (1.88) 1.13*** (18.73) 2.75*** (7.21) 0.47*** (6.84) -0.64* (-1.61) 0.31* (1.70) 0.60*** (5.71) 0.87* (1.85) 3.15*** (7.28) 1.03*** (14.07) -0.10* (-1.71) -1.33*** (-4.39) -1.49*** (-27.20)

8.42*** (32.59) 0.30 (1.31) -21.89 (-1.43) 1092 0.91

(4) With First Differencing and IV-2SLS (1995-2005) 1.55** (2.33) 1.44*** (8.74) 0.43 (1.29) 0.48*** (3.47) -1.53*** (-3.83) 0.45 (1.00) 0.58 (1.30) 1.05** (2.20) 2.34*** (3.39) 0.82** (2.21)

(5) With IV and GMM (1995-2005) 2.26** (2.18) 1.46*** (4.14) 0.20*** (0.36) 0.45* (1.75) -1.46* (-1.69) 0.69 (1.03) 0.77* (1.74) 0.98* (1.90) 2.95** (2.18) 1.07*** (2.78)

-1.42*** (-4.50) -2.04*** (-10.18) 2.78* (1.83) 0.55 (0.68) 6.64*** (8.33) 0.67*** (3.16) -25.79*** (-3.71) 462

-1.38*** (-3.33) -1.91*** (-4.92) 2.95* (2.82) 0.81** (2.12) 7.41*** (5.38) 0.71*** (4.26) -34.10*** (-2.94) 462

0.75

0.76

Note: Regressand (ln(Xijt) is the log real exports. Numbers in parenthesis are t-statistics. *, ** and *** indicate significance level at 10 percent, 5 percent and 1 percent, respectively. Two-Stage Least Squares and Autoregressive Moving Average (2SLS and ARMA), GLS (Cross Section Weights), GMM (Generalized Method of Moments), and White Heteroskedasticity-Consistent Standard Errors & Covariance used. Source: Author’s estimation.

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Focusing again on the TRAG_b and CONF variables in Specification 4, the coefficients retain the expected signs. On one hand, TRAG_b has a positive impact yielding about 127 percent increase in exports; CONF, on the other hand, has a strong negative impact of nearly 87 percent. It is worth noting that t-statistics for the CONF dummy is for the most part above 10, signifying a high degree of precision of the coefficient estimates. Hausman Test is applied to compare the OLS and 2SLS estimates and to determine whether the differences are statistically significant. This protocol tests the null hypothesis (H0) that the error term, ε ijt of the OLS and the error term of the 2SLS (say, ρ ijt ) are not correlated. The test fails to reject the H0 concluding the exogeneity of IV because ε ijt and ρ ijt are not correlated. In addition, the testing of overidentifying restrictions used in the 2SLS suggests that the model is just identified. Finally, the author applies IV and Generalized Method of Moments (GMM) estimation in Specification 5 of Table 5.2. The use of GMM estimators can improve upon the traditional 2SLS approach and the reliability of IV method can be further assessed, particularly in the potential presence of weak instruments (see Baum et al., 2002). The GMM employs the Bartlett’s regression, a method whereby the linear statistical function can be estimated when the dependent and independent variables are subject to error. Again, the signs and magnitudes of almost all the covariates are as predicted and coherent. TRAG_b and CONF are highly significant in corresponding reverse order. Interestingly, the impact of INDLIB is also highly significant at 1 percent level of significance. Additionally, the author also estimates the fixed effects and random effects models. Fixed effects means separate intercepts estimated for each pool member, while random effects treat intercepts as random variables across pool members. F Test was performed to see whether the fixed effects coefficients are equal or not by comparing the sum of squared residuals (SSR) from the fixed effects model and the random effects model. The computed p-value of zero soundly rejects the H0 of equal intercepts. In addition, pvalues of the Hausman Test are essentially zero for almost all sub-periods, and so the H0 of the random effects model in favor of the fixed effects model is rejected. In line with the core result in pooled OLS model, the impact of the explanatory variables in both the fixed effects and random effects models remain broadly unchanged, bearing anticipated signs.14 Perturbations using varied sensitivity analyses thus confirm that estimated parameters by the pooled OLS model are consistent and reliable. It is logical for that matter to prefer the OLS estimates. However, considering the magnitudes of the coefficients, R-squareds, and also that the fixed effects method seems to be more plausible in explaining country pairs trade relations, other preferred estimations are the GLS specification with country and year effects, and IV-GMM (Table 5.2, Specification 3 and 5). One must, however, bear in mind that IV estimators, unlike their OLS counterparts are consistent in the presence of endogeneity only if the required 14

For the purpose of brevity, the tables of results are not reported.


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assumptions are satisfied. If not, they have a larger sampling variance and are biased, in particular toward the point estimates of their OLS counterparts. C.

TRAG-SAPTA Nexus: Trade Creation or Trade Diversion?

Researchers have oftentimes raised doubts as to whether SAPTA has been the main vehicle for enhancing intra-bloc trade. This contentious argument calls for further empirical testing. In order to clarify this point, the author employs a novel procedure: first splitting the effects of the TRAG and SAPTA distinctly, and then further dividing the impact of TRAG and SAPTA among those members that do have trade agreements in place and those that do not have. Separating the two different impacts yields an interesting outcome (see Table 5.3). The results eloquently portray that TRAG has a significantly positive impact on exports in all three sub-periods, 1995-2005, 1985-2005 and 1980-2005. Interestingly, the impact of SAPTA is evident only in the case of countries that have trade agreements in place. As for those countries that do not have trade agreements, it is quite surprising to find that the impact of SAPTA is either insignificant or even negative. These effects are further corroborated in Figure 5.2, wherein the net trade creation is very evident in all sub-periods for those countries that have trade agreements, registering almost 77 percent during the period 1995-2005, while there is also a significant trade diversion in the case of countries that are not conjoined by trade agreements. Trade diversion is especially prominent as we trace back to the earlier period (1980-2004). Yet, above all, it is comforting to find that the combined impact for all countries is still net tradecreating in the post-SAARC and post-SAPTA periods. TABLE 5.3 SUMMARY: IMPACT OF TRAG AND SAPTA Impact of TRAG Countries with Trade Agreements

Countries without Trade Agreements

(1995-2005)

(1995-2005)

Impact of SAPTA Countries with and without Agreements

Countries with Trade Agreements

Countries without Trade Agreements

Countries with and without Agreements

(1995-2005) (1995-2005) (1995-2005) (1995-2005) 0.88** (2.21) (1985-2005) (1985-2005) (1985-2005) (1985-2005) (1985-2005) (1985-2005) 0.85*** 1. 45*** -0.55 -0.31 (3.07) (3.18) (-0.23) (-1.25) (1980-2005) (1980-2005) (1980-2005) (1980-2005) (1980-2005) (1980-2005) 0. 76*** 0.36** 0.26 -0.10 (2.87) (1.98) (0.61) (-0.50) Note: Numbers in parenthesis are t-statistics. ** and *** indicate significance level at 10 percent and 5 percent, respectively. Intercepts and coefficients for all standard covariates not reported for brevity and ease of presentation. TRAG/SAPTA variable for specific periods are omitted to avoid collinearity. Source: Author’s estimation.

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FIGURE 5.2 NET TRADE CREATION AND TRADE DIVERSION 76.8

80 60

41.9

40

27.1 18.5

Per cent

20

1995-2005

8.3

1985-2005

0 -14.8

-20 -40

1980-2005

-47.3 -63.6

-60 -80

-94.9

-100 With Trade Agreements

Without Trade Agreements

With & Without Trade Agreements

Countries

Source: Author, based on estimations.

Furthermore, it is interesting to observe that, while TRAG has a diminishing impact as we move backwards in time, the impact of SAPTA as well has a similar retreating trend, and more so in the case of countries with the trade agreements. This could simply mean that the positive impact on exports in the later sub-periods is not only due to SAPTA per se, but it seems to be the combined upshot arising from the delayed impact of the existing bilateral trade agreements among the member states. This argument is underpinned by introducing the distributed lagged effects of TRAG in Table 5.4. TABLE 5.4 DISTRIBUTED LAGGED EFFECTS OF TRAG Specification (2)

(1)

Year

With no Country and Time Effects (1980-2005) Percent Increase


With Country Effects (1980-2005) Percent Increase


(3)

(4)

With Time Effects (1980-2005)

With Country and Time Effects (1980-2005)

Percent Increase


Percent Increase


1.47 85.88 1.35 82.46 1.30 82.88 1.47 69.52 1981 1.50 87.98 1.53 93.54 1.44 91.82 1.60 75.70 1985 1.61 94.23 1.57 96.04 1.48 94.35 1.56 74.12 1990 1.65 96.36 1.64 100.36 1.76 112.69 1.68 79.57 1995 1.97 114.94 1.71 104.41 1.86 118.79 2.06 97.46 2000 2.33 136.31 2.05 124.94 2.19 140.27 2.22 105.46 2004 Note: The base year is 1980. The percentage increase is calculated based on magnitude of the coefficients. Source: Author’s estimation.

Adjusting for unobservable heterogeneity using country and time effects, the impact propensity is striking, with increasing effect over time.15 For example, in 2004 15

To focus on the ceteris paribus effect of regressor (say z ) on regressand (say y ), we set the

error term in each time period to zero. Then, if y t − y t −1 = δ 0 , this shows that change in y due to one-unit increase in z at time t . impact multiplier. Similarly, change, and so on.

δ 0 is the immediate δ 0 is usually called the impact propensity or

δ 1 = y t +1 − y t −1 , is the change in one period after the temporary


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there is a dramatic increase in the TRAG impact to about 106 percent, 140 percent, 124 percent and 136 percent in Specification 1, 2, 3, and 4, respectively. Thus, the impact of TRAG is more than twofold. 16 These results are evocative and coherent with those commonly reported in the previous literature. 17 Understandably, it took nearly two decades for the trade agreements to show reasonably clear impacts, which clearly indicates the sluggish nature of SAARC’s progress in regional integration and trade liberalization.

5.6 CONCLUSION This chapter has estimated an augmented gravity model using the panel data to determine the impact of trade agreements on exports in SAARC bloc. The model generated reasonably precise and stable estimates with expected signs and magnitudes. For instance, the signs of the coefficients for the traditional variables, such as GDPs, POPNs, DIST, DREX, BORD, LANG, CURR, ORTA, and other geography and infrastructure variables including ILAND and PORT were as expected; PORT has indeed a very significant impact on exports. The CONF variable was negatively associated with exports at a high level of significance across all periods. Another variable of our interest was to see the impact of India’s trade liberalization policy (INDLIB) on exports. Interestingly, INDLIB had a significant impact especially in the post-SAPTA period registering an export increase of about 86 percent under the standard (benchmark) analysis, and as high as 95 to 103 percent with first differencing and IV-2SLS, and with IV and GMM analysis. The implications from these results are quite obvious. If the existing conflicts in the region, particularly India and Pakistan, are amicably settled and the benefits of trade liberalization suitably tapped, the growth of trade in the SAARC region will take a different turn. The fundamental question posed was whether the existing bilateral trade agreements (TRAG) have had a positive impact on the volume of intra-regional exports of SAARC countries. The answer is yes, but one should interpret this with caution, as empirical tests have found scarce evidence of its impact in the pre-SAARC and preSAPTA periods. Despite being prolonged, a significant positive impact is observed in the post-SAARC and post-SAPTA periods, even amidst a sustained negative impact of the conflict variable. This propensity is discernible regardless of the estimation techniques applied. The next question was whether or not SAPTA has been a catalytic

16 One caveat in this regard is that this increase is only an artifact of TRAG among many other variables. It should not be confused with the overall increase in the volume of intra-SAARC exports that account for other trade liberalization measures. Note also that an absolute percentage increase of exports from the base year 1980 to 2004 is about 866 percent (see Figure 5.1). If we consider the average impact of TRAG in Table 5.4, Specification 1, 2, 3, and 4 in 2004 as 127 percent, then the independent role of TRAG works out to be around 15 percent. 17 Using a panel data and ATE, Baier and Bergstrand (2007) find that that an FTA on average doubles two member countries’ bilateral trade after 10 years. Rose (2004) finds the impact of regional FTA with country effects to be about 156 percent, while Subramanian and Wei (2007) find that the GATT/WTO in 2000 alone increased the world imports by about 120 percent.

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factor for intra-SAARC exports. Test results clearly exhibited the stimulating role of SAPTA only for those countries that have trade agreements in place, while the reverse was true for those countries without the trade agreements. The increase in exports in the later periods emanates evidently as a combined impact of SAPTA as well as the TRAG among the contracting states. Moreover, trade creation is observed prominently in the post-SAARC and post-SAPTA periods, albeit only for those countries with the trade agreements. In all, the findings support our hypothesis suggesting the trade-enhancing effect of the South Asian trade agreements. Another important implication of this empirical investigation is the strong signal for deeper trade integration among SAARC member states. However, one can only become more optimistic about the future of SAARC, as SAFTA assuages SAPTA, and matures through further dismantling of different forms of tariffs and restrictive ROO, as well as committing steadfastly with the right perspective and affirmative political will to alleviate the intra-regional conflicts, evolve new complementarities, or aggregate with inter-regional blocs. Finally, the focus of this chapter was limited to examining the impact of some specific variables of interest; hence, future work may well consider investigating the effects of other forms of trade restraints and/or trade facilitation measures, such as services, energy, infrastructure, monetary and investment cooperation.

PART IV GTAP MODEL

CHAPTER

6

Welfare Effects of SAFTA and SAFTA+5

147

CHAPTER

6

Welfare Effects of SAFTA and SAFTA+5

T

he focus of this chapter is the GTAP Model—the second pillar of this treatise. The purpose is to evaluate the welfare implications of SAFTA and FTAs with observer countries at the inter-regional dimension. The chapter begins with the general overview, objectives and intuition behind the GTAP model in Section 6.1. Section 6.2 builds up the theoretical framework of the GTAP model concerning the Armington structure, intricacies of variables, types of closures and solution methods, tariffs and subsidies, and formula approaches to tariff reduction. Section 6.3 provides the analytical framework encompassing the model calibration, shocks, closures and solution methods, assumptions, the data and software, aggregation strategy, and the structure of taxes and subsidies. The simulation scenarios and the experimental design or techniques including the diagrammatic framework are illustrated in Section 6.4. Detailed simulations centering on the effects of SAFTA and FTAs with five observer countries, which is the main objective of this chapter, is fully examined in Section 6.5, and Section 6.6 concludes.

6.1 INTRODUCTION In Chapter 4 and 5, it was established using a gravity model that trade agreements among SAARC countries have positive impacts on increasing the volume of intraregional trade. Based on the results obtained from this model, the next pertinent questions that follow are: What would be the welfare effects for SAARC countries as a result of the implementation of the SAFTA, and welfare implications of FTAs among SAFTA members and five observer countries of China, Japan, South Korea, the United States and the EU? Which of the countries are likely to have the most viable FTAs? However, the gravity model cannot efficaciously estimate the welfare effects of FTAs, or more expressly, the trade creation and trade diversion effects. In order to tackle the above research questions posed in this study, the most appropriate tool is found to be the global AGE/CGE model, such as the GTAP model. In view of this, the author employs the GTAP model in this chapter to address the welfare implications of SAFTA as well as trade integration with five observer countries so as to explore the relevance and economic potential for North-South economic cooperation. This is also done with a view to throw light on any possibilities for SAARC countries in having FTAs with the observers, and thereby expanding new perspectives for future memberships. 148

CHAPTER 6 Welfare Effects of SAFTA and SAFTA+5

149

Therefore, the primary objective of this chapter is to perform simulations and policy experiments using the Global Trade Analysis Project (GTAP)1 model to answer the two abovementioned questions. 2 The first hypothesis to be tested is whether compensation by means of preferential tariff concessions from winners to losers will ensure all countries to gain from FTAs. In other words, this hypothesis tests whether selective combinations of tariff rates will result in welfare gains for both the contracting parties. The second hypothesis to be tested is whether or not FTAs with observer countries will be welfare improving, causing more trade creation than trade diversion. The fitting attributes of the aforementioned set of questions is the crucial factor that led to the selection of this model. The GTAP model is a multiregion, multisector computable general equilibrium (AGE) model,3 with perfect competition and constant returns to scale. This model handles the bilateral trade via Armington assumption. The basic innovations of this model include the treatment of private household preferences using the non-homothetic Constant Difference of Elasticities (CDE) functional form, explicit international trade and transport margins, and a global banking sector, which links global savings and investment. It also allows users a wide range of closure options, including selection of partial equilibrium closures that facilitate comparison of results to studies based on partial equilibrium assumptions (see Hertel, 1997a; Hertel and Tsigas, 1997). The GTAP database contains a large set of global economic database covering many sectors and countries/regions in the world. The database consists of bilateral trade, transport, and protections characterizing economic linkages among regions, in conjunction with individual-country input-output (I-O) databases that account for intersectoral linkages within each region (Hertel, 1997a). The database is formulated and solved using GEMPACK (General Equilibrium Modelling Package), which is a flexible suite of general-purpose economic modelling software especially suitable for solving CGE models, developed at the Centre of Policy Studies, Monash University, under the direction of Ken Pearson. The GEMPACK has the ability to handle a wide range of economic behaviors with powerful capabilities for viewing data and analyzing results (GTAP, 2005a).4 1 GTAP (Global Trade Analysis Project) is a global network of researchers, who conduct quantitative analysis of international economic policy issues, especially trade policy. They cooperate to produce a consistent global economic database, covering many sectors and countries/regions in the world. The GTAP project is coordinated by a team at the Center for Global Trade Analysis (CGTA), based in the Agricultural Economics Department at Purdue University. The team maintains a global computable general equilibrium model (GTAP model), which uses the GTAP database. Besides the core model, there are many variants (including one focused on agricultural analysis), each focusing on a different issue in economic policy analysis. 2 That is, the research Question 4 stated in Chapter 1. 3 AGE models are a class of economic model that use actual economic data to estimate how an economy might react to changes in policy, technology or other external factors. AGE models are also referred to as applied general equilibrium (CGE) models. They are derived from the inputoutput (I-O) models pioneered by Wassily Leontief (1986), but assign a more important role to prices. 4 The current release is the sixth (GTAP 6) since 1993 based on 2001 benchmark. The GTAP 7 database is expected to be released sometime by the end of 2008. The base year for GTAP 7 is 2004.

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PART IV GTAP Model

One of the main advantages of using the GTAP model is the availability of welltested and reliable global database. Another component is the CGE framework of the model that works seamlessly with GEMPACK software suite. Third, the GTAP has filled the niche in the area of global trade analyses. The provision of standard database and modeling framework has made it possible for analysts from many areas to work with CGE modeling to address specific issues of interest without having to go through the rigorous procedures of collecting data and programming lessons (see Hertel, 1997b). Fourth, due to its economy-wide coverage, GTAP is particularly useful for analyzing issues that cut across many diverse sectors. This database is particularly useful to researchers analyzing the potential impact of global trade liberalization, regional trade agreements, the economic consequences of environmental issues, and domestic impacts of economic shocks in other regions. Sector-by-sector analyses can provide a valuable input into such studies.

6.2 THEORETICAL FRAMEWORK Over the last decade, the GTAP model has become an important tool for analyzing various economic issues. This development is explained by the capability of CGE models to provide an elaborate and realistic representation of the economy including the linkages between all agents, sectors, and other economies. Global CGE models often include an enormous number of variables, parameters, and equations. They are indeed a very powerful tool because it allows analysts to explore numerically a huge range of issues on which econometric estimation would be difficult to forecast, particularly the effects of future policy changes. The complete coverage permits unique insight into the effects of changes in the economic environment throughout the whole economy. The GTAP database describes bilateral trade patterns, production, consumption and intermediate use of commodities and services. The GTAP model integrates and incorporates a macro framework of the multiregion open economy model using a wide set of variables, parameters, and equations. In contrast to the closed economy, the multiregion model includes separate conditional demand equations for domestic and imported intermediate inputs. Of late, several researchers in the field of international trade and economics have become ardent users of the GTAP model because the database accompanying the GTAP model is particularly well-suited to analyze the consequences of a free trade area (see Gehlhar, 1997; Young and Huff, 1997). In fact, a multiregion AGE approach has a number of advantages over partial equilibrium in that the model not only allows for endogenous movements of regional prices and quantities in response of technological change, but also provides a consistent framework that “avoid pitfalls of under- or overcounting welfare effects in a multimarket setting” among others (Frisvold, 1997, p. Improving the bilateral services trade data, energy, taxes and primary factors are some of the top priorities for the GTAP 7 database. In addition, a number of initiatives are currently being undertaken to improve regional coverage, issues with commodity disaggregation, trade totals, land use database, etc. This version includes 105 countries/regions (GTAP, 2005b).


151

324). According to Raihan (2008, p. 16), the GTAP model “is the best possible way for the ex ante analysis of economic and trade consequences of comprehensive multilateral or bilateral trade agreements.” A.

Structure of GTAP FIGURE 6.1 GRAPHICAL EXPOSITION OF THE MULTIREGION OPEN ECONOMY Regional Household

Private Expenditures (PRIVEXP)

Taxes (TAXES)

Taxes (TAXES)

Savings (SAVE)

Private Household

Government Expenditures (GOVEXP)

Government

Global Savings Factor Payments (VOA)

Taxes (TAXES)

Export Taxes (XTAX)

Net Investment (NETINV)

Private Domestic Purchases (VDPA)

Import Taxes (MTAX)

Public Domestic Purchases (VDGA)

Producer Imports (VIPA)

Imports (VIGA)

Imports (VIFA)

Inter-Firm Transactions (VDFA)

Exports (VXMD)

Rest of the World

Source: Adapted by author from Brockmeier (2001), Figure 6; Hertel and Tsigas (1997), Figure 2.2; and Otsubo (1998), Figure 2.

Figure 6.1 depicts the GTAP macro framework of the multiregion open economy model. As opposed to closed economy, the open economy model introduces two global sectors. The global bank (shown in the center of the figure) intermediates between global savings and regional investment. The global sector accounts for international trade and transport activity. It transacts and assembles regional exports of trade, transport, and

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PART IV GTAP Model

insurance services and acts as intermediaries to move merchandise trade among regions (Hertel and Tsigas, 1997). The figure also depicts the accounting relationships of the component of final demand in an open economy. The government and private households not only spend their income on domestically produced but also on imported commodities which are denoted as VIPA and VIGA, respectively. Furthermore, both agents pay additional commodity taxes on imports to the regional household, so that the accounting relationships of these two agents now also include consumption taxes and expenditure for imported commodities. In this model, savings and investment are computed on a global basis, so that all savers in the model face a common price for this savings commodity. This means that if all other markets in the multiregional model are in equilibrium, all firms earn zero profits, and all households are on their budget constraint, then global investment must equal global savings to satisfy the Walras’ Law (Brockmeier, 2001). Finally, the accounting relationships for the rest of the world show that they get payments for selling their goods for private consumption, government, and firms (producers). These revenues will be spent on import taxes (MTAX), and export taxes (XTAX) paid to the regional household. There is also an inter-firm transaction among producers (VDFA). These producers transact among themselves and with the regional household by paying for factors (VOA) and taxes (TAXES), as well as with the rest of the world in the form of exports (VXMD) and imports (VIFA). B. Armington Structure and Assumptions A visual depiction of Armington structure of the assumed technology for firms in each of the industries in the GTAP model is provided in Figure 6.2. This “technology tree” represents the separable, constant returns-to-scale technologies. Individual inputs demanded by each firm are shown at the bottom of inverted tree. The primary factors of production are land, labor and capital, and their quantities are denoted by qfe(i,j,s). While some intermediate inputs are purchased by firms in the domestic market, i.e., qfd(i,j,s); others are imported, qfm(i,j,s). In fact, imports are “sourced” from across the border from specific exporters, qxs(i,r,s); hence this is represented by the dashed line between the firms’ production tree and the constant elasticity of substitution (CES) nest aggregating bilateral imports. Therefore, firms produce their output by combining individual inputs and this is largely based on the assumption of the separability in production. In other words, it is assumed that firms have the liberty to choose on the optimal mix of primary factors independently of the prices of intermediate inputs. By such an assumption of separability, the model imposes the restriction that the CES between any individual primary factor and intermediate inputs is equal. This is how it helps to sketch the production tree with the common CES in the inverted technology tree, where intermediate and primary factors of production are combined. This then helps in reducing the number of parameters significantly to make the model more handy and


153

operational. Moreover, with respect to specific form of equations used to represent firm behavior, the model imposes restriction of non-substitution between composite intermediates and primary factors. The fact that this is a common specification in AGE models becomes a poor justification for its incorporation into the GTAP model. FIGURE 6.2 ARMINGTON PRODUCTION STRUCTURE qo(j,s) [ao(j,s)]

0

Leontief

ava(j,s)] qva(j,s)

qf(i,j,s) [af(i,j,s)]

σ VA

σ DD

CES

CES

Land

Labor

Capital

Domestic

Foreign

qfd(i,j,s)

qfm(i,j,s)

qfe(i,j,s)

[afe(i,j,s)]

σ MM CES

qxs(i,r,s) Source: Hertel and Tsigas (1997), Figure 2.6, p. 39.

Furthermore, imported intermediate inputs are assumed to be separable from domestically produced intermediate inputs. In other words, firms first decide to source their imports, and based on the composite price they decide the optimal mix of imported and domestic goods. This specification is known as the “Armington approach”, as this was first proposed by Paul Armington in 1969. However, the idea has been widely criticized on the ground that it is very restrictive (Alston et al., 1990; Winters, 1984). Although more flexible functional forms are obviously preferable, the Armington approach is still useful and “permit[s] us to explain cross-hauling of similar products and to track bilateral trade flows” (see Hertel and Tsigas, 1997, p. 41).

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PART IV GTAP Model

C. Variables and Sets Most economic models contain more variables than equations. The model is only able to determine values for as many variables as there are equations. The GTAP integrates and uses several set of variables, which are: (1) quantity variables, (2) price variables, (3) policy variables, (4) technical change variables, (5) dummy [0,1] variables, (6) slack variables, (7) value and income variables, (8) utility variables, (9) welfare variables, and (10) trade balance variables (see Swaminathan, 1997 for technical details of these variables). Values for some variables must be set externally (by the user). Such variables are called “exogenous” (see Appendix A11 for definition of exogenous variables). The rest, whose values are determined by the model, are called “endogenous”. Although the number of endogenous variables is always equal to the number of equations, there may be several ways to pick the endogenous group from among the entire set of variables. D.

General Equilibrium Closures

A particular choice of “exogenous” and “endogenous’ variables is called a “closure”. The two most common closures used in the GTAP model version 6.0 are: Standard Closure The exogenous5 variables include: pop, psaveslack, pfactwld, profitslack, incomeslack, endwslack, cgdslack, tradslack, ams, atm, atf, ats, atd, aosec, aoreg, avasec, avareg, afcom, afsec, afreg, afecom, afesec, afereg, aoall, afall, afeall, au, dppriv, dpgov, dpsave, to, tp, tm, tms, tx, txs, and qo. The rest are endogenous. GTAP Book Closure The exogenous variables include: pop, psave, pcgdswld, profitslack, incomeslack, endwslack, cgdslack, tradslack, ams, atm, atf, ats, atd, aosec, aoreg, avasec, avareg, afcom, afsec, afreg, afecom, afesec, afereg, aoall, afall, afeall, au, uelas, dpgov, dpsave, to, tp, tm, tms, tx, txs, and qo. The rest are endogenous. The sets contain various groups and subgroups of commodities and primary factors used by the GTAP. These are: - CGDS_COMM = capital goods commodities - DEMD_COMM = demanded commodities - ENDW_COMM = endowment commodities - ENDWC_COMM = capital endowment commodity - ENDWM_COMM = mobile endowment commodities - ENDWS_COMM = sluggish endowment commodities - MARG_COMM = margin commodities - NMRG_COMM = non-margin commodities 5

See Appendix A12 for definition of exogenous variables.


E.

NSAV_COMM PROD_COMM REG TRAD_COMM

= = = =

155

non-savings commodities produced commodities regions in the model traded commodities

Solution Methods

One interesting feature of the RunGTAP is that it offers a variety of different solution methods. The most important methods are as follows: Single-Step Solution (Johansen Method) The Single-Step Solution or Johansen Method treats the model as a linear system, linearized around the initial solution. This approach is computationally the simplest and quickest. However, since GTAP is actually a non-linear system, Johansen results are not quite accurate, except for small shocks. The errors are super-proportional to the size of the shock: double the size of the shock, the size of the errors will also more than double. Multi-Step Solution (Euler Multi-Step) Another effective method is the Multi-Step Solution or Euler Multi-Step procedure, which is used to reduce linearization errors. The Euler Multi-Step procedure automatically divides the exogenous shock into a (user-specified) number of equal components. For example, a 10 percent increase in labor supply might be computed as two successive increases of 4.88 percent (1.0488 x 1.0488 = 1.1). Results for the first 4.88 percent installment are calculated and the database is updated accordingly. Using the new database, results are calculated for the second 4.88 percent installment. Since errors are super-proportional to the size of the shock, halving the shock leads to errors at each step which are less than half the size of the error produced by a single, full-size, step. Thus, the results from the two steps may be combined to produce a solution that is more accurate than that obtained by a single step. The use of more steps yields more accurate results. Sequence of Solutions with Extrapolation (Gragg and Midpoint Methods) Early experiments in solving models by the Euler method led to the following observations. The differences between a 8-step and a 16-step solution are often about half those between a 4-step and an 8-step solution. The differences between a 16-step and a 32-step solution are about half those between a 8-step and a 16-step solution, and so on. This rule enables us to predict what results would be generated by a solution with an infinite number of steps – that is, the exact solution. For example, we might choose to run 3 Euler solutions with respectively 4, 8 and 12 steps. GEMPACK automatically uses these results to extrapolate to a solution that is more accurate than any of the 3 individual solutions.

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PART IV GTAP Model

The Gragg and Midpoint methods are variations on the Euler method. For these methods the numbers of steps in a sequence of solutions must be either all odd or all even, and they generally produce more accurate results for a given number of steps. Therefore, the author adopts the Gragg 2-4-6 (3-solution method). This extrapolation solution method produce an error bound for each variable. In this case, the model is solved several times, each time with a successively finer grid. An extrapolated solution is produced based on these outcomes. These bounds are conservative estimates of computation error and they are not related to uncertainties in the setting of shocks or parameters. Hence, this procedure yields good results (see Harrison & Pearson, 1994). To ensure a more accurate solution, GEMPACK’s automatic accuracy feature has been applied. This procedure decides, during the computation, how many sub-intervals are needed to achieve a user-specified level of accuracy. F.

Behavioral Elasticities: General Equilibrium Analysis

One of the big problems in general equilibrium analysis stems from the fact that firms do not face a single demand schedule. The typical firm or industry sells to many different agents in the economy. Some of these are domestic firms, some are domestic households and some consist of overseas buyers of exported goods. Thus, the demand elasticity faced by a sector in the GTAP model is a composite of responses of many different agents to a price change. For example, if 10 percent of steel output is sold to the domestic automobile industry and that industry’s conditional, own-price elasticity of demand for steel is 0.10, then the contribution of the auto industry to steel’s total, partial equilibrium demand elasticity would be 0.10 * 0.10 = 0.01. However, in general equilibrium, automobile firms’ response to an increase in the price of steel will be more complicated. In particular, this will tend to raise the price of automobiles. As a result, the total number of autos sold is expected to decline. This output effect must be added to the substitution effect captured by the conditional price elasticity of demand to get the total effect. The story does not stop here, though. Since the increase in the price of steel raises the price of other products, some of which may themselves be substitutes for steel, one must also take into account cross-price effects. Finally, income in the economy may be affected, in which case income effects will also come into play. The only way to capture all of these components of the general equilibrium demand elasticity is via model simulation. Simulation of the general equilibrium elasticity of demand proceeds as follows: The output tax on steel is perturbed by 1 percent. This in turn causes the market price of steel to rise by something less than 1 percent, say 0.6 percent (the remainder is borne by producers in the form of a decline in the agent’s supply price). We then observe the reduction in market demand, which might be 0.9 percent. In this case, the general equilibrium elasticity of demand for steel in the model would be -0.9 percent/0.6 percent = -1.5 percent. But to segregate how much of this is attributable to different agents, one must observe the change in demand by all of the different agents in the model. Auto producers might show a reduction in steel demand of 0.08 percent. This is


157

then divided by the market price change to find the auto sector’s general equilibrium, own-price elasticity of demand for steel = -0.08 percent/0.6 percent = -0.13. If this elasticity is then multiplied by the auto sector’s share of total steel demand, we obtain their contribution to the total general equilibrium demand elasticity for steel = 0.1 * 0.13 = -0.013. For a small perturbation, the share-weighted agents’ responses will add up to the total general equilibrium demand elasticity in this manner (see GTAP, 2005a). G. Import Tariffs and Export Subsidies A tariff is a tax on foreign goods upon importation. Simply put, import tariffs are “taxes levied on imports” (Krugman & Obstfeld, 2003, p. 109). The term subsidy (or export subsidy) is often used as an antonym to a tax, i.e., a government transfer of money to an entity in the private sector. More explicitly, export subsidies are “payments given to domestic producers who sell a good abroad” (ibid). Tariffs may be of various kinds: An ad valorem tariff is a fixed percentage of the value of the item that is being imported. A specific tariff does not relate to the value of the imported goods but to its weight, volume, surface, etc. A revenue tariff is a set of rates designed primarily to raise money for the government. A protective tariff is intended to artificially inflate prices of imports and “protect” domestic industries from foreign competition. A prohibitive tariff is one so high that no one imports any of that item. The distinction between protective and revenue tariffs is subtle: protective tariffs, in addition to protecting local producers, also raise revenue; revenue tariffs produce revenue but they also offer some protection to local businessmen. Effects of Tariffs and Subsidies The market access schedules are not simply announcements of tariff rates. They represent commitments not to increase tariffs above the listed rates, i.e., the rates are “bound”. For developed countries, the bound rates are generally the rates actually “applied”. Most developing countries have bound the rates somewhat higher than the actual rates charged, so the bound rates serve as ceilings.6 Critics of free trade have argued that tariffs are especially important to developing countries as a source of revenue. Developing nations do not have the institutional capacity to effectively levy income and sales tax. In comparison with other forms of taxation, tariffs are relatively easy to collect. The trend of lifting tariffs and promoting free trade has been argued to have had disproportionately negative effects on the governments of developing nations who have greater difficulty than developed nations in replacing tariffs as a revenue source. These government interventions in trade usually take place for the purpose of income distribution, for industrial production considered to be crucial to the economy, 6 See “Understanding the WTO: The Agreements – Tariffs: more bindings and closer to zero.” Available at http://www.wto.org/english/thewto_e/whatis_e/tif_e/agrm2_e.htm (retrieved November 10, 2007).

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PART IV GTAP Model

or for balance of payments. Whatever may be the intention for applying tariffs and subsidies, they have marked effects on the terms of trade. The distinctive characteristic of tariffs and export subsidies is that they create a difference between prices at which goods are traded in the world market and their prices. The direct effect of a tariff is to make imported goods more expensive inside a country than they are outside. An export subsidy gives producers an incentive to export. The extent of the terms of trade effect depends on how large the country imposing the tariff is relative to the rest of the world. If the country is relatively small, it cannot have much effect on global supply and demand, and therefore cannot have much effect on relative prices. Tariffs and export subsidies are often treated as similar policies because they both seem to support domestic producers, but they have opposite effect on the terms of trade (ibid., pp. 110111). Computation of Tariffs and Subsidies Trade generated tax revenues and subsidy expenditures are computed in a manner analogous to the ones which are being raised by policy instruments used in the domestic market. The only difference is that the tax or subsidy rates are defined as the ratio of market prices to world prices. If there is an import tax (subsidy), the market price is higher (lower) than the world price, so that the power of the ad valorem tax is greater (smaller) than one. In the case of an export tax (subsidy), the market price lies below (above) the world price and the power of the ad valorem tax is smaller (greater) than one (see Brockmeier, 2001). Figure 6.3 offers graphical depictions of this concept of border interventions and its effects on demand, supply and price of commodity. For instance, in the left-hand panel of the figure, the market price exceeds the world price (PMS(i,r,s)>PCIF(i,r,s), indicating the presence of tariff on imports or import quota (MTAX(i,r,s)>0), and this contributes to regional income. In the right-hand panel, the domestic price exceeds the world price (PM(i,r)>PFOB(i,r,s)), indicating the presence of a subsidy, and so XTAX(i,r,s)=VXWD(i,r,s)-VXMD(i,r,s) 0 . t1 = t0 when t0 = 0 , otherwise initial conditions ensure that t1 < t0 . The formula has the property of being a function of both the initial tariff and the coefficient a, and the coefficient can be negotiated. As the level of the initial tariff rate increases, the new tariff rate will approach the value of the coefficient a. Thus, the maximum tariff after the cut will, at the limit be equal to a. Moreover, the percent rate of reduction in the case of the Swiss cut is dependent on the level of the initial tariff—larger the initial tariff, larger is the percent rate of reduction. Figure 6.6 depicts a hypothetical non-linear tariff structure for percent rate of reduction, and graphs the final tariff rate against the initial tariff rate. FIGURE 6.6 NON-LINEAR STRUCTURE OF TARIFF CUTS – MAXIMUM TARIFF 5%

Source: Iyer (UNESCAP).

To summarize, the linear tariff cut formula reduces tariffs by an equal percentage across the entire class of products, which is equivalent to an average. Alternatively, nonlinear cuts are reductions that are in an inverse relation, i.e., they tend to cut down higher tariffs more than lower ones, since the former tend to be more trade distorting. The Swiss formula is one such mathematical device designed to cut and harmonize tariff rates. Many developed countries today are pushing for the Swiss formula, which requires steeper tariff cuts for higher levels of existing tariff. However, the developing countries consider the Swiss formula more detrimental.8

6.3 ANALYTICAL FRAMEWORK In this study, the effects of SAFTA and FTAs with observer countries are simulated and quantified using the GTAP model, a multiregion, multisector, global CGE model. The GTAP model employs the so-called Armington assumption in the trading sector which provides the possibility to distinguish imports by their origin and explains intra-industry trade of similar products. Thus, imported commodities are assumed to be separable from 8 See “TRADE: Developed Countries Should Contribute the Most,” Interview with Argentine trade negotiator Néstor Stancanelli, Geneva, June 6, 2008. Available at http://ipsnews.net/news.asp?idnews=42702 (retrieved June 19, 2008).


163

domestically produced goods and combined in an additional nest in the production tree. The elasticity of substitution in this input nest is equal across all uses. Under these circumstances, the firms decide first on the sourcing of their imports and based on the resulting composite import price, they determine the optimal mix of imported and domestic goods. The multiregion model therefore includes separate conditional demand equations for domestic and imported intermediate inputs (Brockmeier, 2001). As in the Michigan model applied by Brown et al. (2003) that incorporates different aspects of the New Trade Theory including increasing returns to scale, monopolistic competition, and product variety, the GTAP model operates in much the similar way and the database is formulated and solved using the GEMPACK software as illustrated in Harrison and Pearson (1996). This model takes into account the cross-sectional data from a single base period (2001), and imposes a detailed theoretical structure on the interactions among different data elements. The model is exploited by changing the shocks and observing how the remaining variables adjust. This is a comparative-static model that can be effectively used to analyze the reactions of the economy at a point in time. The results exhibit the difference between two alternative future states, with and without the policy shock. The model follows the GTAP world model developed by Hertel and Tsigas (1997) that allow for regional and industrial disaggregation and detailed treatment of taxes and subsidies. A.

Model Calibration

In order to estimate and simulate the effects of FTA, the author develops two scenarios: a base scenario with unaltered trade policies, and a free trade scenario among SAARC countries (SAFTA effects) as well as FTAs with the observer countries (SAFTA+5 effects). SAFTA, in this context, stands for those SAARC countries who are signatories to SAFTA Agreement. As aforementioned, the descriptor code “+5” is assigned for China, Japan, South Korea, the United States and the EU. The model evaluates the effects of both bilateral and plurilateral FTAs so as to precisely compare and contrast the extent of these effects quantitatively. The aggregations are set up with a view to investigating and testing five major effects under a number of different scenarios/experiments: (i) Effects of bilateral FTAs among SAARC countries; (ii) Effects of plurilateral FTAs among SAARC countries; (iii) Effects of bilateral FTAs of SAARC members as a single entity with +5; (iv) Effects of bilateral FTAs of individual SAARC countries with +5; and (v) Effects of plurilateral FTAs of SAARC members as a single entity with +5. B. Shocks, Closures and Solution Method Different combinations of shocks tms(i,r,s) [source-specific change in tax on imports of i from r into s], and txs(i,r,s) [destination-specific change in subsidy on exports of i from

164

PART IV GTAP Model

r to s] have been used on the basis of fixed, equal and varying tariffs pertaining to bilateral and plurilateral FTA arrangement (see detailed list of experiments and shocks in Appendices A17 to A21). Standard general equilibrium closures have been used: exogenous variables are population, the numeraire price of savings, all technological change shifters, all slack variables except the Walrasian slack variable, all policy variables, and all endowments (refer Section 6.2.D). The solution method used is the Gragg, steps = 2-4-6 (3-solution method) with automatic accuracy. This extrapolation solution method produce an error bound for each variable. In this case, the model is solved several times, each time with a successively finer grid. Hence, this procedure yields better accuracy than the Euler solution in less time(refer Section 6.2.E). C. Assumptions As generally accepted, the model also assumes the fixed expenditure shares dictated by the Cobb-Douglas regional expenditure function. That is, a rise in income implies an increase in savings and government expenditures, as well as private consumption. Additionally, capital and labor are assumed to be mobile across economic sectors with the assumption of full employment. The labor component is divided into skilled and unskilled labor, which is combined in a CES function to form a composite labor input, and sectoral output is a CES function of capital and composite labor. D. The Data and Software The GTAP 6 Aggregate Package (GTAPAgg6) is the main source of the data for simulations. The full GTAPAgg6 covers 87 countries or regions, 57 commodities or sectors, and five primary sectors. The database corresponds to the world economy based on 2001 benchmark. The GTAPAgg6 helps prepare an aggregation scheme and then uses the scheme to prepare an aggregated database for the GTAP economic model. The original sectoral data draw heavily on the source input-output (I-O) tables from varying years from respective countries. However, since the GTAP database was constructed by combining these tables with 1997 macroeconomic data, the flows reported may not be identical to those in the I-O tables. Thus, these figures are to be treated as estimates of inter-industry flows. The 57 sectors in the GTAP database may be aggregated into three broad sectors, viz., food and agriculture, manufactures, and services. The source of the basic labor-capital splits are the individual region I-O tables (Dimaranan & McDougall, 2002a, 2002b). The RunGTAP software program, version 3.40 has been used to run the general equilibrium simulations, which is designed to work with version 6.2 of the GTAP model and the GTAP 6 database. The RunGTAP is a visual interface to the GTAP model. It supports various versions of GTAP which are distinguished fundamentally by level of


165

aggregation. It incorporates a detailed treatment of international trade margins and other enhancements (GTAP, 2005a). E.

Aggregation Strategy

From 87 countries/regions and 57 sectors (including agriculture, manufactures and services), the GTAP dataset for this model is aggregated down to 10 regions and 20 sectors, respectively. Individual country/region are separated to the extent possible so as to distinguish the welfare and trade effects of policy changes by country/region and sectors based on similarities in factor shares and characteristics. The five primary factors include land, unskilled labor, skilled labor, capital, and natural resources. The regional and sectoral aggregation has been done with specific purpose and in the manner as follows: Selection of Countries/Regions The countries/regions are mapped into two different categories in order to effectively perform experiments for SAARC bloc as a single entity as well as individual countries. The details of regional aggregation are shown in Table 6.1 and 6.2 as follows: TABLE 6.1 REGIONAL MAPPING (A) S/N 1

New Code SAARC

Region Description South Asian Countries

2 3 4 5 6

CHN JPN KOR USA EU

China Japan South Korea United Sates of America European Union 27

7 ROW Rest of the World Source: Author, based on GTAP 6 database.

Comprising Old Regions Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, Sri Lanka China Japan Republic of Korea United States of America Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, The Netherlands, United Kingdom All other countries/regions

China, Japan, South Korea, the United States and the EU are selected because they are the current observers in the SAARC bloc, who have the likelihood of entering into FTA with SAARC countries in the future. Firstly, China, Japan and South Korea are three major economies in Asia. While China and South Korea are the newly industrializing economies (NIEs) with promising growth, Japan is the most developed economy in the East Asian region. The cooperation of SAARC countries with these three Asian giants could have a strong positive influence on its economic development. Secondly, the United States and the EU are two of the most powerful economies in the western world possessing highly developed technology and manufacturing sectors. The association with these countries could also mean an influential model of development for the SAARC countries. Although Iran is one of the observers, however, it is excluded

166

PART IV GTAP Model

from this study because it is and not represented as a separate country and is still included within the Rest of Middle East in the GTAP Database. TABLE 6.2 REGIONAL MAPPING (B) S/N 1 2 3 4 5 6 7 8 9

New Code BGD IND LKA RSA CHN JPN KOR USA EU

Region Description Bangladesh India Sri Lanka Rest of South Asia China Japan South Korea United Sates of America European Union 27

10 ROW Rest of the World Source: Author, based on GTAP 6 database.

Comprising Old Regions Bangladesh India Sri Lanka Afghanistan, Bhutan, Maldives, Nepal, Pakistan China Japan Republic of Korea United States of America Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, The Netherlands, United Kingdom All other countries/regions

Selection of Sectors As shown in Table 6.3, the original GTAP aggregate database containing the total of 57 sectors has been mapped to 20 sectors so as to reduce them to a manageable number of sectors. Attention has been given to combine similar sectors to build a new composite sector. Each sector is assigned with a new sector code that is explained in the new sector description column. The last column integrates all the comprising sectors as included in the GTAPAgg6 main database. The sectoral aggregation is broadly classified into three main sectors, viz., agriculture, manufacturing, and services. The selection of sectors has been done taking into account the diversified production patterns, comparative advantage, and structure of protections in SAARC countries. The study places greater emphasis on agricultural and manufacturing activities considering the importance of these sectors in this region because most of the commodities recorded in the sensitive list concentrate on these two sectors. Furthermore, the service sector in the GTAP database does not incorporate import tax rates and subsidies.


167

TABLE 6.3 SECTORAL MAPPING S/N

New Sector Description Comprising Old Sectors from GTAPAgg6 Agriculture 1 Crops Food and agriculture Paddy rice; wheat; cereal grains nec; products vegetables, fruit, nuts; oil seeds; sugar cane, sugar beet; plant-based fibers; crops nec; vegetable oils and fats; processed rice; sugar; food products nec. 2 Livestock Farm animals and products Cattle, sheep, goats, horses; animal products nec; wool, silk-worm cocoons. 3 Dairy Dairy and meat products Raw milk; meat: cattle, sheep, goats, horse; meat products nec; dairy products. 4 Forestry Forestry and logging Forestry. 5 Fishing Fishing and related activities Fishing. 6 Mining Mining and extraction Coal; oil; gas; minerals nec. Manufacturing 7 Beverages Beverages and tobacco Beverages and tobacco products. products 8 Manufactures Manufactures and recycling Wood products; paper products, publishing; manufactures nec. 9 Textiles Textiles and clothing Textiles; wearing apparel. 10 Leather Leather tanning and products Leather products. 11 Chemical Chemical and mineral Petroleum, coal products; chemical, rubber, products plastic prods; mineral products nec. 12 Automobile Automobiles and spares Motor vehicles and parts. 13 Metals Metals and metal products Ferrous metals; metals nec; metal products. 14 Electronics Office equipment and Electronic equipment. apparatus 15 Machinery Machinery and equipment Transport equipment nec; machinery and equipment nec. Services 16 Utility Basic utilities Electricity; gas manufacture, distribution; water. 17 Trade Retail and wholesale trade Trade. 18 Transport Transport and Transport nec; sea transport; air transport; communication communication. 19 Construction Construction works Construction. 20 Services Other services Financial services nec; insurance; business services nec; recreation and other services; public administration/defense/health/ education; dwellings. Source: Author, based on GTAP 6 database.

F.

New Code

Structure of Import Taxes and Export Subsidies

In small SAARC countries, such as Bangladesh, Bhutan, Maldives and Nepal (encapsulated under RSA), the domestic markets are much smaller than in the larger countries of India and Pakistan. The production structure, therefore, tends to be more specialized in fewer sectors. The commodity composition of trade and the specialization are largely determined by comparative advantage as per the availability of natural resources, traditional factor proportions and policies. Trade complementarities are therefore quite evident among small compared to large countries within the SAARC region. For example, Bangladesh protects its dairy, manufactures and textile industries, and therefore, levies high import tariffs on these three sectors.

168

PART IV GTAP Model

The analysis of this study lays special focus on the reduction of import tariffs and export subsidies mainly because these are the two most important protection measures available in quantifiable terms that influence trade in South Asia to a large extent. Although the importance of other trade barriers such as para-tariffs and non-tariff barriers (e.g., tariff rate quota, negative-list exceptions, ROO and destination requirements, administrative delays, customs clearance, restrictions on health safety, environmental, and religious reasons) are well recognized, the same cannot be taken into account, as these factors are not readily quantifiable within the purview of the GTAP analysis. These may be fruitful areas of interest for future research. Table 6.4 provides an overview of the average import tax rates levied by SAARC, +5 and rest of the world (ROW). The average import tax charged among the SAARC countries is 21.7 percent. Japan, United States, EU and ROW impose well below 10 percent. TABLE 6.4 IMPORT TAXES BY SOURCE S/N

Code

Country/Region

Bangladesh 1 BGD India 2 IND Sri Lanka 3 LKA Rest of South Asia 4 RSA China 5 CHN Japan 6 JPN South Korea 7 KOR United Sates of America 8 USA European Union 27 9 EU Rest of the World 10 ROW Source: Author’s calculation based on GTAP 6 database.

Import Tax Rates (Mean % ad valorem rate) 19.4 31.9 14.5 20.9 16.5 8.7 14.7 2.8 4.4 9.2

Import Tax by Source Tables 6.5, 6.6 and 6.7 provide the average import taxes levied in source countries. Clearly, the import taxes in the SAARC countries on an average are much higher than those charged by the non-members. The United States and the EU levies the lowest rates of all. India imposes the highest among the SAARC countries, followed by RSA, Bangladesh and Sri Lanka. 9 A careful scrutiny of the import taxes reveal that the adjacent neighboring countries levies higher taxes to one another in larger number of sectors (e.g., SAARC members to other SAARC members; similarly China to South Korea and vice-versa). More emphasis is being given to import taxes as opposed to export subsidies because export subsidies are not so significant in the SAARC region.

9 For a detailed breakdown of import tax rates by individual source country, see Appendices A13 and A14.


(i)

169

Among SAARC Countries TABLE 6.5 AVERAGE IMPORT TAXES BY SOURCE, % AD VALOREM RATE (A)

S/N

Sector

BDG

IND

LKA

RSA

ROW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

15.14 12.62 27.38 6.98 14.07 11.78 27.92 27.42 27.64 15.68 19.06 15.34 23.60 11.13 13.33 0.00 0.00 0.00 0.00 0.00

32.53 18.54 62.93 9.50 2.03 11.88 85.66 24.69 22.83 24.05 23.96 53.83 33.79 13.87 20.54 0.00 0.00 0.00 0.00 0.00

13.69 8.33 9.10 3.72 5.18 1.70 38.96 7.03 2.58 12.32 5.85 8.43 7.20 1.84 6.20 0.00 0.00 0.00 0.00 0.00

17.39 6.61 17.73 26.71 12.30 7.99 55.28 17.36 19.04 20.46 15.36 69.56 15.56 13.84 14.84 0.00 0.00 0.00 0.00 0.00

9.71 2.81 8.29 0.67 5.09 0.55 42.65 2.32 8.85 5.57 4.29 4.55 3.14 1.83 2.24 0.04 0.00 0.00 0.00 0.00

Mean

17.94

29.37

8.81

22.00

6.84

Note: Highest three import tax rates are highlighted in bold. Source: Author’s calculation based on GTAP 6 database.

(ii) SAARC as Single Entity TABLE 6.6 AVERAGE IMPORT TAXES BY SOURCE, % AD VALOREM RATE (B) S/N

Sector

SAARC

CHN

JPN

KOR

USA

EU

ROW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


23.43 13.53 35.52 11.83 8.03 16.74 74.98 25.17 18.85 21.36 23.03 36.89 27.06 15.14 18.27 0.00 0.00 0.00 0.00 0.00

33.60 8.91 16.84 5.31 12.62 1.80 47.67 12.82 18.42 10.68 12.35 35.07 7.95 10.58 12.03 0.00 0.00 0.00 0.00 0.00

20.47 2.78 40.69 0.93 4.23 0.05 22.36 1.04 8.17 11.24 1.31 0.00 0.77 0.00 0.08 0.00 0.00 0.00 0.00 0.00

85.04 5.88 31.52 2.61 15.73 2.10 37.38 6.14 9.39 6.28 6.98 7.80 4.00 1.21 5.62 0.00 0.00 0.00 0.00 0.00

3.01 0.37 5.83 0.27 0.26 0.06 1.89 1.14 9.86 10.05 2.37 1.41 1.64 0.27 1.12 0.00 0.00 0.00 0.00 0.00

11.62 1.57 16.57 0.21 2.56 0.05 11.90 1.30 6.27 5.26 2.31 4.55 2.42 1.03 1.14 0.35 0.00 0.00 0.00 0.00

10.02 4.74 14.77 3.03 3.61 1.44 36.80 6.19 12.61 5.67 5.38 11.65 5.58 2.11 6.01 0.15 0.00 0.00 0.00 0.00

Mean

24.66

16.44

8.78

15.18

2.64

4.61

8.65


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PART IV GTAP Model

(iii) SAARC as Individual Countries TABLE 6.7 AVERAGE IMPORT TAXES BY SOURCE, % AD VALOREM RATE (C) S/N

Sector

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Crops Livestock Dairy Forestry Fishing Mining Beverages

14.38

33.48

11.12

17.16

26.14

16.88

86.01

2.33

10.21

10.26

17.64

15.70

9.27

7.52

8.31

2.78

6.67

0.47

1.51

4.56

23.88

55.20

16.56

17.13

15.66

38.37

30.55

5.23

14.41

14.86

6.50

12.92

2.40

23.62

5.91

1.03

2.68

0.25

0.25

2.69

15.96

7.12

5.86

12.14

13.40

4.30

15.89

0.46

2.71

4.41

10.93

15.25

3.45

9.10

1.42

0.05

1.98

0.13

0.04

1.63

37.27

102.46

122.72

54.57

43.07

23.99

38.33

2.39

14.35

46.25

Manufactures

25.48

27.40

8.00

17.17

12.84

0.92

6.50

1.49

1.05

6.07

Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

29.38

25.73

1.95

18.19

17.25

6.73

9.26

10.25

5.74

13.26

22.07

26.03

14.50

19.23

10.83

10.22

5.90

9.41

4.54

6.31

16.81

27.51

7.07

14.86

14.51

1.37

6.89

2.27

1.81

6.32

17.16

50.87

9.67

58.90

36.00

0.00

7.83

1.19

3.57

10.34

21.33

33.87

8.10

16.30

10.97

0.79

3.95

1.66

1.75

5.73

13.77

15.25

2.89

13.85

10.90

0.00

1.91

0.26

0.83

2.32

11.27

23.32

6.19

14.42

11.86

0.08

5.69

1.15

0.86

5.08

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.35

0.15

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

0.00

Mean

18.92

31.47

15.32

20.94

15.94

8.27

14.38

2.60

4.27

9.35


Export Subsidies by Destination Tables 6.8, 6.9 and 6.10 show the average export subsidies by destination.10 As regards to export subsidies, the United States and the EU provide significant amount of subsidies in the agriculture sector, viz., crops, dairy and livestock. Hence, subsidies with respect to these two countries/regions become an important issue for consideration in our simulations.11

10 For a detailed breakdown of export subsidy by individual destination country, see Appendices A15 and A16. 11 Agricultural subsidies have been one of the main issues of contentions and common stumbling block in trade negotiations between developed and developing countries. This is because rich countries spend billions subsidizing their agricultural sector, leading to chronic overproduction and dumping of surpluses on global markets. Poor countries demand reform of this trade practice that impoverishes small-scale farmers while enriching large agri-business. In 2006, talks at the Doha round of WTO trade negotiations stalled because the United States refused to cut subsidies to a level where other countries’ non-subsidized exports would have been competitive. See “US blamed as Trade Talks end in acrimony,” by Alan Beattie and Frances Williams in Geneva, The Financial Times, July 24, 2006. Available at http://www.ft.com/cms/s/0/dfa460d0-1afd-11db-b1640000779e2340.html?nclick_check=1; and “Europe: Subsidies Feed Food Scarcity,” by Julio Godoy, Inter Press Service, Global Policy Forum, April 25, 2008. Available at http://www.globalpolicy.org/socecon/trade/subsidies/2008/0425feed.htm (retrieved September 11, 2008).


(i)

171

Among SAARC Countries

TABLE 6.8 AVERAGE EXPORT SUBSIDIES BY DESTINATION, % AD VALOREM RATE (A) S/N

Sector

BDG

IND

LKA

RSA

ROW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


0.53 0.05 1.55 0.00 0.00 0.00 0.44 -0.02 -0.02 0.26 -4.18 0.43 -0.11 0.02 -0.28 0.00 0.00 0.00 0.00 0.00

0.13 0.03 1.27 0.00 0.00 2.51 0.47 -0.01 -0.01 0.21 -4.79 0.42 -0.09 0.10 -0.17 0.00 0.00 0.00 0.00 0.00

0.45 0.06 0.84 0.00 0.00 -0.23 0.45 -0.09 -0.03 0.14 -3.37 0.05 -0.07 -0.12 -0.07 0.00 0.00 0.00 0.00 0.00

0.81 0.04 1.45 0.00 0.00 2.78 0.14 -0.08 -0.04 0.27 -5.78 0.42 -0.07 0.09 -0.10 0.00 0.00 0.00 0.00 0.00

0.46 0.05 1.26 0.00 0.00 2.28 0.24 -0.03 -3.99 0.14 -3.91 0.43 -0.12 0.10 -0.15 0.00 0.00 0.00 0.00 0.00

Mean

-0.10

0.00

-0.15

0.00

-0.25

Source: Author’s calculation based on GTAP 6 database.

(ii) SAARC as Single Entity TABLE 6.9 AVERAGE EXPORT SUBSIDIES BY DESTINATION, % AD VALOREM RATE (B) S/N

Sector

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


SAARC

CHN

JPN

KOR

USA

EU

ROW

1.55 0.21 5.85 0.00 0.00 -0.06 0.87 -0.07 -0.01 -0.07 -1.42 -0.05 -0.08 -0.03 -0.19 0.00 0.00 0.00 0.00 0.00

2.02 0.30 3.67 0.00 0.00 -0.12 0.91 -0.23 0.00 -0.04 -0.82 0.07 -0.23 0.03 -0.31 0.00 0.00 0.00 0.00 0.00

1.53 0.20 2.55 0.00 0.00 -0.04 0.92 -0.21 -0.01 -1.05 -0.47 0.05 -0.13 0.01 -0.04 0.00 0.00 0.00 0.00 0.00

1.54 0.31 3.51 0.00 0.00 -0.07 0.92 -0.11 -0.05 -0.66 -0.47 0.07 -0.09 0.02 -0.07 0.00 0.00 0.00 0.00 0.00

1.33 0.09 5.88 0.00 0.00 -0.06 0.91 -0.03 -6.07 -0.08 -0.57 0.00 -0.06 0.00 -0.02 0.00 0.00 0.00 0.00 0.00

0.37 0.06 0.79 0.00 0.00 -0.56 0.03 -0.11 -11.91 -1.61 -0.98 0.01 -0.13 -0.02 -0.11 0.00 0.00 0.00 0.00 0.00

1.68 0.23 5.70 0.00 0.00 -0.46 0.88 -0.11 -0.25 -0.41 -0.54 -0.05 -0.19 -0.01 -0.13 0.00 0.00 0.00 0.00 0.0


172

PART IV GTAP Model

(iii) SAARC as Individual Countries TABLE 6.10 AVERAGE EXPORT SUBSIDIES BY DESTINATION, % AD VALOREM RATE (C) S/N

Sector

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


1.23 0.33 4.59 -0.18 0.00 -0.04 0.92 -0.01 -0.02 0.25 -4.28 0.32 -0.28 -0.07 -0.13 0.00 0.00 -0.28 0.00 0.00

0.88 0.21 5.19 -0.02 0.00 0.47 0.90 -0.07 -0.01 0.13 -5.38 0.27 -0.06 0.07 -0.20 0.00 0.00 -0.14 0.00 0.00

1.37 0.11 6.87 -0.03 0.00 -0.09 0.92 -0.07 -0.04 0.12 -2.61 0.03 -0.04 -0.08 -0.09 0.00 0.00 -0.05 0.00 0.00

2.80 0.30 5.89 -0.20 0.00 0.21 0.70 -0.37 -0.07 0.28 -5.14 0.29 -0.07 0.06 -0.13 0.00 0.00 -0.07 0.00 0.00

2.02 0.30 3.13 -0.20 0.00 0.68 0.91 -0.16 -0.01 0.20 -4.78 0.37 -0.23 0.07 -0.14 0.00 0.00 -0.48 0.00 0.00

1.53 0.20 2.20 -0.49 0.00 1.11 0.92 -0.09 -0.07 -0.45 -4.24 0.35 -0.13 0.08 -0.08 0.00 0.00 -0.01 0.00 0.00

1.54 0.31 2.89 -0.30 0.00 0.02 0.92 -0.08 -0.06 -0.21 -3.66 0.37 -0.14 0.10 -0.09 0.00 0.00 -0.02 0.00 0.00

1.33 0.09 4.79 -0.04 0.00 -0.08 0.91 -0.03 -5.35 0.16 -3.73 0.32 -0.06 0.07 -0.07 0.00 0.00 -0.02 0.00 0.00

0.37 0.06 0.72 -0.58 0.00 -0.17 0.03 -0.06 -10.65 -0.82 -4.51 0.32 -0.19 0.06 -0.13 0.00 0.00 -0.06 0.00 0.00

1.68 0.23 4.77 -0.43 0.00 0.10 0.88 -0.07 -0.22 -0.02 -4.28 0.28 -0.19 0.07 -0.17 0.00 0.00 -0.05 0.00 0.00


6.4 SIMULATION SCENARIOS AND EXPERIMENTAL DESIGN Simulations are designed in such a way as to investigate the effects of SAFTA as well as FTAs with +5 on the basis of both bilateral and plurilateral FTA options. These simulation scenarios are depicted in Appendices A22, A23, A24, A25 and A26, and Figures 6.7, 6.8, 6.9, 6.10 and 6.11. The novelty of the analyses lies in the experiments that are highly exhaustive and attempt to encompass all major integration options, such as the regional trading arrangement among the SAARC members, bilateral FTAs of SAARC as a single entity, and FTAs of individual SAARC countries with +5. A.

What is New in Experiments?

In Section 6.2.H, two broad formula approaches to tariff reduction or negotiations were discussed, viz., tariff independent and tariff dependent formulae. In order to simplify the process of tariff reduction scheme, the author takes the combination of both the approaches in our experiments. The reduction is based on: (i) linear and fixed proportion for all trade commodities by contracting parties (hereinafter referred to as “fixed”); (ii) linear and equal reciprocity-based reduction for at least three highest/peak tariffs by contracting parties (“equal”); and (iii) varying and less than full reciprocity in reduction commitment for LDCs for at least three highest/peak tariffs (“varying”). Most CGE-based simulations apply either partial or full liberalization of tariffs without further decomposing the existing tariffs. Although comprehensive reforms and


173

full liberalization is the main goal of any FTA, even experts agree that full liberalization is extremely unlikely. The experiments in this study are novel in two respects: Firstly, for each set of simulation scenario, experiments have been decomposed and performed in three stages by applying: (i) fixed, (ii) equal, and (iii) varying tariff combinations. Second, each group of experiments is meticulously arranged with the intent of creating FTA negotiation scenarios as close to reality as possible. The experiments are carried out robustly with maximum possible combinations in a step-by-step method. The fixed and equal tariff combinations range between 0-10 percent, while the varying combinations range between 0-30 percent on a case-by-case basis. For example, the first stage of experiments begins with 10 percent, 5 percent, and 0 percent fixed tariffs for all traded commodities by all parties (plurilateral FTA). The second stage experiments have tariff combinations of 10 percent, 5 percent, 0 percent, but they are levied equally by both the contracting parties for a maximum of three of their most protected sectors in a reciprocal manner. The third stage experiments considers removal of protections in different combinations (for e.g., 30 percent-20 percent, 10 percent-5 percent, and 5 percent-0 percent), selecting up to three sectors that have the highest tariff rates. Where the tariff rates of LDCs are already below 30 percent, it starts from 20 percent; and where the non-LDCs/developed countries’ tariff rates are already below 20 percent, the next level starts from 10 percent or below, depending upon each case. The benefit of manipulating in this manner with different combinations is that it could partly reflect an actual negotiation process; this is also done particularly to see whether the preferential treatment (compensation by tariff concessions) given by nonLDCs/developed countries to LDCs/developing countries can provide higher gains. In other words, this is to investigate whether the lowering or the complete removal of tariffs from the most protected sectors will be welfare improving to all countries involved. Combinations are designed on a case-by-case basis depending upon the results of previous experiments. The final objective is to find the best possible combinations of tariff rates for viable FTAs, and also with a view to testing our two hypotheses: whether the compensation scheme from winners to losers will ensure all countries gain from FTAs, and whether FTAs of SAARC countries with +5 will be welfare improving, causing more trade creation than trade diversion. B. Why Bilateral and Plurilateral Effects? Bilateralism comprises the political, economic and cultural relations between two states, while regionalism constitutes more than two states that express a particular identity and shape collective action within a geographical region. Plurilateralism, on the other hand is in “between bilateralism and multilateralism, and indicates a policy of three or more countries concluding a regional economic agreement, and promoting trade liberalization” (Oyane, 2001, p. 9). Plurilateral agreements are the contractual

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PART IV GTAP Model

agreements that are made in between the states and/or blocs of diverse geographic regions. Plurilateralism provides the possibility of enabling relatively simple negotiations between multiple countries with common interests, and expanding in a domino effect the resultant liberalization (U.S. Council of Economic Advisers, 1995). Among the many trade agreements in the world, plurilateral agreements are one of the most important developments leading to some of the historical moments in international trade. Without restricting to any particular region of the world, plurilateral agreements have made their mark all over the world. Examples of plurilateral agreements include US-Middle East Free Trade Area (US-MEFTA) and Euro-Mediterranean Free Trade Area (EU-MEFTA) and the recently concluded ASEAN, Australia and New Zealand Free Trade Agreement (AANZFTA).12 As such, SAFTA+5 in this study typify these forms of plurilateral agreements. Today, the fad for free trade and economic integration is in effect questioning the virtues of bilateral versus multilateral trading system. Proponents have their own set of arguments for favoring their respective positions. Raihan and Razzaque (2007, p. 17) argue that bilateralism is trade-creating because countries can “lock-in” reforms via bilateral FTAs or RTAs, 13 which is often politically not executable under multilateralism. Moreover, while the multilateral trade talks are much more complex, trade liberalization can take place more easily through bilateral talks, since bilateral agreements have greater flexibility and ease that is lacking in most compromisedependent multilateral systems. On the flip side, Raihan and Razzaque (2007) as well point out that bilateral FTAs undermine the spirit of multilateralism. They affirm that there is a possibility of the discrimination against the excluded countries, and too much involvement in bilateral negotiations may distract attention from multilateral liberalization, and as such, the world might be divided into a few protectionist blocs, further strengthening the opposition to multilateral liberalization. Khor (2006) argues that multilateralism tends to have a systematic bias toward rich countries and multinational corporations, harming smaller countries which have less negotiating power. Furthermore, the “spaghetti bowl” phenomenon, as propounded by Bhagwati (2005, p. 28), can emerge because of the traversing of simultaneous bilateral trade negotiations. Nonetheless, Burfisher and Zahniser (2003) maintain that a country need not necessarily follow a stringent single policy towards liberalization in a fundamentally globalized world. Dual trade reforms involving bilateral and plurilateral trading arrangements form the best possible options for taking full advantage of liberalized economies. Multilateralism is clearly beneficial in that it engages virtually every country in the world in a mutual process of trade reform. In contrast, while the bilateral and plurilateral are exclusive and discriminatory, they are capable of much deeper trade 12 Multilateralism, on the other hand, is a term in international relations that refers to a large number of countries working in concert on a given issue. Good examples are the United Nations (UN) and the World Trade Organization (WTO). The antonym of multilateralism would be unilateralism, when one state acts on its own. 13 Bilateral FTAs are often referred to as RTAs as they fall within the domain of regional blocs.


175

reforms since their adherents are fewer, more like-minded and committed, and often linked geographically and historically. In reality, what is more dependable and creditable has always been the BTAs or FTAs as opposed to the “hard-to-compromise” multilateral WTO. In view of these contentions, simulations in this model take account of both forms of integration options so as to better comprehend and test the efficacy of these trade liberalization measures. Whether it is through bilateralism, plurilateralism or multilateralism, the rationale for trade liberalization is essentially the same in that free markets allow countries to specialize in the production of goods in which they have a comparative advantage. Moreover, the testing of both bilateral and plurilateral effects will allow us to compare the extent to which these integration options affect quantitatively. The results will be compared to find the type of arrangement that is most viable for SAARC as well as the observer countries. C. Why Percent Target Rate? The type of shock to tax variables is chosen as “percent target rate”. This option is preferred taking into account the tariff reduction schedule stipulated under the Trade Liberalization Programme (TLP) of the SAFTA Agreement. For instance, in Table 2.4, the targeted tariff reduction schedule under SAFTA is to reduce from the existing rates to 30 percent for LDCs and 20 percent for non-LDCs respectively; and ultimately reduce between 0-5 percent in equal annual installments within the specified time frames for respective country groups. Therefore, in the GTAP simulations, for example, the first experiment (particularly in varying tariff combinations) begins from 30 percent for LDCs - 20 percent for non-LDCs and ultimately down to 5 percent for LDCs - 0 percent for non-LDCs. However, if the tax rate is already below 20 percent, especially in the case of observer countries, then an appropriate rate (10 percent or 5 percent) will be taken to begin with – depending upon the case. D. Determining Highest Tariff Rate for Reduction The fundamental goal of the SAFTA Agreement is to lower the tariffs that are above 30 percent in case of LDCs and 20 percent in case of non-LDCs in a phased manner (as shown in Table 2.4). The tariffs are to be ultimately lowered to 0-5 percent in equal annual installments within the stipulated time frames set for LDCs and non-LDCs. In keeping with the objective of the Agreement, setting the highest tariffs at 30 percent for LDCs and 20 percent for non-LDCs is the most plausible step forward. E.

Diagrammatic Framework

The diagrammatic representation of different simulation scenarios are shown in Figures 6.7, 6.8, 6.9, 6.10 and 6.11.

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PART IV GTAP Model

FIGURE 6.7 PLURILATERAL FTA: AMONG SAARC COUNTRIES

Bangladesh

India

S A F T A Sri Lanka

RSA

FIGURE 6.8 BILATERAL FTA: AMONG SAARC COUNTRIES Bangladesh Under negotiation

Sri Lanka

Existing

India

Existing

Existing

Existing

RSA Existing

Note: Existing Bilateral FTAs are between Bhutan-India, Bangladesh-Nepal, Nepal-Sri Lanka, India-Maldives, Nepal-Pakistan, India-Nepal, India-Sri Lanka, Bangladesh-Bhutan, Pakistan-Sri Lanka, and India-Bangladesh. The FTAs under negotiation are between Bangladesh-Sri Lanka and Bangladesh-Pakistan. Source: Author.


177

FIGURE 6.9 PLURILATERAL FTA: SAARC AS SINGLE ENTITY AND +5

S USA

A A

EU

China

Japan

R C

South Korea

Note: This plurilateral FTA depicts the inter-regional FTA arrangement, similar to the ASEAN Free Trade Area (AFTA) or the Free Trade Area of the Americas (FTAA). Source: Author.

FIGURE 6.10 BILATERAL FTA: SAARC AS SINGLE ENTITY AND +5 China

S Japan

A A

South Korea

R C

USA

EU

Source: Author.

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PART IV GTAP Model

FIGURE 6.11 BILATERAL FTA: SAARC AS INDIVIDUAL COUNTRIES AND +5 China Bangladesh Japan India South Korea Sri Lanka USA RSA EU

Note: Bilateral FTAs under study or negotiation are India-China, India-Japan, India-South Korea, and India-EU. Source: Author.

F.

Simulation and Experiment Techniques

For developed countries/regions such as the United States and the EU, two sets of simulations are performed—the first set taking into account the impact of tariff removal, while the second set considers the removal of both tariffs and agricultural subsidies. It may also be noted that export subsidies among SAARC countries is not a key issue because it is almost non-existent. As such, the removal of export subsidies in the case of SAARC countries is not considered. SAFTA also recognizes the special needs of the LDCs and the importance of providing special and more favorable treatment and flexibility “… by adopting concrete preferential measures in their favor on a non-reciprocal basis” (SAFTA, 2004, p. 4). Therefore, simulations in this study are performed to encompass these objectives and principles as well. For instance, Article 3 of the SAFTA states: SAFTA shall be based and applied on the principles of overall reciprocity and mutuality of advantages… to benefit equitably all Contracting States, taking into account their respective levels of economic and industrial development, the pattern of their external trade and tariff policies and systems (SAFTA, 2004, p. 3).

Furthermore, as per the SAFTA agreement, the non-LDCs are required to bring their tariff duties down to 20 percent in the first phase of the two-year period ending in January 2008. In the final five year phase ending January 2013, the 20 percent duty will be reduced to 0-5 percent in a series of annual installment cuts. The LDCs have an additional three years to reduce the tariff rates to 0-5 percent by January 2016.14 In light 14

Refer to Table 2.4 for schedule of phased tariff cuts under the SAFTA Agreement.


179

of this, the simulations incorporate different stages of experiments bringing down the import duties in a piecemeal fashion (e.g., from 30 percent, 20 percent, 10 percent, 5 percent, and ultimately down to 0 percent). A simplified visual representation of the welfare experimentation model in Figure 6.12 illustrates this concept. The detailed lists of simulations and experiments based on this model are provided in Appendices A17, A18, A19, A20, and A21. FIGURE 6.12 WELFARE EXPERIMENTATION MODEL Intra-regional

S A F T A E F F E C T S

Inter-regional

Plurilateral FTA (10)

Fixed tariffs - all (3) Equal tariffs - highest three import taxes (3)

Varying tariffs - highest three import taxes (3)

Bilateral FTA (55)

Fixed tariffs - all (18) Equal tariffs - highest three import taxes (18)


C O ` N C L U S I O N

W E L F A R E E F F E C T S

Plurilateral FTA (38)

Fixed tariffs - all (9) Equal tariffs - highest

three import taxes (13)


S A F T A + 5

Bilateral FTA (10,148)

Fixed tariffs - all (3,49) Equal tariffs - highest

three import taxes (3,48) Varying tariffs - highest three import taxes (3,50)

E F E C T S

Note: The numbers in parenthesis indicate the number of experiments in each group of simulation. In “fixed” tariffs: maximum=10 percent, minimum=0 percent; in “equal” tariffs: maximum=10 percent, minimum=0 percent; and in “varying” tariffs: maximum=30 percent, minimum=0 percent. Source: Author.

6.5 SIMULATION RESULTS A.

Effects of SAFTA

The results of the simulations for plurilateral as well as bilateral FTAs among SAARC countries are shown in Appendices A22 and A23. These results estimate the welfare effects of the SAFTA as indicted by the Equivalent Variation (EV). The equivalent variation is the amount or percentage of additional income that consumers require to achieve the post-simulation level of utility given pre-simulation price level. In economics, it means “how much money would have to be taken away from the consumer before the price change to leave him as well off as he would be after the price change” (Varian, 2003, p. 255). A positive value indicates welfare improvement and a negative value denotes welfare deterioration.15 15 The value of the equivalent variation is given in terms of the expenditure function as: EV = e(p0,u1) − e(p0,u0) = e(p0,u1) − w = e(p0,u1) − e(p1,u1) where w is the wealth level, p0 and p1 are the old and new prices respectively, and u0 and u1 are the old and new utility levels respectively.

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PART IV GTAP Model

Welfare Gains and Losses (i) Fixed tariffs • Plurilateral: In the case of plurilateral FTAs among SAARC countries, the outcome of the experiments (S1a, S1b and S1c) show that Bangladesh is the biggest loser as we continue reducing the tariffs down to 0 percent, whereas India and RSA are the largest gainers. All other countries including ROW face welfare losses. This may be due to a significant trade diversion effect, as SAARC countries switch their trade from +5 and ROW to the SAARC bloc. • Bilateral: With regard to bilateral FTAs among SAARC countries, the first set of experiments (S1a, S1b and S1c) demonstrates that only India gains considerably through BDG-IND FTA, while all other SAARC members lose. Further reduction of tariffs lead to an increasing welfare for India and vice-versa for others. As for +5, only the United States becomes better off to some extent, but all others become worse off. BDG-LKA FTA (S2a, S2b and S2c) brings gain only to Sri Lanka; all others become worse off. In fact, the welfare to Sri Lanka increases as the import tariff is reduced from 10 percent down to 0 percent. RSA gains exclusively in BDG-RSA FTA (S3a, S3b and S3c) while all others become worse off, IND-LKA FTA produces mixed effects on other countries. In S4a, India loses along with RSA and the United States, but all others gain. Moving from S4a to S4b produces welfare losses to Japan, the EU and ROW. However, it is interesting to note that both India and Sri Lanka experience gains together with China in S4c at 0 percent-0 percent tariff level, while all others lose. INDRSA FTA in experiments S5a, S5b and S5c demonstrate that both India and the RSA gain significantly at the tariff combinations of 10 percent-10 percent and 5 percent-5 percent, but India faces a welfare loss at 0 percent-0 percent combination. With the exception of China, all others lose. This is an important finding because Pakistan is also within RSA group. As Pakistan holds a major share of trade in RSA group, it can be deduced that both India and Pakistan (along with the smaller economies such as Bhutan, Maldives and Nepal) will become better off by this FTA. LKA-RSA FTA (S6a, S6b and S6c) brings benefits mostly to Sri Lanka, although Bangladesh and China also share some welfare gains from this FTA. (ii) Equal tariffs • Plurilateral: In the second stage experiments from S2a through S2c in the case of plurilateral FTAs, fixed tariffs of 10 percent, 5 percent and 0 percent for three sectors with the highest import tax rates in each of the SAARC countries/group of countries demonstrate that Bangladesh is again the biggest loser, but the losses are not so significant. India, despite being the winner, does not gain as much as in the first stage. RSA reigns as the biggest winner though. Among +5, most of them lose with the exception of China in S2a. • Bilateral: With regard to the bilateral FTAs, BDG-IND FTA (S7a, S7b and S7c) shows that India’s welfare improves while that of Bangladesh and other SAARC members decline. Concurrently, the welfare of China, Japan, the United States and the


181

EU also improve. With BDG-LKA FTA (S8a, S8b and S8c), only Sri Lanka becomes better off while the opposite is true for Bangladesh. All others become worse off as a result of this FTA. Concerning BDG-RSA FTA (S9a, S9b and S9c), only RSA’s welfare improves, but Bangladesh along with all others becomes worse off. While India gains with IND-LKA FTA (S10a, S10b and 10c), Sri Lanka loses. China, Japan, South Korea and the EU also derive some benefits out of this FTA. RSA experiences significant gains via IND-RSA FTA (S11a, S11b and S11c)—more than double than that of India. Bangladesh and China also experience gains to some extent. In the case of LKA-RSA FTA (S12a, S12b and S12c), both Sri Lanka and RSA gain at 10 percent-10 percent combination; however, further reduction of tariffs results in loss to Sri Lanka. Interestingly, Bangladesh and China again derive some welfare gains. (iii) Varying tariffs • Plurilateral: The two sets of experiments with fixed distortions of 10 percent, 5 percent and 0 percent for all traded commodities in all SAARC countries found welfare loss for Bangladesh, together with most of the +5 countries and ROW. In order to find the best possible combinations for Bangladesh to be better off, a third set of experiments with varying tariff combinations was performed. This time, the LDCs (Bangladesh and RSA) impose a fixed tax rate of 30 percent to non-LDCs (India and Sri Lanka), while the non-LDCs impose tax at a descending rates of 20 percent, 5 percent and 0 percent. Interestingly, the experiment with 30 percent-20 percent tariff combination results in welfare gains for all SAARC countries except for Sri Lanka; while all non-members become worse off, China becomes better off. When the tax rate is lowered to 10 percent-5 percent, Bangladesh becomes worse off than Sri Lanka. All non-members as well become worse off. Lowering it further to 5 percent-0 percent is not an optimal combination, as only India and RSA become better off. • Bilateral: BDG-IND FTA (S13a, S13b and S13c) demonstrates that Bangladesh and India gain at 30 percent-20 percent combination. However, further reduction of tariffs result in the welfare loss to Bangladesh and welfare gain to India. Therefore, it is not beneficial for Bangladesh to have FTA at lesser than 20 percent tariff rate. China, Japan, South Korea, the United States and the EU also experience some welfare gains. As could be expected, Bangladesh and Sri Lanka has little trade. However, the FTA between these two countries brings some relief to Sri Lanka but not to Bangladesh. The welfare of all others does not improve as well. BDG-LKA FTA (S14a, S14b and S14c) brings gain only to Sri Lanka, while all others lose. Both BDG and RSA (S15a) experience welfare gain at 20 percent-15 percent tariff combination, but further reduction of tariffs to 15 percent-10 percent and 10 percent-5 percent leads to welfare loss to Bangladesh and gain to RSA (S15b and S15c). IND-LKA FTA (S16a, S16b and S16c) clearly demonstrates that both India and Sri Lanka gain, if the tariff is maintained at 10 percent-15 percent. Further reduction of tariff diminishes the welfare of Sri Lanka while increasing the welfare of India together with that of China, Japan, South Korea and the EU. This shows that Sri Lanka’s small

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PART IV GTAP Model

economy is no match for India’s strong presence in the region, which therefore necessitates the need for compensation by way of tariff concessions in order to derive full benefit out of this FTA. IND-RSA FTA (S17a, S17b and S17c) provides large gains to India as well as RSA. RSA in particular reaps significant gains from this FTA, as also Bangladesh and China do to some extent. The benefits to RSA are larger than that of India because the former gets free access to many protected sectors of the latter, which results in a significant improvement in their terms of trade. Other countries, however, do not experience much welfare gains. Finally, the LKA-RSA FTA (S18a, S18b and S18c) shows that both Sri Lanka and RSA gain when the tariff is reduced further. Yet again, Bangladesh and China’s welfare improves to some extent from this FTA. From these experiments, it might be safely surmised that the right combination of tariffs and FTA improves the welfare of both the contracting parties. A good example is the case of LKA-RSA FTA. Overall, this provides a strong support for our first hypothesis. Figure 6.13 depicts the case of the bilateral FTA effects. Clearly, the welfare of SAARC countries increases by nearly US$339 million. It appears that both +5 and ROW incur net welfare losses of about US$140 and US$54 million, respectively. This means that the feasible tariff combinations among SAARC countries would manifestly help increase trade flows within the bloc.16 The welfare losses for +5 and ROW can be explained by the fact that SAARC’s imports from +5 and ROW are diverted, as SAARC members augment their trade within the bloc. FIGURE 6.13 SAFTA: WELFARE GAINS AND LOSSES (BILATERAL – AMONG SAARC)

339.15

Region

SAARC

+5

-140.07

-54.42

ROW

-200

-100

0

100

200

300

400

Million US$

Source: Author.

Trade Creation and Trade Diversion Effects Table 6.11 shows the changes in export sales in 20 sectors of Bangladesh, India, Sri Lanka, RSA, China, Japan, South Korea, the United States, the EU, and the ROW under plurilateral SAFTA. In each cell, the positive value indicates increased volume of 16

See Table 6.16 for most feasible tariff combinations for bilateral FTAs.


183

exports, and the negative value indicates decreased volume of exports from the country designated in the row to the country designated in the column. TABLE 6.11 EXPORT SALES IN 20 SECTORS UNDER PLURILATERAL SAFTA BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

0.00 0.00

5.73 0.24 0.04 0.00 0.00 0.00 0.00 1.97 5.41 -0.01 4.36 0.00 4.07 -0.02 6.69 0.00 0.00 -0.02 0.00 -0.04

6.58 -0.09 0.00 0.01 0.00 5.31 0.04 5.42 372.01 1.51 12.13 1.20 1.58 0.01 7.70 0.00 -0.02 -0.15 0.00 -0.11

-1.65 -0.39 -0.02 0.00 -0.02 -1.87 -0.01 -2.39

-0.44 -0.01 0.00 0.00 0.00 -0.07 0.00 -1.77 -7.41 -0.02 -7.05

-9.91 -0.28 -0.86 0.00 0.00 -0.12 -0.01 -3.22 -4.92 -0.04

0.00 -0.03 -0.46 0.00 -0.55

-8.43 -0.14 -7.99 0.00 -0.06 -0.80 -0.20 -7.52 -8.49 -0.32 -29.93 -2.30 -6.57 -7.49 -59.35 -0.01 -0.07 -0.80 0.03 -0.79

-78.85 -0.87 -30.24 -0.09 -0.07 -34.00 -0.20 -24.94

0.00 0.00 -0.06 0.01 -0.03

-0.19 -0.02 -0.01 0.00 0.00 -0.26 0.00 -12.66 -50.00 -2.30 -17.72 -0.20 -17.99 -1.38 -24.63 0.00 0.00 -0.03 0.00 -0.02

BDG Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

182.69 2.49 64.45 0.05 1.72 34.20 0.70 83.30 322.51 5.33 305.89 20.41 147.17 58.62 251.17 0.00 0.00 -0.02 0.00 -0.06

-137.22

-0.30 -20.89 -1.79 -8.33 -13.61 -30.88 0.00 0.00 -0.03 0.00 -0.02

-11.36 -25.63

-2.08 -32.92

-12.03

-0.32 -0.89 -3.59 -12.09

-144.49

-0.95 -142.75

-1.80 -36.65 -23.74 -53.20 -0.01 -0.38 -0.65 0.01 -0.51

Sum

0.00

1480.62

28.42

413.13

-219.42

-88.84

-127.41

-49.32

-141.23

-574.38

IND Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

24.58 0.26 2.57 0.00 0.14 0.00 0.01 0.34 8.61 4.57 18.82 0.28 4.01 0.49 2.32 0.00 0.00 0.01 0.00 0.03

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

28.24 0.62 1.14 0.62 0.00 6.00 0.02 18.95 10.06 3.13 38.34 0.12 80.79 2.24 9.82 0.00 -0.06 -0.25 0.00 -0.33

280.93 1.89 617.35 0.04 0.00 1.77 5.34 55.27 147.88 6.36 459.59 0.07 413.23 0.20 18.35 -0.28 -0.29 -1.33 -0.03 -0.95

-1.35 0.22 -0.48 0.01 0.00 2.77 -0.04 0.14 -6.96 -0.22 -22.86 0.04 -7.12 3.02 3.07 0.01 1.83 0.11 0.00 0.31

-0.16 0.00 -0.29 0.00 0.00 0.04 0.00 0.12 -2.49 -0.01

-10.96

0.49 -8.14 5.41 14.52 0.08 0.82 2.43 0.01 10.35

-3.86 0.05 -12.67 0.08 0.00 1.35 -0.68 6.58 -9.62 -1.99 -42.89 2.95 -51.81 5.27 33.09 0.68 9.57 5.70 0.11 22.18

-78.67 0.38 -64.21 5.87 0.00 103.48 -0.49 2.32 -29.69 -1.91

1.09 8.52 0.00 1.82 0.96 0.01 1.42

-0.21 0.00 -1.32 0.00 0.00 0.01 -0.01 0.13 -7.04 -0.08 -5.23 1.59 -5.98 2.50 5.13 0.00 0.25 0.32 0.00 0.86

Sum

67.04

0.00

199.45

2005.39

-27.50

-8.81

-9.08

-10.60

-35.91

-210.92

0.01 0.00 0.00

114.98 0.36 2.31

0.00 0.00 0.00

27.34 0.00 0.01

-4.62 -0.02 0.02

-0.17 -0.01 0.00

-0.50 -0.01 0.00

-18.27

-5.79 -0.06 0.26

-39.12 -0.12 3.74

LKA Crops Livestock Dairy

-12.86

3.18 -10.16

0.01 -2.04 0.04 0.00 0.16 -0.05 0.60 -2.07 -0.23 -22.03

-0.01 0.01

-105.33

1.79 -104.37

14.70 16.47 0.49 9.16 4.49 0.16 14.44

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PART IV GTAP Model

Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

0.00 0.00 0.00 0.00 0.05 0.73 0.00 0.27 0.00 0.01 0.00 0.98 0.00 0.00 0.00 0.00 0.00

0.13 0.01 1.89 1.01 23.85 12.67 3.07 49.56 12.41 24.19 0.87 37.56 0.00 0.02 -0.01 0.00 -0.09

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.05 0.01 0.00 0.12 7.34 1.66 0.94 0.09 1.27 -0.01 1.42 0.00 -0.01 -0.10 0.00 -0.05

0.00 0.00 0.03 0.00 -0.35 -2.61 -0.63 -1.66 -0.19 0.54 0.20 -1.26 0.00 0.23 0.04 0.00 0.02

0.00 0.00 0.00 0.00 -0.23 -0.59 -0.41 -1.30 -3.69 0.45 0.22 -1.96 0.00 0.22 0.11 0.00 0.05

0.00 0.00 0.00 0.00 -0.62 -3.57 -0.23 -2.16 -0.14 0.73 0.17 -0.59 0.00 0.04 0.05 0.00 0.04

0.00 0.00 0.04 -0.01 -0.25 -1.00 -0.08 -1.46 -0.08 0.15 0.48 -0.41 0.01 0.23 0.52 0.01 0.49

0.11 0.00 0.08 -0.18 -5.39 -2.02 -2.67 -4.25 -1.88 2.29 1.51 -3.95 0.01 1.42 1.05 0.05 0.96

0.01 0.00 7.56 -0.17 -4.68 -8.64 -1.05 -18.55 -1.08 6.39 2.74 -3.63 0.01 3.03 0.81 0.04 0.59

Sum

2.05

284.79

0.00

40.08

-10.26

-7.31

-6.79

-19.63

-18.45

-52.12


30.88 0.00 0.11 0.00 0.00 0.00 0.15 0.13 5.21 0.40 5.12 0.23 0.13 0.15 1.52 0.00 0.00 0.02 0.00 0.11

209.06 0.32 2.44 2.71 0.05 44.19 21.18 22.78 30.58 1.81 249.91 343.35 47.86 7.79 145.54 0.00 0.13 0.06 0.02 0.24

33.36 0.40 6.30 0.03 0.01 0.22 0.32 5.22 130.40 0.32 9.33 2.23 3.62 1.21 5.94 0.00 0.00 -0.05 0.00 -0.01

-14.70 -0.11 -2.52 -0.82 0.00 -0.11 0.00 -2.58 -0.94 -0.02 -0.65 -0.15 -0.27 0.00 -0.26 -0.03 -0.03 -0.15 0.00 -0.13

0.11 0.25 0.13 0.00 0.01 0.04 -0.10 0.83 -10.51 0.57 -3.98 -13.15 3.95 2.75 1.05 0.02 0.81 0.37 0.06 0.91

0.02 0.00 0.02 0.00 0.00 0.00 -0.09 0.60 -1.74 0.00 -1.56 3.36 0.73 1.19 0.01 1.28 2.22 0.22 3.05

0.01 0.00 0.04 0.00 0.00 0.00 -3.04 0.61 -12.75 0.02 -2.50 -12.78 2.82 1.27 0.25 0.00 0.15 0.51 0.02 0.70

0.54 0.05 1.16 0.00 0.00 0.04 -0.25 1.05 -1.22 0.01 -2.17 -2.66 0.99 1.54 0.87 0.10 2.06 4.12 0.52 16.14

0.36 0.15 4.56 0.00 0.01 0.10 -1.81 3.78 -4.41 0.10 -9.13 -46.37 12.80 4.50 3.24 0.55 5.45 10.53 0.59 15.54

3.35 1.81 18.84 -0.02 0.01 20.96 -3.52 5.65 -22.22 0.45 -44.24 -22.03 30.18 12.50 1.92 0.38 4.70 7.16 0.48 10.20

Sum

44.16

1130.02

198.85

-23.47

-15.88

-56.71

-24.67

22.89

0.54

26.56

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals

0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.31 0.37 0.02 0.00 0.01

-2.51 -0.08 0.04 -0.02 -0.02 -15.69 -0.02 -3.83 -4.27 -1.04 -7.54 -0.06 -2.38

-0.09 0.00 0.00 0.00 0.00 -0.04 0.00 -0.03 -0.39 0.00 -0.08 0.00 0.00

-3.98 -0.03 -0.12 -0.03 -0.01 -1.11 -0.01 -0.19 -61.56 -3.69 -4.98 0.00 -0.15

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.11 0.01 0.01 0.00 0.00 0.10 0.00 0.51 13.63 0.16 2.61 0.15 1.39

0.04 0.00 0.02 0.00 0.00 0.01 0.00 0.84 7.11 1.18 3.21 0.08 1.22

0.37 -0.01 0.03 0.00 0.00 0.08 0.00 0.07 0.82 0.12 -0.16 -0.11 -0.40

0.03 -0.02 -0.03 -0.04 0.00 0.14 -0.02 -0.18 1.79 0.26 -0.66 -1.27 -1.00

1.94 0.23 0.35 -0.23 0.01 7.51 0.00 0.85 11.36 1.34 -0.89 -0.17 -1.63

RSA

-66.02

CHN

CHAPTER 6 Welfare Effects of SAFTA and SAFTA+5 Electronics Machinery Utility Trade Transport Construction Services

185

0.01 0.03 0.00 0.00 0.00 0.00 0.00

-0.18 -2.09 0.00 -4.70 -0.66 0.00 -0.93

-0.26 -0.03 0.00 -0.34 -0.26 -0.06 -0.73

-0.14 -1.14 -0.10 -1.26 -2.22 -0.13 -2.05

0.00 0.00 0.00 0.00 0.00 0.00 0.00

4.11 4.80 0.00 0.29 0.04 0.05 0.05

3.05 2.41 0.00 0.06 0.05 0.00 0.08

-1.76 -2.30 0.00 0.02 0.30 -0.01 0.32

-3.75 -6.83 0.00 -0.18 0.28 -0.12 0.26

-3.91 -1.89 0.02 2.88 0.72 0.00 0.58

Sum

0.82

-45.98

-2.31

-82.90

0.00

28.02

19.36

-2.62

-11.34

19.07


0.47 0.02 0.00 0.00 0.00 0.00 0.00 0.10 2.40 2.40 0.16 0.00 0.15 0.10 0.60 0.00 0.00 0.03 0.00 0.02

-11.00 -0.39 0.04 -0.04 -0.02 -5.72 -0.02 -11.59 -7.68 -0.67 -3.98 -0.13 -1.40 -0.13 -2.54 0.00 -4.85 -2.30 -0.01 -7.70

-3.04 0.00 -0.01 -0.02 -1.25 -0.59 0.00 -1.79 -1.61 -0.20 -0.97 -0.01 -0.09 -0.06 -1.60 0.00 -0.39 -1.82 -0.24 -1.71

-4.75 -0.58 -0.32 -0.10 -0.20 -0.44 -0.01 -1.62 -23.08 -2.43 -1.44 -0.01 -0.32 -0.02 -0.48 0.00 -2.22 -12.42 -0.50 -7.86

1.21 0.14 -0.02 0.02 0.13 0.78 0.00 1.30 11.47 0.37 0.20 -0.04 -0.34 -1.04 -0.56 0.00 0.20 0.15 -0.01 0.08

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.41 0.00 0.01 0.00 0.07 0.02 0.01 0.46 0.42 -0.03 1.00 0.07 0.66 2.19 1.27 0.00 0.09 0.29 0.00 0.39

1.24 0.16 -0.67 0.00 0.03 0.19 -0.13 0.09 0.18 -0.02 -0.90 -0.64 -0.45 -3.96 -5.18 -0.01 0.08 0.98 -0.10 0.70

0.09 0.07 -0.61 0.00 0.08 0.02 -0.17 -0.45 0.64 -0.70 -2.07 -2.15 -0.81 -2.31 -4.35 -0.03 -0.02 1.49 -0.89 -0.09

4.17 0.27 0.07 -0.03 0.50 3.21 -0.02 1.06 0.93 0.04 -1.69 -0.27 -2.72 -7.01 -2.06 -0.03 1.24 3.08 -0.11 1.94

Sum

6.45

-60.13

-15.40

-58.80

14.04

0.00

7.33

-8.41

-12.26

2.57


0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.54 1.04 0.12 0.02 0.05 0.00 0.04 0.00 0.00 0.02 0.00 0.01

-2.79 -0.02 0.02 -0.02 0.00 -2.99 0.00 -0.52 -4.92 -0.88 -2.19 -0.35 -2.07 -0.09 -0.60 0.00 -0.49 -1.04 0.00 -4.22

-0.23 0.00 0.00 0.00 0.00 0.00 0.00 -0.04 -1.54 -0.02 -0.10 0.00 0.00 -0.05 -0.06 0.00 -0.05 -0.95 0.00 -0.32

-2.58 -0.02 -0.06 -0.02 0.00 -0.23 0.00 -0.35 -32.34 -6.78 -0.27 0.00 -0.44 -0.01 -0.22 0.00 -0.27 -4.11 -0.03 -1.36

0.93 -0.02 0.00 0.00 0.00 0.39 0.00 -0.15 12.03 1.68 -0.23 -0.09 -0.27 0.32 -0.04 0.00 0.00 0.06 0.00 0.02

0.13 0.00 0.00 0.00 -0.01 0.01 0.00 0.05 2.56 0.13 0.60 0.00 0.38 3.13 2.90 0.00 0.01 0.49 0.00 0.14

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.92 -0.27 -0.14 -0.02 0.00 0.02 -0.02 -0.34 0.84 0.41 -0.96 -0.15 -0.42 -0.84 -2.18 -0.01 -0.05 0.97 0.00 0.27

0.13 -0.03 -0.10 0.00 0.00 0.00 -0.09 -0.41 3.14 1.53 -1.26 -0.36 -0.88 -0.60 -2.19 -0.02 -0.23 1.87 -0.01 0.02

1.86 -0.06 -0.01 -0.15 0.00 -2.78 -0.01 -0.64 3.67 0.77 -1.59 -0.11 -1.45 -0.26 -0.41 -0.02 -0.06 1.90 0.00 0.68

Sum

1.85

-23.17

-3.36

-49.09

14.63

10.52

0.00

-1.97

0.51

1.33

2.02

-16.04

-1.44

-8.24

0.46

0.19

0.11

0.00

0.68

11.67

JPN

KOR

USA Crops

186

PART IV GTAP Model

Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

0.01 0.00 0.00 0.01 0.00 0.00 0.93 242.55 1.30 10.00 0.01 0.06 0.01 0.06 0.00 0.00 0.03 0.00 0.35

-0.10 0.11 -0.13 -0.02 -1.33 -0.15 -81.87 -62.60 -5.84 -18.77 -2.66 -13.77 -2.29 -15.42 -0.02 -1.02 -6.28 -0.02 -23.44

Sum

257.34


0.00 -0.03 -0.03 -0.07 -0.02 -0.01 -5.69 -11.14 -6.37 -0.01 -0.04 -0.21 -0.84 0.00 -0.27 -5.40 -0.02 -3.77

-0.24 -0.80 -0.27 -0.14 -2.43 -0.05 -19.29 -369.58 -2.50 -1.68 -0.14 -4.24 -0.13 -5.82 -0.46 -3.49 -32.84 -0.62 -29.50

0.02 0.00 0.01 0.01 0.16 0.01 18.44 23.44 10.01 1.99 0.01 1.10 0.58 2.91 0.00 0.09 1.06 0.00 0.55

0.00 0.01 0.00 0.00 0.01 0.01 3.29 2.13 0.02 5.09 11.93 2.26 9.40 18.73 0.00 0.06 0.95 0.03 1.52

0.00 0.01 0.00 0.00 0.00 0.01 1.67 9.76 0.11 1.21 3.31 1.56 7.63 3.84 0.00 0.09 1.02 0.00 1.84

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

-0.06 -0.34 0.01 0.01 0.20 -0.21 5.84 17.00 0.93 -1.44 -8.06 -1.18 -5.70 -17.36 0.00 -0.01 8.39 0.10 12.18

0.31 1.02 0.20 0.17 -3.45 0.15 33.44 146.18 4.27 3.59 -5.19 4.47 -12.48 3.30 0.27 2.64 12.85 0.12 12.88

-251.66

-143.36

-482.46

60.85

55.63

32.17

0.00

10.98

216.41

3.66 0.00 0.22 0.00 0.00 0.01 0.00 0.22 298.18 8.35 3.08 0.02 0.08 0.10 0.80 0.00 0.02 0.13 0.00 0.16

-19.91 -0.47 0.30 -0.49 -0.05 -6.69 -0.09 -40.74 -101.79 -29.83 -22.96 -3.65 -13.93 -2.95 -23.52 -0.15 -24.65 -14.81 -0.06 -67.81

-7.85 0.00 -0.08 -0.32 -0.18 -0.39 -0.02 -17.30 -54.25 -2.20 -6.12 -0.15 -0.51 -0.38 -3.73 -0.01 -2.12 -10.29 -0.73 -10.94

-28.96 -3.75 -1.56 -0.42 -0.18 -2.84 -0.05 -25.99 -270.57 -28.94 -3.87 -0.14 -4.60 -1.42 -13.10 -2.82 -8.38 -55.88 -1.26 -30.62

0.79 0.23 0.04 0.01 0.02 0.21 0.02 7.60 14.57 8.33 1.74 0.05 0.62 2.53 3.36 0.02 1.95 1.40 0.04 0.97

0.09 0.01 0.02 0.00 0.00 0.01 0.00 3.35 1.63 0.05 3.78 4.80 1.07 9.34 15.10 0.00 2.45 3.69 0.77 3.99

0.08 0.00 0.01 0.00 0.00 0.00 0.00 0.53 2.39 0.27 0.87 1.96 0.62 5.38 6.10 0.01 0.63 2.17 0.02 3.75

2.30 0.16 0.07 0.05 0.00 0.33 0.04 4.25 3.09 0.52 2.79 -0.30 0.14 -1.62 0.67 0.07 1.94 11.68 -0.04 16.71

19.35 1.46 -1.09 0.39 0.14 1.55 -0.31 28.95 106.07 23.74 -3.13 -24.34 -16.85 -33.47 -43.00 0.46 5.47 17.85 -0.81 18.58

20.29 1.34 1.97 0.55 0.26 -4.18 0.18 16.39 47.93 13.14 4.26 0.55 2.82 1.52 6.70 1.21 9.52 25.96 0.11 24.15

Sum

315.03

-374.25

-117.57

-485.35

44.50

50.15

24.79

42.85

101.01

174.67

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical

1.37 0.02 0.05 0.00 0.06 0.00 0.01 0.25 47.05 5.97 4.85

-43.20 -0.74 2.49 -0.22 -0.12 -8.60 -0.39 -58.84 -90.39 -10.71 -53.52

-22.08 0.00 -0.10 -0.06 -0.09 -0.51 -0.09 -5.95 -10.68 -0.44 -2.89

-102.16 -1.09 -3.32 -0.29 -0.92 -17.04 -0.11 -17.17 -276.20 -16.46 -10.32

3.70 0.08 0.01 0.01 0.05 0.68 0.03 6.51 64.04 9.89 2.60

1.97 0.01 0.01 0.00 0.01 0.05 0.07 2.96 7.82 0.19 7.61

0.47 0.01 0.02 0.00 0.00 0.03 0.02 1.69 16.37 0.37 3.72

21.35 0.09 -0.94 0.14 0.05 0.62 -0.03 10.00 36.09 0.61 2.60

13.29 -0.09 -3.74 0.03 0.10 0.32 -0.44 9.41 52.95 1.44 -6.06

48.49 0.55 0.48 0.20 0.27 -1.20 0.06 13.99 61.59 3.38 1.73

-108.00

EU

ROW

CHAPTER 6 Welfare Effects of SAFTA and SAFTA+5 Automobile Metals Electronics Machinery Utility Trade Transport Construction Services Sum

0.01 0.22 0.03 1.50 0.00 0.01 0.13 0.01 0.25

-4.33 -36.95 -15.52 -38.52 -0.18 -9.94 -11.09 -0.19 -45.41

-0.02 -0.32 -2.23 -2.08 -0.01 -0.97 -6.60 -0.30 -11.63

-0.28 -5.39 -0.66 -23.31 -3.26 -4.82 -35.68 -0.80 -28.17

0.00 1.09 1.79 3.21 0.08 0.45 0.72 0.01 0.63

6.39 6.65 15.41 27.81 0.00 0.81 2.50 0.26 2.72

2.20 3.60 11.43 11.50 0.01 0.24 1.17 0.01 2.16

61.79

-426.37

-67.05

-547.45

95.58

83.25

55.02

-9.00 0.33

187

-9.55 0.07 0.43 4.75 -0.06 8.66

-10.41 -4.83 -15.45 -31.49 -0.16 0.04 6.82 -0.61 7.42

-1.58 3.39 -6.39 2.17 0.71 3.23 11.21 -0.01 12.36

56.16

18.54

154.63

-10.05

Note: The value in each cell represents changes in export sales of 20 sectors from the country/region in the row to the country/region in the column. The unit is US$ million. Source: Author.

First, the result in terms of India’s import changes in the crops sector is examined. India increases its import of crops by US$182.69 million from Bangladesh, US$114.98 million from Sri Lanka, and US$209.06 from RSA under the plurilateral SAFTA scenario. The total increase in imports of crops accounts for US$506.73 million. On the contrary, India decreases its import of crops by US$2.51 million from China, US$11 million from Japan, US$2.79 million from South Korea, US$16.04 million from the United States, US$19.91 million from the EU, and US$43.20 million from ROW, which adds up to US$95,45 million. The difference between the increase in trade volume of crops (U$506.73) and a decrease in trade volume of crops (US$81.94) is the trade creation effect due to SAFTA, which is equal to US$ 411.28 million. Similarly, RSA also increases its total trade volume with Bangladesh, accounting for US$413.13 million, with India accounting for US$2,005.39 million, and with Sri Lanka accounting for US$40.08 million. At the same time, RSA decreases its trade with other RSA members accounting for US$23.47 million, with China: US$82.90 million, Japan: US$58.80 million, South Korea: US$49.09 million, the United States: US$482.46 million, the EU: US$485.35 million, and ROW: US$547.45 million. The net trade creation effect is US$729.08 million (2,458.60–1,729.52). This indicates that there is a significant trade creation effect particularly among the SAARC members under the SAFTA scenario. As for non-members, China decreases its trade with SAARC members and increases its trade with the outside world. For instance, China decreases its trade with Bangladesh by US$219.42 million, with India by US$27.50 million, with Sri Lanka by US$10.26 million, and with RSA by US$15.88 million. Therefore, the total decrease in trade volume of China’s trade with the SAARC members adds up to US$273.06 million. This decrease represents the trade that is diverted away from the SAARC region as a result of the SAFTA, and so it is termed as the trade diversion effect. Changes in Industry Output, Private Household Demand, Aggregate Exports and Imports The launching of SAFTA has major impacts on the output of industry, household demand and exports and imports of seven SAARC countries. Table 6.12 shows the

188

PART IV GTAP Model

effects of the SAFTA on these variables. The industry output of SAARC countries shifts significantly under the SAFTA scenario. Bangladesh’s agriculture and service industries shrink, while the manufacturing sector expands. Both India and Sri Lanka’s manufacturing and service sector expands, but their agriculture sector declines. The case of RSA is just the reverse: the agriculture sector expands while the manufacturing and service sectors decline. As expected, little impact is observed as far as the non-members are concerned. Considering the resource endowments of each of the countries, the changes in the pattern of production are not surprising. Bangladesh, India, and Sri Lanka are continuously moving away from the traditional agriculture to more broadbased growth in the manufacturing sector. For example, the SAFTA scenario expands Bangladesh’s textile sector by US$498 million. Likewise, India emerges as the major supplier of chemical (US$346.7 million), automobile (425.6 million) and machinery (381.5 million). Agriculture still stands as a dominant sector for RSA. To the extent that private household demand is concerned, the demand for both agriculture and manufactured products in all SAARC countries increases. There is a rise in demand for services especially in India, Sri Lanka and RSA with the exception of Bangladesh, but there is a decline in demand in the case of all non-members. Aggregate exports and imports in agriculture and manufacturing sectors increase in all SAARC countries, while the reverse is true for the non-members. This is not surprising. With free trade, the volume of trade within the bloc will increase and therefore the aggregate household demand will be augmented. Based on the comparative advantage, India and Sri Lanka are better placed in terms of their service-based industries with a pool of educated youth; hence, the increase in demand for services is expected. On the other hand, this also means that trade from the non-members will be diverted away, which will impact the non-members’ household demand negatively. TABLE 6.12 SAFTA: CHANGES IN INDUSTRY OUTPUT, PRIVATE HOUSEHOLD DEMAND, AGGREGATE EXPORTS AND AGGREGATE IMPORTS Changes in Industry Output of Commodity (US$ million) qo Crops Livestock Dairy Forestry Fishing Mining Agriculture Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Manufacturing

BDG -65.3 -3.8 -23.3 -1.5 -14.6 -10.8 -119.2 -1.8 -46.2 498.0 22.3 -45.5 -0.4 -48.5 -2.4 -28.6 346.9

IND 169.8 9.7 -583.1 -5.2 1.3 -17.8 -425.3 22.3 -134.2 1.6 -61.6 346.7 425.6 33.7 34.3 381.5 1049.8

LKA -43.7 3.0 14.9 1.1 0.6 1.9 -22.2 -0.1 -8.5 -48.4 -10.4 40.9 1.1 85.6 0.3 13.1 73.6

RSA -159.1 9.1 661.5 0.9 5.9 -23.4 495.0 -9.9 -39.7 -783.0 -63.5 260.6 -161.3 424.3 -17.2 -133.1 -522.7

CHN 7.5 5.9 4.6 0.2 0.1 5.4 23.7 -0.6 37.6 12.8 41.8 -60.8 -23.7 -27.1 -5.7 -34.1 -59.8

JPN 12.7 0.7 2.0 0.6 1.0 1.9 18.9 -0.9 18.9 40.2 1.6 1.3 -103.9 -37.7 42.5 44.9 6.8

KOR 4.0 -0.8 -1.6 0.0 0.1 0.5 2.1 -3.4 -8.5 -28.2 1.1 -24.3 -6.9 -15.0 35.4 18.7 -31.1

USA -3.1 -0.6 -5.1 1.2 0.1 4.4 -3.2 -1.2 43.4 205.6 4.4 -24.4 -48.6 -32.5 -42.0 -60.8 44.0

EU 37.4 -1.3 -26.7 2.1 0.5 2.2 14.2 -5.6 61.3 255.0 37.6 -128.3 -118.9 -137.9 -87.5 -172.3 -296.5

ROW -48.9 -15.4 -85.8 8.6 0.0 40.4 -101.1 -8.1 76.4 256.2 32.2 -345.0 -43.2 -128.1 -29.5 -35.8 -224.8

CHAPTER 6 Welfare Effects of SAFTA and SAFTA+5 Utility Trade Transport Construction Services Services

-16.7 -26.2 -48.6 105.2 -80.1 -66.5

56.0 21.8 39.0 165.4 -114.2 168.0

19.1 12.9 -17.2 47.7 -28.2 34.3

24.7 -4.9 -158.2 206.1 -103.4 -35.7

-3.0 7.4 7.5 -26.0 2.3 -11.8

-1.6 -11.1 48.0 -94.0 3.5 -55.2

-3.2 -1.8 26.9 -12.2 2.3 12.0

-0.4 -33.5 64.5 -97.0 32.0 -34.4

189

-2.9 -1.5 196.1 -114.5 47.5 124.8

-22.4 0.6 159.6 -124.0 55.8 69.6

Changes in Private Household Demand for Imports (US$ million) qpm

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

Crops Livestock Dairy Forestry Fishing Mining Agriculture Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Manufacturing Utility Trade Transport Construction Services Services

44.2 0.7 13.2 -0.1 2.0 0.0 60.1 0.3 5.2 80.4 2.1 15.7 0.0 0.0 0.0 0.6 104.3 0.0 -0.1 -1.8 0.0 -0.8 -2.7

216.4 3.1 736.9 7.9 0.2 0.3 964.8 3.7 13.5 82.8 7.6 69.0 0.8 2.3 4.8 1.8 186.3 0.3 13.1 0.2 0.0 4.0 17.6

80.9 0.2 6.5 0.1 0.1 0.0 87.7 0.8 3.7 7.9 0.0 4.8 0.9 1.5 0.5 0.4 20.4 0.0 0.0 0.1 0.0 3.1 3.2

227.5 3.0 31.2 2.4 0.1 0.0 264.2 20.5 7.6 64.2 4.2 47.2 19.5 4.4 5.6 6.4 179.7 0.5 0.0 0.1 0.0 15.4 16.0

-0.8 0.0 -0.1 0.0 0.0 0.0 -0.9 0.0 -0.3 -6.1 -0.9 -0.3 0.0 0.0 -0.1 -0.1 -7.8 0.0 -1.8 -0.5 0.0 -0.3 -2.6

-4.7 0.0 -1.2 0.0 -0.3 -0.1 -6.3 -0.3 -3.8 -14.1 -1.7 -2.3 -1.1 -0.5 -1.7 -1.4 -26.9 0.0 -5.5 -5.9 0.0 -2.3 -13.8

-2.4 0.0 -0.2 0.0 0.0 -0.2 -2.9 -0.1 -0.6 -5.4 -1.2 -0.5 -0.1 -0.1 -0.8 -0.2 -9.1 -0.1 -0.6 -1.4 0.0 -1.3 -3.4

-5.8 0.0 0.0 0.0 0.0 0.0 -5.9 -0.2 -14.9 -94.1 -3.6 -2.4 2.7 -0.2 1.3 0.2 -111.3 -0.1 -1.3 -6.7 0.0 -9.7 -17.8

-11.2 -0.2 0.1 -0.1 -0.1 -0.4 -11.9 -0.2 -8.7 -39.6 -8.7 -2.2 -0.3 -0.2 1.1 -0.2 -59.0 -0.4 -3.6 -8.2 0.0 -7.7 -19.9

-47.7 -0.3 -3.0 0.0 -0.4 -0.1 -51.6 -1.2 -13.3 -72.8 -6.9 -6.1 -1.9 -1.3 -1.9 -2.8 -108.0 -1.0 -3.7 -12.1 0.0 -15.5 -32.3

qxw

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

Crops Livestock Dairy Forestry Fishing Mining Agriculture Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Manufacturing Utility Trade Transport Construction

63.1 0.3 3.0 0.0 0.2 0.0 66.6 0.2 2.0 605.6 24.4 42.5 0.6 4.7 0.9 7.9 688.7 0.0 0.0 0.4 0.0

411.3 1.4 72.2 2.0 1.5 39.3 527.6 22.2 -67.5 81.3 -38.7 496.4 365.0 148.7 46.1 351.6 1405.1 -0.4 -45.5 -42.4 -0.3

32.6 1.3 7.3 0.2 -1.6 4.3 44.1 0.2 -4.7 -30.9 -10.5 40.1 2.1 87.5 0.3 14.2 98.4 0.0 -4.2 -26.5 -1.4

149.5 -4.0 608.7 -1.9 -1.4 -17.2 733.7 5.2 -6.4 -564.7 -51.1 479.9 0.6 400.7 -2.2 -16.9 245.0 -7.0 -20.8 -151.9 -3.4

-0.4 0.5 -0.3 0.1 0.2 3.2 3.2 -0.1 31.9 -18.7 29.7 -43.1 -15.2 -8.8 -3.5 -19.1 -46.8 0.1 5.6 8.6 0.1

1.7 0.0 -0.2 0.0 0.0 0.1 1.7 0.0 8.9 15.5 0.1 -3.1 -54.6 -20.2 41.4 44.2 32.1 0.0 6.9 42.6 1.3

0.2 0.0 -1.2 0.0 0.1 -0.2 -1.1 -3.0 -7.4 -36.8 -0.7 -17.6 -3.9 -12.8 32.3 5.3 -44.6 0.0 1.6 24.3 0.1

-12.4 -0.1 -3.3 0.2 0.1 1.4 -14.2 -0.5 12.3 31.8 1.3 -34.3 -12.8 -8.7 -14.4 -15.7 -41.0 0.3 5.5 47.1 0.3

15.8 1.3 -17.4 0.6 0.3 3.0 3.5 -4.1 40.6 157.2 22.3 -100.8 -94.2 -68.8 -57.5 -132.2 -237.4 1.5 21.4 170.4 -1.6

-104.6 3.9 -68.0 6.3 1.2 97.2 -64.1 -4.0 43.6 68.5 20.0 -310.6 -29.9 -99.8 -22.4 -30.6 -365.4 3.0 36.0 147.1 0.8

Changes in Aggregate Exports, FOB weights (US$ million)

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PART IV GTAP Model

Services Services

0.9 1.4

-149.4 -238.0

-29.5 -61.5

-100.8 -283.8

3.5 17.9

12.9 63.8

9.8 35.7

53.1 106.3

76.3 268.0

77.3 264.2

Changes in Aggregate Imports, market price weights (US$ million) qim

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW


116.1 1.2 24.7 0.0 2.8 -0.7 144.2 0.3 35.7 338.7 3.1 85.4 2.3 54.7 5.1 47.3 572.6 0.0 -0.5 -2.2 0.1 -2.2 -4.9

263.1 4.3 839.1 8.1 0.2 143.4 1258.2 4.5 106.2 117.4 10.2 377.1 15.2 332.4 40.8 138.2 1142.1 1.0 23.1 12.4 0.3 48.3 85.1

100.9 0.2 7.3 0.3 0.1 11.0 119.6 0.8 14.2 2.6 -0.9 22.7 5.8 40.5 6.5 29.7 121.9 0.0 5.2 2.5 0.1 2.0 9.8

313.0 3.2 36.2 2.5 0.1 80.8 435.7 21.3 45.3 126.7 4.3 224.0 190.3 124.5 36.7 179.7 952.9 1.0 14.6 24.8 1.9 46.8 89.0

-3.9 0.1 0.4 -0.4 0.0 -11.2 -15.0 -0.1 -2.1 -33.7 -1.5 -10.5 -1.9 -3.4 -3.1 -7.9 -64.2 -0.1 -3.3 -1.8 -0.3 -2.5 -7.9

-11.3 -0.4 -2.2 -0.2 -0.8 -3.3 -18.2 -0.4 -12.5 -18.2 -1.8 -10.7 -3.3 -5.6 -12.5 -15.3 -80.1 -0.1 -5.9 -10.7 -1.9 -14.4 -32.9

-6.1 -0.5 -0.4 -0.2 0.0 -6.6 -13.8 -0.2 -2.7 -19.2 -2.2 -6.7 -1.2 -5.6 1.7 -2.9 -39.0 -0.1 -1.2 -0.9 0.0 -4.8 -6.9

-11.3 0.0 0.0 -0.2 -0.1 -7.3 -18.9 -0.3 -42.2 -134.1 -2.8 -8.0 -0.6 -10.7 -3.2 -10.8 -212.7 -0.2 -2.0 -20.7 -0.4 -27.9 -51.1

-17.8 -1.2 0.7 -0.3 0.0 -13.6 -32.2 -0.2 -23.1 -27.7 -7.4 -21.3 -21.1 -32.2 -20.8 -51.4 -205.3 -1.2 -13.4 -18.9 -2.0 -41.8 -77.2

-99.8 -1.3 -5.3 -0.2 -0.9 -28.9 -136.4 -1.4 -37.6 -117.4 -6.8 -56.2 -18.4 -38.2 -22.7 -63.8 -362.5 -2.8 -10.6 -26.6 -1.7 -51.6 -93.2

Source: Author’s simulation using GTAP 6 Database.

Regional Changes in Terms of Trade, GDP Indices, and Allocative Efficiency Table 6.13 illustrates the regional changes in terms of trade, GDP indices, and allocative efficiency. TABLE 6.13 SAFTA: REGIONAL CHANGES IN TERMS OF TRADE, GDP INDICES, AND ALLOCATIVE EFFICIENCY Change in GDP Allocative Change in Quantity Index Efficiency Terms of Change GDP (US$ million) (Regional EV) Trade (%) Price Index (%) BDG -1.22 -0.86 -112.69 -112.47 IND 0.28 0.34 166.41 166.41 LKA 0.92 1.12 21.23 21.24 RSA 2.47 2.99 72.66 72.69 CHN -0.01 -0.02 6.38 6.37 JPN -0.01 -0.03 -6.25 -6.29 KOR -0.02 -0.04 -10.78 -10.80 USA -0.01 -0.02 -14.00 -13.53 EU 0.00 -0.02 -14.00 -14.24 ROW -0.01 -0.02 -59.50 -59.27 Note: The change in terms of trade (2nd column) and GDP price index (3rd column) are compared to the base scenario fixed at 1 vis-à-vis the value of the post simulation under the FTA scenario. Source: Author. Countries


191

The results show that SAFTA has positive effects on the terms of trade and GDP price indices of India, Sri Lanka and RSA, while it has negative effects on Bangladesh and the non-members. The results with regard to Bangladesh is consistent with the study by Raihan and Razzaque (2007), who find that Bangladesh incurs a net welfare loss because the positive trade creation effect is not large enough to offset the negative trade diversion effect. India receives the largest gains in terms of GDP as well as allocative efficiency followed by RSA. This supports the argument that an FTA is beneficial to member countries, but detrimental to non-member countries. Non-members are at a disadvantage as a result of the trade diversion effect. Sectoral Changes in Trade Balance and Allocative Efficiency Effect Table 6.14 and Figure 6.14 depict the sectoral changes in trade balance in three major sectors of agriculture, manufacturing and services. There are major fluctuations in trade balances with respect to India and RSA as a result of major shuffling in their industrial output patterns (as it was also seen in Table 6.13). TABLE 6.14 SAFTA: SECTORAL CHANGES IN TRADE BALANCE Sector Agriculture Manufacturing Services Source: Author.

Changes in Trade Balance (US$ million) IND LKA RSA CHN JPN KOR

BDG -91.18 -62.05 5.84

-483.19 420.69 -254.15

-57.20 14.34 -54.76

434.07 -442.14 -295.83

15.91 -25.96 24.09

26.34 40.67 84.02

10.46 -36.92 34.08

USA

EU

ROW

5.09 162.77 118.40

36.16 -96.04 283.98

-15.32 -87.65 285.56

FIGURE 6.14 SAFTA: CHANGES IN TRADE BALANCE 500 400

M illion US$

300 200 100 0 -100 -200 -300 -400 -500 BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

Country/Region Agriculture

Manufacturing

Services

Source: Author.

India and Sri Lanka’s trade balance in the manufacturing sector increase, while RSA’s trade balance in the agriculture sector increases. The result is as expected because India and Sri Lanka has much stronger manufacturing base compared to that of RSA, which comprise mainly the LDCs. From this result, one can again understand

192

PART IV GTAP Model

from the comparative advantage pattern, that India and Sri Lanka will do well by focusing on the manufacturing sector, while Bangladesh might rather do well by focusing on the services sector (which is also apparent from its success of micro credit, rural banking and income-generation services). Evidently, RSA will do well by specializing in the agriculture sector. Table 6.15 and Figure 6.15 show the changes in the allocative efficiency effect (commodity summary) in three major sectors of agriculture, manufacturing and services. The allocative efficiencies of both India and Sri Lanka in all the three sectors are positive, while the reverse is true for Bangladesh, except in its services sector, which is still very low. As for the non-members, not much impact can be observed. The result with regard to Bangladesh is again consistent with the earlier result. Clearly, Bangladesh will need to work on not only allocating its resources and factors of production more efficiently, but strive more towards enhancing technology, production efficiency and quality in order to stand at equal footing with its neighbors. TABLE 6.15 SAFTA: ALLOCATIVE EFFICIENCY EFFECT – COMMODITY SUMMARY Sector Agriculture Manufacturing Services Source: Author.

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

-7.02 -105.72 0.26

101.63 57.72 7.07

7.75 10.72 2.77

42.06 27.85 2.80

1.11 4.92 0.36

-0.16 -0.26 -5.79

-4.68 -6.15 0.04

0.12 -13.66 0.02

-0.56 5.41 -14.86

-8.77 -48.49 -1.65

FIGURE 6.15 SAFTA: ALLOCATIVE EFFICIENCY EFFECT 150

Million US$

100 50 0 -50 -100 -150 BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW

Country/Region Agriculture

Manufacturing

Services

Source: Author.

Viable FTAs among SAARC Countries Table 6.16 displays the most viable FTAs among SAARC members. While IND-LKA FTA is the most viable within the framework of fixed as well varying tariff rates, INDRSA FTA is the most flexible of all, since this FTA would be possible in the case of fixed, equal and varying tariff combinations. There is a good prospect for BDG-IND FTA, but only at the varying tariff rates of 30 percent-20 percent. This may be explained by the fact that Bangladesh’s export base is still narrow vis-à-vis India – a


193

situation that is compounded because the country is so strongly dominated by India’s diversified trade pattern in the region. This also implies that further reduction of tariffs from this level would likely undercut the protected industries in Bangladesh, such as textiles and leather by Indian producers with similar line of products. Sri Lanka and RSA could have a successful FTA at varying rates within the range of 0-10 percent. Overall, our hypothesis is further supported by the fact that there are maximum possible combinations available for successful FTAs at varying tariff combinations.17 TABLE 6.16 VIABLE FTAS UNDER SAFTA SCENARIO Tariffs % Combination* Bilateral FTA S/N Contracting Fixed Equal Varying Simulation No. Countries 1 S4c IND-LKA 0-0 2 S5a IND-RSA 10-10 3 S5b IND-RSA 5-5 4 S11a IND-RSA 10-10 5 S11b IND-RSA 5-5 6 S11c IND-RSA 0-0 7 S12a LKA-RSA 10-10 8 S13a BDG-IND 30-20 9 S15a BDG-RSA 20-15 10 S16a IND-LKA 10-15 11 S17a IND-RSA 20-30 12 S17b IND-RSA 5-10 13 S17c IND-RSA 0-5 14 S18b LKA-RSA 10-5 15 S18c LKA-RSA 5-0 Note: (i) BDG=Bangladesh, IND=India, LKA=Sri Lanka, and RSA=Rest of South Asia. (ii) No viable plurilateral FTA exists among SAARC countries. (iii) *Viable FTA refers to FTA scenario that provides welfare gains to both/all the contracting parties at the tariff level as stipulated under the SAFTA Agreement. Fixed: tariffs are fixed for all traded sectors; Equal: tariffs are fixed equally for three highly protected sectors; and Varying: tariffs vary for three protected sectors based on SAFTA tariff reduction schedule and on individual country’s development and trade characteristics. Source: Author.

B. Effects of SAFTA+5 Welfare Gains and Losses The results for SAFTA+5 scenario in the case of plurilateral and bilateral FTAs are as follows:18 (i) Fixed tariffs • Plurilateral (SAARC as Single Entity): The result of experiment S1a shows that except for South Korea and ROW, all others lose. The combination of tariffs at fixed 10 percent for all traded commodities is certainly not a feasible proposition, and therefore,

17 There are many more combinations where one of the contracting parties gains while the other loses. They are not considered because our interest lies in finding the best possible tariff combinations that would most likely be acceptable or feasible to both/all contracting parties. 18 See Appendices A24, A25 and A26 for detailed simulation results.

194

PART IV GTAP Model

it will not be acceptable to the losers. Experiments S1b and S1c show significant gains to +5, but SAARC loses. Hence, these tariff combinations are not feasible as well. • Bilateral (SAARC as Single Entity): In this case, SAFTA-CHN FTA (S1a, S1b and S1c) shows that SAARC loses. Similarly, SAFTA-JPN FTA (S2) and SAFTA-KOR FTA (S3a, S3b and S3c) provide significant welfare gains to Japan and South Korea, but the opposite is true for SAARC. This result is not surprising because both Japan and South Korea are economies that deal in mostly high-tech and value-added products. Conversely, SAARC members in general are agro-based economies dealing in semimanufactured and manufactured products with not much value addition. In the case of SAFTA-USA FTA (S4), both the parties gain. However, in the case of SAFTA-EU FTA (S5), only the EU gains exclusively, while SAARC loses considerably. This is expected because SAARC cannot compete with the EU’s agro-based products, whose agriculture sector is highly subsidized.19 • Bilateral (SAARC as Individual Countries): Bilateral FTA of Bangladesh with China, Japan, South Korea or the EU will not bring gains to Bangladesh, while the reverse is true for its contracting parties. However, Bangladesh experiences significant gains by having an FTA with the United States at both 5 percent and 0 percent fixed tariffs. Similarly, India’s bilateral FTA with +5 affects India’s welfare negatively. In other words, much of the gains from India’s FTA with +5 result in significant gains to the partner country. In particular, FTAs with Japan and the EU results in a major welfare loss to India. However, Sri Lanka gains at 5 percent and 0 percent. A significant welfare gain to Sri Lanka is observed in terms of LKA-USA FTA, while the converse is true for the United States. Except for RSA’s FTA with the EU, RSA gains from entering into FTA pacts with all other nations. The results show that SAARC will reap benefits from the FTAs with +5 on fixed tariff combinations, but only on a case-by-case basis. Therefore, it is in the best interest of SAARC countries to enter into FTA on a selective approach and open only those sectors that ensure positive gains, based on the level of competitiveness of each sector. (ii) Equal tariffs • Plurilateral (SAARC as Single Entity): In these set of experiments, when import tariffs are levied equally for three selected sectors, the welfare for SAARC improves by US$386.7 million (S2b). With complete removal of tariffs by all the contracting parties, the welfare gains for SAARC stands at US$878.4 million (S2c). China, Japan, South Korea and the United States reap the maximum benefits from this plurilateral FTA. The EU gains almost at par with SAARC. • Bilateral (SAARC as Single Entity): In the case of SAARC-CHN FTA (S6a, S6b and S6c), only China and the United States gain, and all others lose. Again, in the case of SAARC-JPN FTA (S7a, S7b and S7c), only Japan gains, accounting for US$169 million, while all others lose, including SAARC to the tune of US$40 to US$63 million. 19 It may be noted that the difference in welfare losses to the United States and the EU as a result of removal of its subsidies is rather insignificant.


195

An interesting outcome is that the FTA between SAARC and the United States (S9a) generates welfare gains of nearly US$875 million to SAARC, while generating a welfare loss of US$98 million to the United States, although there is a maximum tariff cut by SAARC. ROW and all other nations also become worse off. When the tariff level is lowered down to 0 percent (S9b), it results in a massive gain of US$1.96 billion to SAARC, while causing a welfare loss of US$632 million to the United States. SAARCEU FTA (S10a, S10b and S10c) results in welfare gains to only the United States and the EU; the EU gains considerably by US$1.08 billion, while SAARC loses by nearly 326.8 million. In this scenario, the FTA between these two blocs cannot materialize unless the EU compensates by allowing for preferential tariffs to SAARC. Hence, the subsequent section attempts to reapportion the distortions so as to come up with optimal tariff combinations for all contracting parties to benefit from FTAs. • Bilateral (SAARC as Individual Countries): In the case of BDG-CHN FTA (S21a), Bangladesh gains at equal tariffs of 10 percent. While a considerable welfare gain for Bangladesh is observed with the BDG-USA FTA at 5 and 0 percent, the United States is a big loser (S24a and S24b). Besides these two scenarios, Bangladesh loses in all other FTAs. India’s bilateral FTA with China results in welfare losses of US$7.3, US$14.68 and US$26.79 million with 10 percent, 5 percent and 0 percent tariffs for top three protected sectors, respectively (S26a, S26b and S26c). Similarly, with respect to INDJPN FTA, India’s welfare losses account for an even higher amount of US$17.92, 36.99 and 65.24 million at the same rates (S27a, S27b and S27c). However, India gains by having FTAs with South Korea (S28a, S28b and S28c), the United States (S29a and S29b) and the EU (S30a, S30b and S30c). In the case of South Korea and the United States, both of them face welfare losses. The EU observes much higher welfare gains vis-à-vis India accounting for US$130.79, US$136.48, US$113.47 million at 10 percent, 5 percent and 0 percent tariffs. An interesting point is that Sri Lanka benefits from FTAs with all +5 countries. On the other hand, it is also fascinating to see that the United States and the EU do not gain while all others do. RSA gains by having FTAs with Japan (S37a and S37b), South Korea (S38a, S38b and S38c) and the United States (S39a and S39b). However, except for Japan, South Korea and the United States lose in the deal. In the case of RSA-EU FTA, EU gains significantly (S40a and S40b). The above results suggest the existence of comparative advantage of SAARC countries over some of its partners, while the reverse is true for some others, such as China and the EU. Careful observation evinces that SAARC members face welfare losses through FTAs with China and the EU particularly because the sectors they deal with are homogenous and competitive, while the opposite is true with Japan, South Korea and the United States, wherein the sectors are much differentiated.

196

PART IV GTAP Model

(iii) Varying tariffs • Plurilateral (SAARC as Single Entity): It is captivating to see in the last three experiments (S3a, S3b and S3c)20 wherein the tariff protections are levied at varying rates (30 percent-20 percent, 10 percent-5 percent and 5 percent-0 percent) in addition to removal of agricultural subsidies by the United States and the EU, the welfare gains for SAARC countries increase remarkably, especially in the last two experiments. In the final experiment (S3c), where the tariffs are lowered to 5 percent by SAARC and 0 percent by +5, the total gains for SAARC accounts for nearly US$1.2 billion. At the same time China, Japan, South Korea, and the United States also gains significantly accounting for about US$7.6 billion, US$1.8 billion, US$ 6.1 billion, and US$1.8 billion, respectively. However, the welfare gains for the EU dwindle down to some extent accounting for about US$96 million (see Table 6.17). ROW is a loser throughout. Clearly, there is an indication of large trade diversion from ROW. All things considered, SAARC should have no compunction to opt for an FTA with +5. Experiments S2b, S2c, S3b and S3c clearly indicate that the welfares of SAARC as well as all the contracting members improve considerably. In other words, the highest gain comes to the SAARC bloc if the tariffs by SAARC and +5 are lowered down to 5 percent and 0 percent, respectively. This is an interesting result because the plurilateral FTA with the maximum liberalization is the most rewarding of all FTAs to the entire bloc, inclusive of both SAARC and +5 countries. TABLE 6.17 SAFTA+5: INDIVIDUAL WELFARE GAINS AND LOSSES Region/Countries SAARC China Japan South Korea USA EU ROW Source: Author.

Welfare (US$ million) 1,203.20 7,571.00 1,754.00 6,200.00 6,130.70 95.80 -6,951.00

Figure 6.16 depicts the results of the plurilateral SAFTA+5 effects as discussed above. While SAARC gains US$1.2 billion, which is almost the double that of the bilateral effects, +5 receives the largest share of welfare gains under this scenario, amounting to 21.8 billion. This is not surprising considering the fact that +5 is dominated by some of the world’s largest and strongest economies. This is an interesting finding because the plurilateral FTA with maximum liberalization is the most rewarding of all FTAs. The results also suggest that those countries lacking comparative advantage in terms of resource endowments, technology and the like, will be worse off in a free trade. In order for all countries to enjoy the benefits of free trade in a plurilateral FTA scenario, any country that loses will need to be compensated by 20 SAARC is compensated by means of tariff rate concessions as well as removal of agricultural subsidies by the United States and the EU.


197

winners. Therefore, the results once again provide support to our first hypothesis that compensating the losers by winners by way of tariff concessions results in welfare gains for all concerned. FIGURE 6.16 SAFTA+5: WELFARE GAINS AND LOSSES (PLURILATERAL – SAARC AS SINGLE ENTITY)

1,203.20

Region

SAARC

21,751.50

+5

ROW

-6,951.00

-10,000

-5,000

0

5,000

10,000

15,000

20,000

25,000

Million US$

Source: Author.

• Bilateral (SAARC as Single Entity): SAFTA-CHN FTA (S11a, S11b and S11c) gives clear evidence that lowering the tariffs below 30 percent does not generate gains either to SAARC or to the rest of the countries, with the exception of China. An interesting result is the SAFTA-JPN FTA (S12a, S12b and S12c), which brings about welfare losses to SAARC. As observed earlier, this result is not at odds. Japan is highly competitive, highly efficient, and a major producer of high-tech and value-added products. SAARC is composed of countries that belong to LDCs (Afghanistan, Bangladesh, Bhutan, Maldives and Nepal) and non-LDCs (India, Pakistan and Sri Lanka) with varying degrees of development. Naturally, with deeper tariff liberalization, Japan with its highly developed export market will easily circumvent the less competitive firms in SAARC’s domestic market. An anomalous result emerges from the FTA between SAARC and South Korea (S13a, S13b and S13c). SAARC is a winner under all tariff combinations, but South Korea becomes worse off under all tariff combinations – even when SAARC levies a zero percent. In addition, all others become worse off. With regard to SAFTA-USA FTA, SAARC becomes considerably better off under all conditions, while all the rest becomes worse off. Even when the United States is exempted from removal of export subsidies, SAARC still gains. This is quite obvious because the United States is and has been the major importer of goods and services from the developing countries of SAARC. For example, India alone is the 18th largest exporter to the United States. Indeed, the United States’ imports from India have been increasing far more rapidly starting in the early 1990s (see Martin and Kronstadt, 2007). All of the above results plainly suggest that those countries lacking comparative advantage in terms of resource endowments, technology and the like, will be worse off

198

PART IV GTAP Model

in a free trade. To enjoy the benefits of free trade by all countries in an FTA scenario, any country that loses will need to be compensated by winners by extending tariff concessions. These results firmly support our hypothesis that compensating the losers by winners by means of tariff concessions leads to welfare gains for all concerned. In other words, such an action translates into a win-win situation for all country groups involved. Figure 6.17 provides the results of the bilateral SAFTA+5 effects when SAARC behaves as a single entity. SAARC still gains to the sum of US$696.75 million; however, the gains are reduced to a large extent. Gains for +5 are not so significant because of the trade diversion of SAARC members from +5 into the intra-regional bloc. ROW loses, but not as badly as in the case of plurilateral SAFTA+5. FIGURE 6.17 SAFTA+5: WELFARE GAINS AND LOSSES (BILATERAL – SAARC AS SINGLE ENTITY)

696.75

Region

SAARC

178.75

+5

ROW

-2000

-1,518.41

-1500

-1000

-500

0

500

1000

Million US$

Source: Author.

• Bilateral (SAARC as Individual Countries): Varying tariff combinations facilitate both Bangladesh and China to gain from trade (S41a) at 30 percent-20 percent tariff combination. Similarly, with Japan (S42a) and South Korea (S43a) as well, Bangladesh sees some welfare gains. In the case of BDG-USA FTA, there are definite welfare gains for Bangladesh under all combinations, accounting for nearly US$299 million (S44a) and US$598 million (S44b), while the United States loses by about US$124 million and US$308 million. These results clearly suggest that BDG-USA FTA will be welfare improving to Bangladesh. With regard to BDG-EU FTA (S45a and S45b), only the EU gains, while Bangladesh along with all others lose. As regards India’s FTA, unless India maintains a tariff rate of at least 20 percent against China (S46a) and Japan (S47a), it will not benefit by having FTAs with both China and Japan. In contrast, India gains exclusively by having FTAs with South Korea (SS48a, S48b and S48c), and also the United States (S49a and S49b), even at a tariff level as low as 5 percent and without removal of agricultural subsidies. With IND-EU FTA, both India and the EU gains at 20 percent-5 percent combination (S50a), but the


199

EU will lose at 10 percent-0 percent combination (S50b). However, at 5 percent-0 percent, both India and the EU will find marked improvement in their welfares (S50c). China also reaps some benefits out of this FTA. Sri Lanka’s association with China brings welfare gains to both Sri Lanka and China; even Bangladesh and India enjoys the spillover benefits, while all others lose (S51a, S51b and S51c). Interestingly, while LKA-JPN FTA is feasible for Sri Lanka but not for Japan (S52a and S52b). Both Sri Lanka and South Korea gains (S53a and S53b), however, with regard to FTA with the United States (S54a and S54b) and the EU (S55a and S55b), only Sri Lanka gains significantly. RSA gains significantly by having FTAs with Japan (S57a and S57b), South Korea (S58a and S58b) and the United States (S59a, S59b and S59c). But unlike Japan, both South Korea and the United States lose. In the case of FTA with the EU, RSA can improve its welfare only if it maintains a 20 percent-0 percent tariff combination; lowering the tariff anything below 20 percent results in welfare loss (S60a, S60b and S60c). Hence, RSA-EU FTA calls for a cautionary approach to integration. The results in this section firmly support our first hypothesis that although some of the countries may benefit from FTA, it can hurt others; but at the same time, it can also bring welfare to all the contracting parties by compensating the losers through tariff rates adjustment. Additionally, SAARC can have a greater number of FTAs with selective combination of varying tariffs as opposed to fixed or equal tariff combinations. FIGURE 6.18 SAFTA+5: WELFARE GAINS AND LOSSES (BILATERAL – SAARC AS INDIVIDUAL COUNTRIES) BDG

Country/Region

IND LKA RSA CHN JPN KOR USA EU ROW -800

-600

-400

-200

0

200

400

600

Million US$ ROW

EU

Welfare -752.50 319.58

USA

KOR

JPN

CHN

RSA

LKA

IND

BDG

-85.01

-87.39

510.35

306.24

241.20

50.17

94.81

18.55

Source: Author.

Figure 6.18 shows the results of the bilateral SAFTA+5 effects when SAARC countries have individual FTA with +5. In this case, the total gains for SAARC countries reduce to US$404.73 million, while the welfare of +5 improves quite substantially with the exception of South Korea and the United States. This sends a clear signal for why +5 in general will benefit by integrating with the South Asian countries. ROW is still a loser for the same reason as stated earlier.

200

PART IV GTAP Model

Trade Creation and Trade Diversion Effects Table 6.18 shows the changes in export sales in the 20 sectors of SAARC, China, Japan, South Korea, the United States, the EU, and ROW under the plurilateral SAFTA+5. In each cell, positive value denotes increased volume of exports, while the negative value denotes decreased volume of exports from the country designated in the row to the country designated in the column. TABLE 6.18 EXPORT SALES IN 20 SECTORS UNDER PLURILATERAL SAFTA+5 SAARC

CHN

JPN

KOR

USA

EU

ROW

SAARC Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

-11.91 -0.04 -4.98 -0.05 -0.01 0.48 -3.55 -74.05 14.05 -2.60 -2.55 -44.15 -28.11 0.11 0.85 -0.07 -0.12 -0.20 0.00 -0.51

1.35 -0.10 11.00 -0.05 -0.02 -4.81 4.41 -68.49 84.51 111.63 -35.28 -29.32 -39.55 -13.77 -31.19 -0.06 -2.32 -1.07 -0.01 -1.76

3.02 0.16 11.34 0.00 0.00 0.07 0.13 -47.32 13.13 -0.09 22.83 779.51 -28.29 8.17 68.40 0.01 8.99 7.64 0.17 6.61

38.67 2.11 38.42 0.00 0.01 -0.16 3.41 -101.10 33.36 -1.02 -14.05 153.87 -54.28 -18.47 -69.26 -0.02 1.89 -5.23 -0.03 -8.22

-23.32 -0.45 -1.34 -0.07 0.00 -0.02 6.27 -219.86 8.73 -1.15 -14.95 31.35 880.26 -10.52 -34.87 -0.17 -1.10 -9.12 0.01 -24.72

-57.41 0.58 -55.65 -0.14 0.04 0.04 93.50 4703.97 26.22 -22.19 13.26 415.18 -150.26 10.10 41.57 -0.03 9.70 9.02 0.36 13.76

272.16 16.72 26.12 2.48 0.08 -3.17 -19.53 -920.66 150.89 -21.48 165.55 -133.00 -254.04 64.58 86.48 0.77 30.30 28.79 0.66 47.14

Sum

-157.41

-14.90

854.48

-0.10

584.96

5051.62

-459.16


-96.12 -0.02 19.77 0.01 -0.01 2.43 1.50 6.01 -151.90 0.12 0.99 -0.26 -0.70 -0.29 0.73 0.00 -3.39 0.39 0.02 0.08

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

108.21 13.58 6.42 0.01 0.28 1.02 5.09 40.75 -1056.11 17.94 279.40 2254.97 175.18 387.32 775.76 0.01 31.99 2.58 9.20 3.96

69.12 32.97 62.66 0.01 0.63 -0.44 24.00 -72.84 8899.81 190.09 -165.98 449.71 -209.13 -550.79 -328.36 -0.06 6.88 -3.58 -0.14 -5.24

11890.86 -26.35 -12.20 -0.15 -0.09 -0.08 15.77 -19.57 -118.14 15.03 -32.98 109.36 -39.71 -245.91 -130.10 -0.10 -4.76 -1.82 0.67 -2.52

144.21 4.17 9.24 0.88 0.31 0.05 150.26 19.69 -253.09 7.68 49.14 2234.44 3.10 -93.31 228.94 0.78 21.40 29.34 15.66 55.75

-3137.40 88.16 66.80 38.42 0.82 0.30 -39.41 177.99 -1497.65 50.85 425.06 -426.02 195.94 109.28 474.45 3.00 390.58 65.05 6.88 69.95

CHN


201

Sum

-220.64

0.00

3057.56

8399.32

11397.21

2628.64

-2936.95


-229.15 -4.18 -4.24 -0.03 -3.09 1.82 1.52 7.59 -33.50 -10.86 -5.16 -0.09 -0.79 -0.10 -1.04 0.00 -9.75 -6.47 -0.29 -11.73

2011.45 -26.11 -505.65 -2.95 -15.11 -14.53 41.40 -266.71 -70.96 -368.04 -155.89 46.49 -90.73 -387.92 -398.67 -0.21 -33.79 -13.98 -5.51 -14.02

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

917.62 5.27 127.51 0.01 6.07 -1.03 89.16 -54.43 -39.13 364.94 -115.86 -16.75 -147.96 -403.03 -211.17 -0.19 5.51 -30.96 -0.99 -41.43

10818.33 -46.35 7707.54 -14.37 -4.36 2.21 -168.21 -152.28 -14.18 242.72 -206.03 -58.33 -40.43 -340.62 -485.26 -1.26 -35.21 -109.49 -12.15 -242.66

205.35 -14.30 6656.36 -0.48 -8.81 0.51 374.00 -48.36 -38.30 -184.29 -65.25 -63.21 -6.02 -6.81 1.75 -1.69 -102.18 -112.57 -36.71 -159.00

-5735.41 -23.40 -2972.70 1.69 -35.84 228.97 -38.90 82.86 -24.75 -59.05 108.95 2.45 187.85 430.86 253.32 0.17 -6.14 48.49 1.47 78.96

Sum

-309.54

-271.44

0.00

453.16

16839.61

6389.99

-7470.15


-117.41 0.31 11.56 -0.04 0.01 1.07 -0.06 1.29 115.64 8.42 1.38 1.04 -2.05 -0.09 -0.07 0.00 -0.91 1.30 0.02 2.00

4458.15 11.24 15.14 -0.67 -26.69 -9.81 13.15 -6.75 795.30 31.23 -24.98 77.83 -56.74 -138.12 -69.10 0.01 -3.47 -0.94 0.01 -0.73

-143.99 3.20 -8.27 0.00 20.28 0.31 33.19 30.54 162.27 9.05 258.21 75.64 77.13 203.51 378.65 0.02 1.65 24.00 0.16 15.97

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

4577.18 86.85 667.68 -3.12 -1.13 0.84 38.54 -0.64 56.70 24.97 -4.95 14.20 -38.57 -272.42 -204.56 0.98 -8.84 4.88 0.12 20.81

-255.05 11.50 159.08 -0.23 -1.59 0.25 50.69 17.15 216.53 46.82 55.86 41.54 -19.76 -19.89 21.57 1.53 -13.44 86.94 1.38 142.94

-2632.65 68.37 -351.94 -10.32 -4.41 139.82 -11.58 89.40 257.83 25.80 168.73 24.82 35.94 64.02 74.96 4.62 3.01 109.41 1.01 134.63

Sum

23.41

5064.06

1141.52

0.00

4959.52

543.82

-1808.53

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures

-20.31 0.15 -2.51 0.00 0.17 -0.03 -0.02 134.32

5.35 6.47 -3.01 -0.26 0.31 -7.49 1.72 -704.25

124.50 3.24 2.69 0.02 1.09 0.10 2.57 150.16

508.06 2.03 61.52 0.02 0.37 -0.08 11.63 -97.27

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

85.67 20.89 1777.90 -0.17 5.54 -10.77 21.60 159.30

1285.49 215.21 -312.35 4.02 53.59 -405.19 45.49 1881.75

JPN

KOR

USA

202

PART IV GTAP Model


6147.38 -29.24 180.28 -0.35 -3.11 -0.73 -1.69 -0.07 -1.37 -3.19 -0.06 -15.21

3976.17 6024.00 1665.35 108.32 -194.68 -834.70 -832.85 -0.54 -6.71 -45.98 -0.21 -26.76

283.18 3.17 219.83 1137.41 146.24 1069.13 1600.34 0.06 5.63 37.75 1.32 64.25

3184.06 76.15 -89.94 -474.79 -166.75 -1019.90 -454.77 -0.47 8.67 -71.93 -0.17 -116.83

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3360.72 -289.63 -336.77 -14.78 32.09 21.46 293.94 0.75 17.93 193.38 2.42 299.70

-9040.42 -2683.21 454.13 958.28 956.00 1947.68 2934.98 32.79 257.62 612.91 6.06 655.62

Sum

6384.41

9130.25

4852.68

1359.61

0.00

5641.17

-139.55


825.52 -1.83 123.10 0.16 -0.26 0.92 5.77 63.04 -1077.31 -43.06 -19.18 -1.77 -6.45 -1.05 -6.11 -0.68 -28.89 -17.57 -0.55 -56.24

2441.09 -12.72 157.88 -0.66 -2.48 -7.59 1.39 -406.57 7542.86 -176.73 -224.33 41.03 -148.47 -537.31 -600.13 -2.45 -109.88 -67.84 -12.93 -77.00

86.01 3.60 46.61 0.12 0.40 0.10 4.19 138.12 -25.47 4.13 204.76 222.49 65.28 749.15 1019.30 0.12 134.31 101.63 48.59 161.74

754.43 5.26 29.88 0.06 0.36 -0.06 15.59 -35.53 -125.42 28.27 -52.96 1916.98 -73.13 -563.18 -663.72 -1.19 43.11 -155.45 -2.79 -294.22

2347.51 -29.61 2120.57 -1.37 -1.29 -2.75 250.45 -248.76 -143.13 41.63 -617.39 -185.55 -204.29 -751.58 -1732.54 -10.64 -141.05 -455.04 -24.16 -1231.05

-2983.40 41.56 -1295.02 -2.81 -16.69 -8.19 -140.93 207.95 -4570.20 -87.94 -286.25 -1464.20 -209.71 208.16 423.00 -31.66 -86.63 -13.34 -21.33 81.69

-768.07 86.72 6.58 30.07 7.21 -88.44 2.50 818.43 -1758.50 153.01 757.20 53.48 792.21 881.46 1377.98 86.51 344.92 771.38 26.77 1234.95

Sum

-242.44

7797.16

2965.18

826.29

-1020.04

-10255.94

4816.37

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport

-252.29 -3.29 -29.52 -0.02 -0.83 3.65 -0.55 58.68 -438.67 -23.73 -56.60 -1.28 -17.73 -0.01 -8.85 -1.12 -16.30 -20.95

-149.78 -27.26 -44.25 -0.59 -4.48 -16.34 4.48 -431.96 -145.73 -601.76 -471.94 130.70 -275.90 -733.37 -879.67 -15.30 -59.70 -56.60

196.27 5.63 15.65 0.08 3.07 1.16 10.57 96.16 17.64 25.74 400.52 562.36 403.78 1792.65 2211.86 0.11 48.06 56.57

1046.65 30.50 66.27 0.04 0.91 -1.43 24.57 -124.40 -245.44 108.32 -278.24 -341.88 -427.56 -1273.92 -1374.41 -0.78 18.13 -111.16

-1937.48 -147.95 -439.12 -8.82 -8.76 7.58 -35.07 -779.84 -329.56 131.14 -1645.96 -985.07 -634.51 -1274.20 -3435.88 -30.15 -134.87 -399.01

-387.75 -26.67 -221.09 -1.65 -5.69 11.58 -45.65 -234.39 -393.88 -89.70 -412.20 -139.57 -75.68 383.40 246.85 -46.80 -169.44 -273.41

112.65 13.72 158.75 8.71 -1.88 274.12 4.43 472.31 -270.54 41.93 799.75 167.07 1056.51 2387.45 1686.27 42.49 96.51 258.24

EU

ROW

CHAPTER 6 Welfare Effects of SAFTA and SAFTA+5 Construction Services Sum

203

-0.71 -62.29

-7.27 -72.37

9.70 82.17

-1.95 -198.74

-19.46 -1194.42

-45.67 -531.78

2.42 440.86

-872.41

-3859.09

5939.75

-3084.52

-13301.41

-2459.19

7751.77

Note: The value in each cell represents changes in export sales of 20 sectors from the country/region in the row to the country/region in the column. The unit is US$ million. Source: Author’s simulation using GTAP 6 database.

As a case in point, if we take look at China’s import changes, on one hand, China increases its import of crops by US$1.35 million from SAARC, US$2,011.45 million from Japan, US$4,458.15 from South Korea, US$5.35 million from the United States, and US$ 2,441.09 million from the EU under the plurilateral SAFTA+5 scenario. The sum of these increases is US$8,917.39 million. On the other hand, China decreases its import of crops from ROW by US$149.78 million. Thus, the trade creation effect is equal to US$ 8,767.61 million (US$8,917.39–US$149.78). Likewise, Japan increases its trade volume with SAARC by US$854.48 million, with China by US$3,057.56 million, and with South Korea by US$1,141.52 million. At the same time, Japan increases its imports from the United States, the EU and ROW accounting for US$4,852.68 million, US$2,965.18 million and US$5,939.75 million, respectively. This indicates that there is significant trade creation for Japan under the SAFTA+5 scenario. The most interesting example of trade creation is the case of the SAARC bloc itself. The total trade volume of SAARC under this scenario goes up by US$23.41 million from South Korea, and US$6,384.41 million from the United States, while it decreases its trade from within SAARC by US$157.41 million, from China by US$220.64 million, from Japan by US$309.54 million, from the EU by US$242.44 million, and from ROW by US$872.41 million. The net trade creation is US$4,605.38 million (US$6,407.82–US$1,802.44). Taking another example, the EU’s trade volume increases by US$20,255.24 million, while it decreases by US$12,715.13 million. The net trade creation of the EU alone under the SAFTA+5 scenario is US$7,540.11 million. Therefore, the overall trade creation effect is much higher if all the countries are taken into account, evidently supporting our second hypothesis. Needless to say, the SAFTA+5 scenario has a much smaller trade diversion effect. At the sectoral level, Japan increases imports of crops from SAARC, China, the United States, the EU and ROW, but decreases its imports from South Korea. The United States as well increases its import of metals from SAARC by US$880.26, while it decreases the imports of metals from China, Japan, South Korea, the EU and ROW considerably. Hence, there is a substantial change in the inter-industry trade pattern based on resource endowments and comparative advantage. In effect, SAFTA+5 create substantial gains for all concerned by way of shifting the inter-industry trade structure and resource allocation. Although SAARC’s share of trade may seem miniscule in the midst of interplay with giant economies, the plurilateral SAFTA+5 effects doubles the welfare of SAARC as opposed to the SAFTA effects. Moreover, it is welfare improving to all members showing that the positive effects of trade creation is larger than the

204

PART IV GTAP Model

negative effects of trade diversion. Therefore, these results evidently corroborate our second hypothesis. Changes in Industry Output, Private Household Demand, Aggregate Exports and Imports The SAFTA+5 scenario has some major effects on the output of industry, household demand, and exports and imports of all countries. Table 6.19 illustrates these effects. The industry output of SAARC is quite the reverse of what was seen in the SAFTA scenario. There is evidently a swapping of the comparative advantage pattern based on trade complementarities and resource endowments: SAARC countries are forced to pull back to the agriculture sector, while the manufacturing and service sectors are dominated by Japan and South Korea that have a greater advantage over these sectors. SAARC and China specialize in similar industries mostly comprising agro-based and manufacturing sectors, clearly signaling their midway development phases. The United States and the EU are still the major producers of agriculture products, attributable to their well developed system of agricultural subsidies. SAARC, China and South Korea become the major exporters of textile goods, while Japan and the EU specialize in machinery and manufactures, respectively. The results are as anticipated because these are some of the most likely changes to place under the SAFTA+5 scenario. Regarding the private household demand, there is a major increase in all the regions. SAARC’s household demand for machinery goods increases sharply by US$1,147.79 million. Interestingly, Japan’s agricultural imports expand by US$14,313 million. This shows that there is a major shuffling of demand for products among the regions. This may also mean that, as each region specializes in specific products, the resource allocation efficiency improves for all countries, raising not only the demand but also the overall production of those specialized products. This trade pattern largely supports the Heckscher-Ohlin Theory21 that the international trade is largely driven by differences in country’s resources. Aggregate exports for all countries increase significantly in agriculture and manufacturing sectors, except for the United States in the manufacturing sector. SAARC, China, South Korea and the United States experience a drop in exports of services. However, aggregate exports in the case of Japan increase in all the three major sectors. Imports also increase in all the countries, but Japan increases its net agriculture imports significantly and decreases its imports in the manufacturing and service sectors. This sends a clear signal as to why Japan is so reluctant on opening its agriculture sector. As expected, ROW’s imports decrease in all major sectors.

21 The theory emphasizes the interplay between the proportions in which different factors of production are available in different countries and the proportions in which they are used in producing different goods (see Krugman and Obstfeld, 2003. pp. 67-86 for further details).


205

TABLE 6.19 SAFTA+5: CHANGES IN INDUSTRY OUTPUT, PRIVATE HOUSEHOLD DEMAND, AGGREGATE EXPORTS AND AGGREGATE IMPORTS Changes in Industry Output of Commodity (US$ million) qo

SAARC

CHN

JPN

KOR

USA

EU

ROW

Crops Livestock Dairy Forestry Fishing Mining Agriculture Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Manufacturing Utility Trade Transport Construction Oth. Services Services

559.98 -7.32 15.89 -179.01 -13.61 -47.01 328.92 -113.61 -4236.99 6179.10 -186.21 270.71 -1869.97 -1461.82 88.43 527.21 -803.15 -390.31 139.00 241.36 892.56 -331.88 550.73

-867.22 1554.52 388.17 -94.63 58.98 -518.93 520.89 256.00 -2654.00 12550.28 6410.36 -376.31 -8171.33 -4082.06 -4050.23 -5038.94 -5156.23 -208.48 212.09 -303.91 2225.06 517.16 2441.92

-12747.78 -2702.64 -14371.52 0.28 -380.59 78.35 -30123.90 55.77 206.39 -529.92 275.15 2970.88 9145.44 4141.72 7566.34 8467.56 32299.33 234.28 -86.25 125.09 -1093.06 -286.25 -1106.19

-8852.34 508.44 517.26 -45.09 -126.60 -55.79 -8054.12 344.24 -626.12 15039.24 1001.46 719.94 2594.05 -2784.56 -4257.35 -5420.11 6610.79 604.98 1207.38 -212.41 1279.92 271.30 3151.17

32009.28 2020.56 12030.97 -67.44 21.92 -393.08 45622.21 138.86 -1856.13 -13265.56 -2165.50 -6285.75 -2484.63 -2328.13 -5562.78 -10490.75 -44300.37 -239.72 1064.00 -496.63 1618.50 -2811.00 -864.85

-6448.19 1066.61 6991.69 109.34 -48.15 -37.22 1634.08 463.91 6690.69 -5405.11 -781.74 -1553.25 486.63 -801.69 486.50 1096.75 682.69 -151.09 -510.50 1856.75 -959.75 -1023.50 -788.09

-13004.63 -832.53 -3996.59 207.51 -150.39 1346.25 -16430.38 -134.55 3924.13 -16237.13 -2861.27 4606.13 1032.63 6190.63 7330.25 9798.03 13648.85 885.47 381.38 3913.88 -3583.69 2640.00 4237.04

Changes in Private Household Demand for Imports (US$ million) qpm Crops Livestock Dairy Forestry Fishing Mining Agriculture Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Manufacturing Utility Trade Transport Construction Services Services

SAARC 172.69 5.17 57.63 -0.52 0.04 -0.24 234.77 142.18 733.21 36.25 64.92 5.64 157.52 12.63 -2.62 -1.94 1147.79 0.14 10.2 0.21 0 2.02 12.57

qxw

SAARC

CHN 2523.54 24.58 34.04 1.79 1.6 0.27 2585.82 143.74 33.95 950.41 56.13 41.1 61.71 6.29 82.73 52.31 1428.37 0.83 272.52 32.71 0 24.06 330.12

JPN 4666.5 -1.9 9664.18 -1.09 -12.76 -1.93 14313.00 281.84 -143.34 -143.97 -78.87 -123.55 -55.09 -31.98 -174.27 -120.71 -589.94 -1.62 -167.65 -121.39 0 -70.98 -361.64

KOR 8799.55 -45.33 204.95 0.17 1.34 13.93 8974.61 117.01 69.85 52.77 -15.75 51.97 14.94 14.97 112.01 28.75 446.52 7.12 1.93 96.59 0 98.66 204.30

USA 777.43 5.96 700.98 0.97 3.99 0.11 1489.44 61.2 579.57 7371.95 3542.25 602.46 625.44 27.59 267.11 558.39 13635.96 13.84 191.82 215.79 0 301.91 723.36

EU 1724.48 2.54 849.92 2.46 4.68 1.72 2585.80 117.09 38.53 1964.7 25.06 18.65 293.02 6.14 -13 -14.45 2435.74 14.89 49.05 26.66 0.12 -30.55 60.17

ROW -492.12 -21.29 -329.19 -1.07 -5.19 -1.42 -850.28 -34.83 -416.41 -207.29 -183.37 -353.04 -222.05 -62.7 -298.94 -275.85 -2054.48 -32.41 -91.85 -272.81 -1.08 -499.7 -897.85

EU

ROW

Changes in Aggregate Exports, FOB weights (US$ million) CHN

JPN

KOR

USA

206

PART IV GTAP Model


165.87 -7.57 116.24 0.90 -3.40 16.04 288.08 4.88 446.18 5112.21 -54.16 189.54 -42.10 2.07 16.66 75.66 5750.94 -1.02 -39.13 41.67 -0.87 -55.80 -55.15

8767.56 -48.93 -363.87 -5.18 -48.48 -60.68 8240.42 67.10 -1897.18 15818.51 5063.87 755.31 372.13 -813.89 -2629.43 -2792.58 13943.84 -18.53 -215.30 -141.16 -25.73 -191.75 -592.47

372.64 29.36 78.87 0.23 25.12 2.70 508.92 55.74 397.32 -614.76 59.48 1375.10 5174.93 811.96 4201.95 6038.11 17499.83 0.32 229.12 842.11 69.42 332.91 1473.88

3307.44 78.04 400.26 0.15 8.33 -3.21 3791.01 169.71 -500.77 11692.74 764.97 -719.03 1724.05 -1090.25 -3825.44 -3100.18 5115.80 -2.71 84.12 -344.38 -6.05 -664.28 -933.30

27649.34 -164.89 10005.61 -28.09 -15.65 5.80 37452.12 108.37 -1468.68 -554.48 451.82 -2542.79 -1066.16 319.49 -2904.11 -6048.36 -13704.90 -41.62 -328.08 -704.07 -54.93 -2690.84 -3819.54

-3205.80 36.03 6047.96 -5.80 -27.49 -9.34 2835.56 503.80 5919.56 -1756.42 -659.27 -1123.72 1058.91 -572.55 399.11 984.75 4754.17 -82.33 -352.52 1791.55 -87.87 -213.50 1055.33

-10622.48 467.07 -3387.36 75.52 20.07 150.31 -13296.87 -56.12 2486.08 -12337.95 -2506.56 2968.00 665.45 2904.03 5997.25 7031.06 7151.24 175.34 1132.01 3463.34 47.02 2707.42 7525.13

Changes in Aggregate Imports, market price weights (US$ million) qim Crops Livestock Dairy Forestry Fishing Mining Agriculture Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Manufacturing Utility Trade Transport Construction Services Services

SAARC 356.74 21.45 70.67 -2.92 0.05 3.2 449.19 156.83 5102.72 414.16 104.73 124.28 2000.25 460.68 27.78 7.21 8398.64 -0.25 35.82 18.31 1.03 -0.67 54.24

CHN 13289.31 120.57 173.28 43.01 2.58 4.51 13633.26 196.97 175.18 6336.98 325.88 682.35 6516.33 135.5 -449.36 1166.29 15086.12 3.63 443.12 91.6 32.01 121.03 691.39

JPN 10372.55 -120.75 15757.7 -17.52 -83.17 238.55 26147.36 342.86 -459.57 -246.35 -77.92 -470.57 -91.96 -109.21 -724.06 -867.97 -2704.75 -3.17 -180.37 -224.12 -54.67 -389.69 -852.02

KOR 16450.46 198.42 591.45 -16.18 -26.8 148.46 17345.81 161.66 148.19 1873.22 164.33 513.97 267.34 -5.86 -167.4 223.16 3178.61 7.16 -21.97 225.73 2.69 315.52 529.13

USA 2148.32 273.83 1328.94 3.79 73.59 -445.34 3383.13 87.3 1617.85 10543.89 3742.19 2322.31 1765.7 823.24 1203.27 3610.56 25716.31 32.91 283.71 727.28 9.36 866 1919.26

EU 3142.38 99.63 1475.84 34.52 -11.93 -131.05 4609.39 163.14 709.47 2513.66 -83.04 -215.22 726.86 227.41 -21.09 -198.06 3823.13 42.79 168.59 182.33 15.03 -140.09 268.65

ROW -1775.59 -177.78 -576.95 -2.45 -21.32 314.39 -2239.70 -50.54 -1086.22 -2140.51 -479.01 -1816.94 -621.86 42.64 1324.84 -1704.44 -6532.04 -53.96 -223.19 -557.91 -64.99 -1566.58 -2466.63

Source: Author’s simulation using GTAP 6 database.

Regional Changes in Terms of Trade, GDP Indices, and Allocative Efficiency Table 6.20 shows the regional changes in terms of trade, GDP indices, and allocative efficiency. The results establish that SAFTA+5 has positive impacts on the terms of


207

trade of all countries, except for Japan and ROW. There is a mixed effect on change in GDP price indices. However, the GDP quantity indices as well as the allocative efficiencies of SAARC, China, Japan, and South Korea increase significantly, while there is a decrease in the case of the United States, the EU and ROW. TABLE 6.20 SAFTA+5: REGIONAL CHANGES IN TERMS OF TRADE, GDP INDICES, AND ALLOCATIVE EFFICIENCY Change in GDP Allocative Quantity Index Efficiency Change in Terms Change GDP of Trade (%) Price Index (%) (US$ million) (Regional EV) SAARC 0.15 -0.19 793.94 794.10 CHN 0.44 0.30 6,516.75 6,519.40 JPN -0.48 -1.00 4,046.25 4,044.53 KOR 0.55 -1.61 5,516.31 5,497.42 USA 0.57 0.32 -621.00 -621.55 EU 0.02 -0.13 -85.50 -85.43 ROW -0.30 -0.58 -239.00 -238.87 nd rd Note: The change in terms of trade (2 column) and GDP price index (3 column) are compared to the base scenario fixed at 1 vis-à-vis the value of the post simulation under the FTA scenario. Source: Author. Countries

Sectoral Changes in Trade Balance and Allocative Efficiency Effect Table 6.21 and Figure 6.19 show the sectoral changes in trade balance in the three major sectors of agriculture, manufacturing and services. The trade balance of SAARC is negative for the manufacturing and services sectors. However, there is a large fluctuation in the trade balances of the two largest economies, the United States and Japan. They exhibit contrasting changes especially with regard to agriculture and manufacturing sectors. In particular, the large negative change in trade balance in the Japanese agriculture sector is an interesting case. For Japan, agriculture is a highly sensitive and protected sector. Compromising anything in this sector is a hard nut to crack since agricultural policymaking in Japan reflects political power struggles. This state of affairs leaves an open question as to how far a free trade pact would be agreeable to those countries that will have to forgo gains in certain sectors of their interests, while they might also gain in some other sectors simultaneously in different measures. Perhaps, this is an area of research that will need further work, so as to possibly draw the line as to how any imbalances in welfare would be acceptable or tolerable to the concerned parties. TABLE 6.21 SAFTA+5: SECTORAL CHANGES IN TRADE BALANCE Sector Agriculture Manufacturing Services Source: Author.

Changes in Trade Balance (US$ million) SAARC CHN JPN KOR USA 63.50 -1097.94 -83.63

-1475.74 2279.62 -1001.16

-18802.07 17706.20 1979.93

-4803.47 3557.11 -970.82

35686.28 -34852.72 -4526.61

EU

ROW

-731.94 1437.92 838.06

-12568.75 9414.65 7951.61

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PART IV GTAP Model

FIGURE 6.19 SAFTA+5: CHANGES IN TRADE BALANCE 40000 30000

Million US$

20000 10000 Agriculture

0

Manufacturing Services

-10000 -20000 -30000 -40000 SAARC

CHN

JPN

KOR

USA

EU

ROW

Country/Region

Source: Author.

Table 6.22 and Figure 6.20 show the changes in allocative efficiency effect (commodity summary) in the three major sectors of agriculture, manufacturing and services. The allocative efficiencies of SAARC, China, Japan and South Korea turn positive, but in the case of the United States and the EU, it turns negative, especially in the agriculture and services sectors. TABLE 6.22 ALLOCATIVE EFFICIENCY EFFECT – COMMODITY SUMMARY Sector Agriculture Manufacturing Services Source: Author.

SAARC

CHN

JPN

KOR

USA

EU

ROW

79.17 685.39 21.31

1864.72 4584.64 70.04

2903.42 1115.26 -48.37

4564.39 758.44 129.30

-672.93 290.52 -200.79

-430.81 566.07 -164.57

-304.98 -255.57 83.25

FIGURE 6.20 SAFTA+5: ALLOCATIVE EFFICIENCY EFFECT 5000

Million US$

4000 3000 Agriculture 2000

Manufacturing Services

1000 0 -1000 SAARC

CHN

JPN

KOR

USA

Country/Region

Source: Author.

EU

ROW


209

In analyzing these results, a few caveats may be noted. First, one should not regard the terms of trade to be synonymous with social welfare, or even Pareto economic welfare. Terms of trade calculations do not tell us about the volume of the countries’ exports, but only the relative changes between countries. To understand how a country’s social utility changes, it is necessary to consider other variables, such as changes in the volume of trade, changes in productivity and resource allocation, and changes in capital flows. Second, financial analysts often caution that the price of exports from a country can be heavily influenced by the value of its currency, which in turn can be heavily influenced by the interest rate in that country. If the value of currency of a particular country is increased due to an increase in interest rate, one can expect the terms of trade to improve. However, this may not necessarily entail an improved standard of living for that country, since an increase in the price of exports perceived by other nations will result in a lower volume of exports. As a result, exporters in the country may actually be struggling to sell their goods in the international market even though they are enjoying a (supposedly) high price. Third, if we take the case of Japan in this context, its overall trade balance, GDP, and allocative efficiencies still remains largely positive throughout, in which case, the negative terms of trade may not reflect the entire picture. For example, the net trade balance for Japan in the manufacturing and services exceeds that of agriculture sector by US$884.06 million (refer Table 6.21). The other point to note is that the benefits or gains from free trade may not arise only from the positive terms of trade, but there can be other auxiliary benefits that might arise from the political, social and cultural spheres. Also, if we further consider the case of India-Japan FTA and its trade prospects, large benefits are projected to accrue as a result of reciprocal investments and liberalization of trade in services, in addition to movement of professionals. Movements of people and reciprocal investments are undoubtedly important for the spread of not only the fundamental knowledge, but also for the exchange of technological innovations that underlie the broad advancement of human productivity (see Section 3.4.B for more detailed discussion on this issue). These are not reflected in the GTAP results. Finally, the GTAP data pertains to 2001 benchmark (let alone the missing data, if any), and therefore, there is a likelihood that the GTAP 6 database may not reflect a complete and true picture of trade flows that potentially exist among different countries/regions. Viable FTAs among SAARC and +5 Countries • Viable Plurilateral FTAs of SAARC with +5 Table 6.23 shows the most viable FTAs that SAARC as a single entity could have with +5 on a plurilateral basis. SAFTA+5 FTA would be most viable within equal tariffs ranging from 0-5 percent, as well as varying tariff rates between 0 percent and 10 percent. However, this FTA is not feasible at fixed tariff rates.

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PART IV GTAP Model

TABLE 6.23 VIABLE FTAS UNDER SAFTA+5 SCENARIO (PLURILATERAL – SAARC AS SINGLE ENTITY) Plurilateral FTA Simulation Contracting No. Countries 1 S2b SAFTA and +5 2 S2c SAFTA and +5 3 S3b SAFTA and +5 4 S3c SAFTA and +5 Note: As in Table 6.16, note (iii). Source: Author. S/N

Tariffs % Combination Fixed

Equal

Varying

5-5 0-0 10-5 5-0

• Viable Bilateral FTAs of SAARC with +5 Table 6.24 shows the most feasible tariff structure for bilateral SAFTA+5 FTA. With regard to the SAFTA-USA FTA, fixed tariffs of 0 percent-0 percent combination is the most feasible. SAFTA-CHN FTA and SAFTA-EU FTA would be feasible under varying tariff combinations, but lowering anything below 30 percent-20 percent and 10 percent-5 percent in the case of SAFTA-CHN FTA and SAFTA-EU FTA respectively, is not feasible. TABLE 6.24 VIABLE FTAS UNDER SAFTA+5 SCENARIO (BILATERAL – SAARC AS SINGLE ENTITY) Bilateral FTA Simulation Contracting No. Countries 1 S4 SAFTA-USA 2 S11a SAFTA-CHN 3 S15b SAFTA-EU Note: As in Table 6.16, note (iii). Source: Author. S/N

Tariffs % Combination Fixed

Equal

Varying

0-0 30-20 10-5

• Viable Bilateral FTAs of Individual SAARC Countries with +5 With respect to bilateral FTA of SAARC as individual countries (see Table 6.25), Bangladesh has viable FTAs at fixed and varying rates with China, Japan and South Korea but at higher tariffs in general. Sri Lanka has the maximum flexibility to the extent of being able to remove its tariffs completely. Sri Lanka’s FTA is feasible with China, Japan, South Korea, and the EU at fixed, equal and varying tariffs from a minimum of zero to a maximum of 15 percent. This is not a big surprise as Sri Lanka’s economy is the most liberalized of all among the SAARC members. RSA also has a good possibility of having viable FTAs, particularly with China and Japan at fixed, equal and varying tariff rates. As for India, it is quite evident that IND-EU FTA is the most feasible of all. IND-CHN FTA and IND-JPN FTA are viable, but at slightly higher and varying tariff rates.


211

TABLE 6.25 VIABLE FTAS UNDER SAFTA+5 SCENARIO (BILATERAL – SAARC AS INDIVIDUAL COUNTRIES) Bilateral FTA Tariffs % Combination Simulation Contracting Fixed Equal Varying No. Countries 1 S1a BDG-CHN 10-10 2 S11b LKA-CHN 5-5 3 S11c LKA-CHN 0-0 4 S12b LKA-JPN 0-0 5 S13b LKA-KOR 0-0 6 S15a LKA-EU 5-5 + RAS 7 S16b RSA-CHN 5-5 8 S16c RSA-CHN 0-0 9 S17a RSA-JPN 10-10 10 S17b RSA-JPN 5-5 11 S17c RSA-JPN 0-0 12 S30a IND-EU 10-10 + RAS 13 S30b IND-EU 5-5 + RAS 14 S30c IND-EU 0-0 + RAS 15 S31a LKA-CHN 10-10 16 S31b LKA-CHN 5-5 17 S31c LKA-CHN 0-0 18 S32a LKA-JPN 5-5 19 S32b LKA-JPN 0-0 20 S33a LKA-KOR 5-5 21 S33b LKA-KOR 0-0 22 S37a RSA-JPN 5-5 23 S37b RSA-JPN 0-0 24 S41a BDG-CHN 30-20 25 S42a BDG-JPN 20-5 26 S43a BDG-KOR 20-5 27 S46a IND-CHN 20-10 28 S47a IND-JPN 20-10 29 S50a IND-EU 20-5 +RAS 30 S50c IND-EU 5-0 + RAS 31 S51a LKA-CHN 15-10 32 S51b LKA-CHN 10-5 33 S51c LKA-CHN 5-0 34 S53a LKA-KOR 10-5 35 S53b LKA-KOR 5-0 36 S57a RSA-JPN 30-5 37 S57b RSA-JPN 10-0 Note: As in Table 6.16, note (iii); RAS=Removal of agricultural subsidies. Source: Author. S/N

The above results are fascinating because they are in conformity with our expectation. As regards the IND-EU FTA, the EU is India’s largest trading partner, accounting for almost one-fifth of the total trade; India in turn, contributes around 1.5 percent of the total EU trade and is the 10th largest partner of the EU (see Section 3.4.E). In the case of IND-CHN FTA, India and China’s trade trajectory (as discussed in Section 3.4.A) shows that China stands better in terms of its comparative advantage with cheaper goods from highly dispersed and competitive firms that can likely flood

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the Indian market. This is also because India’s tariff rates are still much higher (almost double) than that of China (see Table 6.4). Therefore, deeper reduction in tariff rates can open doorways to broad-based and cheaper imports from China to India. As for Japan, at lower tariff rates, India’s manufacturing sector in particular can be easily outmaneuvered by more technologically advanced Japanese manufacturing firms competing in the Indian market. Thus, this situation raises concerns for both sides. While Japan is apprehensive about losing in its agriculture sector and therefore will be bent on protecting its agriculture sector; India, on the other hand, will have difficulty to compromise liberally on its protected manufacturing sector.22

6.6 CONCLUSION In this chapter, two fundamental questions were examined. The first was to investigate the economic effects and welfare implications of SAFTA and SAFTA+5 on trade flows as a result of the reduction in tariffs, while the second was to determine which of the contracting parties were likely to have the most viable FTAs. Additionally, two hypotheses were tested on whether selective combinations of tariff rates will result in welfare gains of both the contracting parties, and whether or not FTAs with observer countries will be welfare improving, causing more trade creation than trade diversion. Based on the above foundation, the chapter in essence has evaluated the major effects and welfare implications of SAFTA and SAFTA+5. Exhaustive experiments were performed using the equivalent variation component to gauge the welfare gains and losses of different countries/regions. Additional analyses were carried out to investigate the trade creation and trade diversion effects, changes in industry output, private household demand, aggregate exports and imports, changes in terms of trade, GDP indices, and allocative efficiencies. Finally, some viable FTAs were identified among SAARC and +5 countries. The findings revealed that plurilateral FTAs with deeper liberalization would be a win-win situation for all countries concerned generating largest welfare gains as opposed to bilateral FTAs. The magnitude of gains varies from one FTA to another. The maximum possible FTAs emerge from varying combinations of preferential tariffs as compensation by the non-LDCs/developed countries to the LDCs. Among the SAARC members, potential welfare gains were ensured with a selective combination of varying tariffs as opposed to fixed or equal tariff combinations. The results were encouraging because they firmly supported the two hypotheses posed in this model. Our first hypothesis was strongly buttressed by the fact that selective combinations of tariffs (or compensating the loser by way of preferential tariffs) resulted in welfare gains for both the contracting parties generating a number of viable FTAs among SAARC and +5 countries. There was also ample evidence suggesting that SAFTA and SAFTA+5 were indeed welfare enhancing, and resulted in 22 On an average, the ad valorem tariff rate of the manufacturing sector in India (to Japan) is 32.5 percent, while that of Japan (to India) is only 5.4 percent.


213

net trade creation than trade diversion, corroborating our second hypothesis. All the same, trade liberalization under the SAFTA scenario created lesser gains vis-à-vis SAFTA+5 scenario. Trade liberalization also caused major fluctuations and large adjustments in the industry output and sectoral production in all countries; however, the household demand, aggregate exports and imports, terms of trade, GDP, and allocative efficiencies for SAARC as well as +5 increased considerably, particularly in the case of SAFTA+5 scenario. Comprehensive analyses point to some important policy implications. Plurilateral FTA among SAARC countries will not be a feasible proposition, while the same with +5 countries will be the most rewarding of all FTAs. SAARC as a single entity will invariably benefit by having FTAs with South Korea, the United States and the EU. Moreover, the findings suggest that possibilities do exist for several viable FTAs. Some noteworthy ones are SAFTA - +5, SAFTA-USA, SAFTA-CHN, SAFTA-EU, IND-RSA, IND-LKA, IND-JPN, IND-EU, LKA-CHN, LKA-JPN, LKA-KOR, LKA-EU, RSACHN, and RSA-JPN. There is an implication that preferential tariff compensation improves the chances of widening the possibilities for more FTAs. However, while there will be marked improvement in the welfare of the member states, the welfare of ROW could be considerably reduced due to substantial trade diversion. Slashing import tariffs for all traded goods by a fixed proportion will not be in the best interest of all members. As a starter, the best set of tariff combination is to compensate the losers by way of tariff concessions by the winners. This means that non-LDCs should allow LDCs with a grace period to enable them to liberalize selected sectors over time. The long-run implication is, of course, to aim for much deeper liberalization under the SAFTA+5 scenario.23 Nonetheless, one caveat in this regard for the SAARC countries now is to take a selective and calculated approach to tariff reduction, and liberalize only those sectors that ensure positive gains, based on the potential impacts and level of competitiveness of each sector. The findings also establish that tariff reduction must be governed by the principle of less than full reciprocity in favor of developing countries. That means developing countries should cut their industrial tariffs less than developed countries. In other words, it should take into account special and differential treatment (SDT) for developing countries with flexibilities to reduce to a lesser extent or exclude that reduction in a certain quantity of tariff lines that are considered sensitive.24 While this research is by no means the end of algorithm on the subject, few limitations may be set forth as follows: First, the importance of other trade barriers, such 23 While full or deeper liberalization can have a positive impact on welfare and GDP of countries; however, logically speaking in the case of LDCs, this means that the greater the liberalization, the higher will be the fall in their tariff revenues. In other words, exports tend to rise with higher degrees of liberalization, but imports tend to rise even faster, thus leading to deterioration in the balance of payments. 24 Special and differential treatment (SDT) describes a modality whereby more favorable or preferential provisions are provided only to developing countries and LDCs under trade negotiations and agreements. Less than full reciprocity is contained in the Doha Round mandate, which was ratified in the framework pact approved by the WTO in July 2004, and appears in the mandate for the sixth ministerial conference in Hong Kong, 2005.

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PART IV GTAP Model

as non-tariff barriers and para-tariffs though well recognized could not be considered, as these components do not lend themselves readily to quantification within the purview of the GTAP analysis. Second, the GTAP 6 data pertains to 2001 benchmark, hence future work could use more recent data. Lastly, the use of dynamic analysis might be another alternative to deduce more conclusive findings.

PART V CONCLUSIONS AND RECOMMENDATIONS

CHAPTER

7

Key Findings and Policy Implications

215

CHAPTER

7

Key Findings and Policy Implications

T

his final chapter summarizes the major findings of the study in Section 7.1 suggesting answers to the core questions raised in Chapter 1, and it documents some important policy implications and recommendations in Section 7.2. In so doing, the chapter highlights some vital lessons inferred from the study in Section 7.3 to build a road map and a way forward for effective regional economic integration for the SAARC bloc. Section 7.4 concludes, also indicating some limitations of the study and directions for further research.

7.1 SUMMARY OF KEY FINDINGS The aim of this chapter is to extract and compile the main findings from the previous Chapters 2, 3, 4, 5 and 6, and then deduce some underlying policy implications and recommendations in an attempt to build the way forward for effective regional economic integration for SAARC countries. In Chapter 1, the foundation of the research was laid down by stating five research questions and four hypotheses with the objective of addressing them in the subsequent chapters (see Section 1.4 and 1.5). The first question was addressed in Chapter 2. It was ascertained in this chapter that the South Asian economies are growing rapidly maintaining a high growth rate during the past two decades. The structure of exports and imports of SAARC and observer countries indicate good possibilities for complementarities on account of each country’s comparative advantage. However, SAARC is still far from maturing as a regional grouping because the export performance of the SAARC bloc stands much lower in comparison with other regional trade blocs in Europe, America, or even with other Asian blocs. Intra-SAARC exports hover at around 4 percent of the total trade as compared to more than 60 percent in the EU and 20 percent in the ASEAN bloc. Nevertheless, SAARC is optimistic and looking forward to bilateral and plurilateral trading arrangements including India-ASEAN FTA, BIMSTEC FTA and South Asian Economic Union (SAEU). The inference from this chapter is that there are ample opportunities for benefiting from accelerated trade expansion considering the region’s low share of trade, rapidly growing economies and its huge population. Question 2 was tackled in Chapter 3. The findings in this chapter evinced that free trade is the order of the day. All forms of trade liberalization including bilateral, 216

CHAPTER 7 Key Findings and Policy Implications

217

plurilateral and multilateral trade liberalizations are considered as suitable means for opening world markets, and every country stands to gain from free trade. The SAFTA framework was developed with the conviction that much trade can be created by reduction of tariff, non-tariff and para-tariff barriers and promoting various trade facilitation measures. Evidently, non-tariff and para-tariff barriers in South Asia hinder trade to a large extent. It was established that India is the hub in the region, and therefore, it has a significant role to play. India has already started negotiating for comprehensive FTAs with Japan, the EU and South Korea. Given this growing influence and relationship outside of the bloc, other SAARC members can also benefit in the process. To take advantage of the region’s full trade potential, India should take the lead to appease the growing tensions with Pakistan. Question 3 was addressed in Chapter 4 and Chapter 5, where the impact of trade agreements on the volume of exports of SAARC countries was examined. Chapter 4 investigated the effect of SAPTA on intra-SAARC trade using a generalized gravity model. Findings showed that the trade agreements did have positive impacts on enhancing the level of intra-regional trade, but only in the post-SAARC/post-SAPTA periods. However, the impact of trade agreements was rather sluggish, as it took nearly two decades to double the trade in the region. Moreover, the catalytic role of SAPTA was not found to be very significant, as the positive impact appeared to have emanated from the delayed effects of the existing bilateral agreements among the member countries. The same questions and hypotheses posed in Chapter 4 were re-examined and tested in Chapter 5 using a standard and augmented gravity model. Additionally, the data was harmonized for all sub-periods, and an additional variable was introduced to estimate the impact of India’s trade liberalization policy as well as the trade creation and trade diversion effects of the bilateral trade agreements. It was found that the results did not vary much in essence. There was a good evidence of trade creation in the postSAARC and post-SAPTA periods, but only for those countries that have trade agreements in place. Moreover, India’s trade liberalization efforts showed a significant positive impact on SAARC’s exports, especially in the post-SAPTA period. In Chapter 6, Question 4 on the economic effects and welfare implications of SAFTA as well as SAFTA+5 was addressed by employing the GTAP model. Exhaustive simulation scenarios were designed and extensive experiments performed on a case-by-case basis. In so doing, the welfare effects of different FTA scenarios was gauged. The economic effects on other variables, such as industry output, household demand, exports and imports, terms of trade, GDP indices, and allocative efficiencies were also computed. The findings suggested that the effects of SAFTA and SAFTA+5 are highly promising. However, the core essence of accomplishing the objectives of deeper regional integration lies in sincere commitment and political will of all SAARC leaders to integrate beyond the fetters of social and political confines. Indeed, there was enough evidence to support all of the four hypotheses tested in this study. The final research Question 5 is addressed in Section 6.2 that follows.

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PART V Conclusions and Recommendations

7.2 POLICY IMPLICATIONS AND RECOMMENDATIONS From the main findings of the study, attention turns to some important policy implications and recommendations. These may be enumerated as follows: • Identify existing production capabilities to exploit full trade potential: While the intra-regional trade in SAARC is not so impressive, but there is an indication for substantial growth of trade (see Chapter 2). This is because the region’s full potential for trade is marred by different forms of barriers and the existence of a large informal trade. This calls for collective efforts from all SAARC member states to aim for deeper trade liberalization by creating the necessary environment for harnessing the region’s potentialities more effectively. SAARC should identify the existing production capabilities of the region and match them with each country’s demand structure and comparative advantage to determine the magnitude of future trade potential. An extensive study is necessary to pinpoint the impediments and barriers that have prevented and/or restricted potential trade flows in the region. • Upgrade trade facilitation measures for smooth trade: Unlike other major trade blocs that have very extensive trade facilitation mechanisms in place, several factors including the lack of transit facilities, inadequate infrastructure, inefficient and cumbersome customs formalities, stringent laws, regulations and administrative guidelines inhibit the smooth flow of goods and services in the SAARC bloc (see Chapter 2). Therefore, there is a strong need for SAARC countries to upgrade the existing infrastructure (e.g., roads, railways and seaports),1 and more importantly, to streamline its applicable rules and regulations, simplify and harmonize its customs formalities, and adopt the latest technology, including electronic data interchange (EDI) and paperless trading. Thus, trade facilitation measures that address all of these challenges are necessary to augment trade flows, whether regional or multilateral. • Dismantle tariff, non-tariff and para-tariff barriers: The average tariffs in SAARC countries continue to be much higher compared to the rest of the world. Evidently, nontariff and para-tariff barriers in South Asia are an even greater hindrance to trade than are tariffs (see Chapter 3). Identifying these barriers has been the main bottleneck in implementing the SAFTA Agreement. Therefore, SAARC should not only lower tariff rates on a faster track than as proposed in the SAFTA schedule, but also handle the issue of non-tariff and para-tariff barriers more seriously through reciprocal negotiations. • Change in bureaucratic, social and cultural perception: Free trade regimen envisaged in the SAFTA Agreement necessitates a fundamental change in bureaucratic, social and cultural thinking at all levels. More importantly, such change can take place only when senior political leaders of SAARC at the highest level make a firm commitment for freer and deeper trade integration. • Divert attention from traditional sources: Most SAARC countries produce a narrow range of goods and tend to compete with each other in the global market 1 In this study, countries with seaports have had a very significant impact on exports (see Chapters 4 and 5).


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predominately in the primary products. This is a big limitation for a free trade area because the lack of diversity limits the pace of integration. Even though the region’s range of potential for trade complementarities is wide, their latent potential has not been exploited (see Chapter 3). SAARC should divert its attention from traditional sources, ease unnecessary restrictions, and thus open the way for mutual exchange of trade and commercial privileges. • Ameliorate supply-side constraints via export diversification: Supply-side weaknesses of many SAARC countries also hamper trade based on comparative advantage. SAARC’s performance in terms of product and market diversification has been relatively poor. In general, SAARC members other than India have relatively undeveloped manufacturing sectors. Such a scenario severely limits competition. Findings of this study suggest that Bangladesh was a loser and most SAARC countries were forced to pull back to the agriculture sector, while the observer countries, particularly Japan and South Korea, were likely to dominate the manufacturing and services sectors (see Chapter 6). Moreover, both SAARC countries and China would specialize in similar products comprising mostly agro-based and manufacturing sectors, which imply that SAARC countries will have to deal with cheaper Chinese goods flowing into the region. There is, therefore, a strong need for SAARC countries to diversify and further intensify its export basket particularly for Bangladesh, Pakistan and Sri Lanka. Smaller countries such as Bhutan, Maldives and Nepal could benefit by specializing either in niche products or focusing on value-added products. • Upgrade technology profile of exports: SAARC countries have also not been able to upgrade the skill and technology profile of their exports which has implications in terms of value addition and competitiveness. As such, there is a clear indication of the need for this region to make innovative efforts to upgrade the technology profile of exports. • Promote intra-industry trade: What follows from the above point is that the gains from intra-regional trade will accrue through export diversification and facilitating intraindustry trade between India and the other SAARC nations. Intra-industry trade is an important consideration so that they could take full advantage of free trade. • India should take the lead: Despite the fact that India’s developing trade relations with some of the observer countries paint a rosy picture, India may still need to work more to stabilize its own position. There is a requirement to align its conformity assessment and procedures, especially for products of mutual trade interest by lowering down tariffs, simplifying guidelines, and any other legal and regulatory framework that deter trade flows. Quite clearly, India is the focal point in the Indian sub-continent – all other economies are dependent on India not only as a supply source and export destination, but also in terms of technology, investment, and monetary links (see Chapter 3). Therefore, India has a crucial role to play in making the SAPTA or SAFTA chronicles a success. In order for SAFTA and SAFTA+5 to materialize, it is vital that India does not wrangle with its neighbors; India should be the solution, supporting and resolving the existing problems, rather than being a part of the problem. It is evident

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that India, among the SAARC countries, has comparatively higher experience, expertise, technology, capital, and connections to set up joint ventures and development projects. Not much investment and exchange has taken place for reasons related to political conflicts, mistrust, and ‘perceived’ fear of Indian dominance. Consequently, the level of economic interactions and interdependence among the SAARC countries is limited. Against this backdrop, it is imperative for India to formulate its own trade policy in such a manner that it is able to effectively combat these obstacles. Though India is serious about enhancing trade and investments via inter-regional FTA deals, the reality at home is starkly different with its ‘adversary’ – Pakistan. India’s interest would be best served if it continues to work on amicable solutions, and come to an FTA deal with Pakistan, and thus alter the image of SAARC for good. India’s continued successful development largely depends not only on the kind of trade policies and integration it pursues outside of the bloc, but more importantly on the strategy and attitude towards its closest neighbors. Sitting down at negotiating tables outside the bloc is important, but it might be wiser and more constructive for both India and Pakistan to work towards building a conflict-free South-South relations in their own backyard and putting their own house in order. • Exploit latent complementarities and discover new comparative advantages: SAARC has indeed the potential to create enough trade complementarities due to wide disparities in industrial structure and natural resources. Specific trade complementarities can be created based on individual country’s comparative advantage and resource endowments. Intra-regional and inter-regional economic cooperation can and will certainly help revitalize the latent complementarities and help discover new comparative advantages to further stimulate trade. Some promising prospects for immediate intraregional trade expansion exist in sectors such as: (i) textiles, garments, and rural banking services for Bangladesh; (ii) hydroelectricity, fresh fruits, minerals and ecotourism for Bhutan; (iii) chemicals, automobile, machinery and equipment, and pharmaceuticals for India; (iv) fishing and tourism for Maldives; (v) traditional carpets, clothing and tourism for Nepal. Nepal can also take advantage of its fast-flowing Himalayan Rivers to generate and sell hydroelectricity; (vi) textiles, leather goods, chemicals and other manufactures for Pakistan; and (vii) tea, spices, diamonds and coconut products for Sri Lanka. In addition, the region would benefit from exploring and developing industries that provide strong forward and backward linkages on products, balanced vertical integration, horizontal specialization, intra-industry relationships, and inter-sectoral processes that have linkages to innovation, technology spillovers and multiplier effects thereof. • Promote joint ventures and investment cooperation: South Asia is one of the regions of the world with scarce capital. This calls for collective and collaborative efforts for joint-ventures among countries in this region. The challenge for the SAARC bloc is to promote bigger and faster investment by tapping the investment potential within the region, and also by streamlining and designing innovative means to attract joint ventures and investment cooperation from outside the region.


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• Mitigate conflicts amicably through peaceful political dialogue: Dirty politics in the SAARC region has always been a stumbling block to trade expansion. It is high time that SAARC should learn from the success stories of other regional trade blocs. Successful regional trading arrangements do more than just integrate economies, but they also help ease political tensions. Such an initiative would not only be of great significance for the conflict-stricken South Asia, but the leaders of SAARC could also draw on this analogy to further extend the economic precincts beyond the confines of SAARC. It was ascertained in this study that the conflict variable was negatively associated with exports at a high level of significance across all periods (see Chapter 4 and 5). As such, there are undoubtedly good prospects for boosting SAARC’s trade, diversifying investment, and integrating beyond the periphery of South Asia, if all SAARC countries take concerted efforts to palliate conflicts through peaceful dialogue with the right perspective, strong commitment, and affirmative political will. If India and Pakistan could tap the region’s trade potential, intra-SAARC trade could reach new heights. If the existing conflicts in the region, particularly between India and Pakistan, are amicably settled, and the benefits of trade liberalization suitably tapped, the growth of trade in the SAARC region will take a different turn. • Introduce open-door policies: With the growing interest of observers around the world, SAARC will not only encounter new challenges but at the same time find new opportunities. SAARC should therefore, mature through further dismantling of both tariff and non-tariff barriers. More importantly, the weakness of SAPTA can be compensated by shaping and sharpening the influence of SAFTA. This also implies that all SAARC countries must come out of its egocentric cocoon and discard its autarkic policies. • Build institutional capacity and promote an enabling environment: Capacity building with appropriate institutions for supporting and establishing the export base via proper marketing strategies are some essential considerations in this regard. While many developing countries desperately seek to ensure market access through regional and multilateral trade negotiations, limitations in their own domestic policy and market environment may not allow them to take advantage of the opportunities that are already available. SAARC is no exception. Developing favorable market condition and an enabling environment is another initiative to be taken in this area. As such, how to take advantage of the regional integration, and formulate an inclusive trade liberalization package constitutes an important policy task. • Promote deeper economic cooperation and trade liberalization: One compelling reason to suggest broader economic cooperation and free trade is to raise the living standards of peoples in the region. The implementation of SAFTA is only the beginning in this direction. India’s liberalization has had a significant positive impact on SAARC’s trade, especially in the post-SAPTA period registering an increase in export by about 86 percent (see Chapter 5). The implication from this result is obvious. Free trade without vested interests will benefit not only India, but it could be instrumental in creating positive repercussions on all other smaller countries in the region. Each country

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has certain advantages over the other, regardless of its size or wealth. For instance, even a big country like India depends on the enormous hydropower potential of Bhutan’s fast-flowing Himalayan Rivers. India in turn allows border-free access and supplies reasonably cheap manufactured goods to Bhutan. Thus, it is in the general interest of all SAARC countries to explore such symbiotic ties and create a more conducive environment for expanding intra- as well as inter-regional economic cooperation. • Expedite and intensify the process of regional trade integration: While there is a strong signal and need for deeper trade integration among SAARC member states, the empirical investigation demonstrated that, in the case of SAARC countries, the impact of trade agreements took nearly two decades to double its exports (see Chapters 4 and 5). This was evidenced clearly due to the sluggish nature of the SAARC’s approach to regional integration and incessant conflicts between the member states like India and Pakistan. In the case of countries without conflict, for example, India and Nepal, India and Bhutan, India and the Maldives, trade has been always smooth, but the volume of India’s trade with these smaller economies is very small. In addition, the magnitude of informal trade between partners, such as India and Bangladesh, India and Pakistan, and India and Sri Lanka is very large due to porous border and close historical and cultural ties, which in actual fact, do not get reflected in the published data. Hence, there is considerable potential for trade in South Asia, if India and Pakistan resolves their hostility,2 and also if the liberalization process of SAFTA and BIMSTEC are expedited and intensified further. • Stimulate transition from SAPTA to SAFTA: It was established from the findings of this study that SAPTA has had a little catalytic impact on exports—except for those countries that have bilateral trade agreements in place (see Chapters 4 and 5). This has a direct relevance to those member states whose political and economic ties rest on shaky foundations. This also implies that the rift among SAARC countries has made regional cooperation difficult and has impelled the member states to pursue their economic goals ‘bilaterally’ rather than ‘plurilaterally’. It seems as if the transition from SAPTA to SAFTA needs to be strongly goaded, or else, the vision for economically independent and self-reliant South Asia will remain far from reality. • Take selective and cautionary approach to tariff reduction: The welfare effects and feasibility analyses of FTAs revealed that SAARC’s association with the five observer countries of China, Japan, South Korea, the United States and the EU will be welfare improving for all, as there was evidently significant welfare gains and net trade creation. However, considering its immature export base, SAARC countries should take a selective and calculated approach to tariff reduction, and liberalize only those sectors

2 To this end, however, there are already good signs and positive developments taking place. For instance, in October 2008, India and Pakistan opened their border for trade in the north of Kashmir (in the Line of Control between India and Pakistan). This was a historic move after 61 years of impasse since the conflict started in 1947. It is believed that without restrictions, cross-border trade between the two countries could reach US$6 billion a year. In view of this changing circumstance, a significant transformation in the trade structure of SAARC region is envisaged in the near future.


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that ensure positive gains, based on the potential impacts and level of competitiveness of each sector (see Chapter 6). • Apply principle of less than full reciprocity: The findings also establish that tariff reduction must be governed by the principle of less than full reciprocity particularly in favor of the LDCs by non-LDCs in the case of intra-regional FTAs, and in favor of SAARC countries by observer countries in the case of inter-regional FTAs (see Chapter 6). This means that the developing countries should cut their tariffs relatively less than the developed ones. In other words, special and favorable treatment for developing countries should take into account flexibilities to reduce or exclude that reduction in a certain quantity of tariff lines that are considered sensitive. For instance, under the fixed tariffs, even India loses with all the five observers. FTA would be feasible for India only under equal or varying tariffs with selected partners, such as the EU, China and Japan, but at higher protections vis-à-vis the observer countries.

7.3 THE WAY FORWARD Deeper regional economic integration and FTAs will undeniably be beneficial to the region in many respects. First, FTAs through progressive tariff reduction can help accelerate domestic policy reforms that distort prices, as they open up borders. Second, the bilateral and intra-regional trading arrangements can serve as a building block towards inter-regional and multilateral trade liberalization. Third, such initiatives will enable SAARC countries to eliminate domestic distortions that are contradictory with free trade, whether from a regional or global perspective. Transparency, simplifying and harmonizing procedures, and adoption of new technologies are some of the incentives for creating a feasible environment for a way forward. In light of the policy recommendations as discussed above, we come to a point where a road map or a way forward for regional trade integration can be established. This concept is represented in a simplified format in Figure 7.1. The concept may be viewed from three different perspectives—socio-cultural, political and economic, with reference to both intra- and inter-regional trade. Firstly, promoting free trade in SAARC requires a change in social intellection or in cultural mindset to accept variation and influx of new ideas, and to be more receptive to the notion of openness, free trade and integration into the global economy. Mutual trust, confidence, and support from all stakeholders including the public and private business community can ease the way for a smooth transition to more liberalized economies. Secondly, political interventions from the governments of respective member states would play an important role in reconciling existing conflicts as well as raising awareness and developing international understanding. The government of each country can play an important role in building political ties and fostering international cooperation in innovative areas. In this context, India has a key role to play in resolving the issue of conflict and tensions, particularly with Pakistan.

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FIGURE 7.1 THE WAY FORWARD INTRA-REGIONAL TRADE

Socio-cultural intellection change

Evolution of social and

Political intervention

Endorse meaningful

Economic revitalization

Comprehensive study

Balanced vertical integration

Export-

processing zones Economies of scale

cross-cultural mindset Mutual respect, trust and tolerance Confidence-building measures Accept variation and change

dialogue Manage longstanding intra-regional conflicts Resolve existing border tensions between states India to take the lead role

Eliminate different forms of trade barriers Develop and upgrade trade facilitation measures Create trade complementarities and comparative advantages Accelerate SAFTA process Promote joint ventures and investment cooperation Expedite pending FTAs Diversify exports Promote intra-industry trade Create enabling environment Support private sector and entrepreneurship Introduce e-technology and value addition

INTER-REGIONAL TRADE

Socio-cultural intellection change

Proactive and reactive

Political intervention

Develop international

forces receptive to global orientation Public and business community support Positive reinforcement that promote integration into the

understanding

Build stronger political ties

Foster deeper international cooperation in innovative spheres Economic revitalization

Lower tariffs and nontariff barriers

Simplify procedures Comprehensive study

Economic development zones/exportprocessing zones Global marketing networks

and upgrade trade facilitation measures Sign CEPAs and/or regional FTAs Promote joint ventures and investment cooperation (FDI) Expedite India’s FTAs with Japan, S. Korea, and the EU Diversify exports and its profile Specialize in niche industries/products Create enabling environment Promote deeper trade liberalization and economic integration Technology, value addition and competitiveness

Paving the way forward for free trade and regional trade integration Source: Author.

Thirdly, SAARC can achieve economic revitalization through various interventions, such as lowering and/or eliminating tariffs, non-tariff barriers and paratariff barriers, upgrading various trade facilitation services, creating trade complementarities and exploring comparative advantages, strengthening and


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accelerating concessions under SAFTA, setting up of regional joint ventures and investment cooperation, expediting the signing of pending FTAs, diversifying the export basket, promoting intra-industry, and further deepening trade liberalization and economic integration. In addition, the introduction of e-technology and value addition to products would be some of the key components to complement economic reinforcement. Sharing available technologies can indeed have a considerable improvement on the region’s interdependence and sovereignty. Overall, such a scheme would require an extensive study to identify and pinpoint the necessary ingredients to be injected for paving the way forward for free trade and regional trade integration, besides increasing balanced vertical integration, capturing economies of scale, developing economic development zones and export-processing zones, and taking a stake in the global marketing networks. Given the limited development of transnational market forces in South Asia, any prospect for regional economic cooperation left exclusively to chimerical forces could take decades. As established in past studies as well as in this study, concerted efforts and strong political will of all South Asian leaders to collaborate seriously are the necessary conditions (if not sufficient conditions) for promoting regional economic integration. Several years of conflict in the region has reaped only fear and poverty. Now, it is high time that trade and economics should pave the way towards peace and prosperity. Furthermore, SAARC should take a more holistic and forward-looking approach to attaining the goal of outshining post-SAFTA benefits by including deeper forms of integration, eliminating all forms of barriers to trade, and expanding trade facilitation measures, such as in services, energy, institution and infrastructure development, monetary integration and investment cooperation. In addition, the future of SAARC countries depends, inter alia, not only on the level of economic integration, but it is also largely dictated by the political soundness in the region. Without easing political tensions, conflicts, and mistrust among the member nations, it is unrealistic to hope for any substantive trade integration in the region. Nonetheless, setting superfluous goals is likely to be counter-productive. SAARC should rather pursue achievable targets and seek joint development projects with the leading observer countries, including in particular, Japan, South Korea and the United States. The establishment of SAARC Development Fund (SDF) was a major step forward, yet there are also some vital lessons that can be learned from the experiences of other successful regional blocs.

7.4 CONCLUDING REMARKS In summing up, four factors can be outlined here to explain the low intra-regional trade among the SAARC countries. First, all SAARC countries (with the exception of Sri Lanka) maintain high tariffs and non-tariff barriers, and separate each other by autarkic policies. Second, most of the countries are producers of homogeneous primary products,

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and they tend to compete with each other. Third, there is a lack of adequate infrastructure and communication links, which is a serious handicap. Finally, the prevalence of sustained conflict and political differences among the member states create a rift and unwillingness to work together effectively. All told, one vital lesson that can be derived from this study is that the regional economic integration in South Asia will require extra commitment and determination to tearing down its complex structure of hardcore policy bound within the walls of protections. This necessitates, beyond any doubt, progressive thinking by all SAARC leaders pretermitting their fears for freer trade and deeper liberalization. Such a stimulus would indisputably go a long way towards transcending their shackles of doubts and suspicions. South Asia has great potential; it can become as successful as any other blocs. There is much hope as the shift in trade focus to this region can already be felt, while the United States and the European economies are slowing down. Concentrating and exploiting its own large market potential and robustly enhancing the South-South trade, perhaps, might be the key to achieving South Asian prosperity. Moreover, the findings send a clear signal that both SAARC as well as the five observers can gain positive benefits by way of a free trade pact. In so doing, there are not only manifestations of trade and economic benefits; such integration can also become a channel for SAARC countries to greater exposure to the global economic system, as they can intensify and step up international understanding by associating and learning from the experiences of veteran leaders in trade, particularly Japan, the United States and the EU. Last but not least, closer regional economic cooperation may also mean deeper social, cultural and political ties in the long run, and therefore, it can become a conduit for a better world.3 Finally, there are some limitations to this study which may need to be addressed in the future. In the gravity model, this study used a panel dataset for an extended period from various sources, such as the World Bank’s World Development Indicators, the IMF’s Direction of Trade Statistics Yearbook, and the UNCTAD’s Handbook of Statistics. In many cases, the data from different sources for the same variable and time period were found to be widely different. Moreover, different sources contain varying degrees of missing data for countries in different years, while the data was completely missing for some smaller countries in most cases. The author also noticed that the data in some cases were divergent from the respective country sources. As such, one of the 3 A good precedent along this line is that Japan is already providing US$200,000 annually to SAARC through the Japan-SAARC Special Fund (see http://www.nerve.in/news:25350010397, retrieved September 22, 2008). Moreover, the Japan Special Fund, through the ADB, has also committed to provide a US$650,000 grant for the feasibility study for a road project in Bhutan that will ensure more balanced and sustainable economic expansion and poverty reduction in the less developed southern part of the country; Bhutan will extend US$165,000 to complete funding requirement (see “Japan, ADB Helping Develop Road Network in Southern Bhutan,” News Release, October 21, 2008, Manila, The Philippines, http://www.adb.org/Media/Articles/2008/12667bhutanese-roads-developments/ (retrieved October 21, 2008). Indeed, Japan’s contribution and participation as an observer in itself is a testimony to the deepening relationship and commitment to further cooperation between Japan and the SAARC countries.


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worthwhile directions for future is the creation of reliable and comprehensive database on important economic variables for all SAARC countries. Other specific limitations may be enumerated as follows: First, the focus of the gravity model was limited to examining the impact of a few specific variables of interest. Therefore, future research may well consider investigating the effects of other forms of trade facilitation measures, such as services, energy, infrastructure, monetary and investment cooperation. Second, the model could be widened and customized with more contemporary techniques to validate the results of this study. Third, in the GTAP model, although the importance of other forms of trade barriers, such as non-tariff and paratariff barriers are well recognized, they could not be considered as these components do not lend themselves easily to quantification within the purview of the GTAP analysis. This remains a topic for future research. Fourth, the use of dynamic analysis might be another alternative to deduce more conclusive findings. Fifth, the author used SAFTA and SAFTA+5 scenarios for investigating bilateral and plurilateral effects. Interested researchers could use a similar framework to test other simulation scenarios and policy experiments. Finally, the GTAP 6 data pertains to 2001 benchmark, and moreover, the rest of South Asia (RSA) includes Pakistan, which is one of the influential non-LDCs in the SAARC bloc. Hence, future work could utilize more recent data, preferably the recently released GTAP 7 data package for more conclusive findings.4

4

Pakistan is treated separately in GTAP 7 database.

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APPENDICES A1 STATUS OF TRADE AGREEMENTS AMONGST SAARC COUNTRIES Date/Year Jan 1972; Renewed Mar 2, 1995; July 28, 2006 Mar 28, 1972; Renewed Mar 21, 2006 Apr 2, 1976

Contracting States India and Bhutan

Apr 3, 1979

Nepal and Sri Lanka

1980; Renewed Sep 2000

Bangladesh and Bhutan

Mar 31, 1981

India and Maldives

Jul 28, 1982

Pakistan and Nepal

Dec 6, 1991

Nepal and India

Apr 11, 1993; Operational Dec 7, ‘95 Dec 28, 1998

Seven member states

Jun 12, 2005

Pakistan and Sri Lanka

India and Bangladesh Nepal and Bangladesh

India and Sri Lanka

Agreement Type/Title Agreement on Trade, Commerce and Transit between the Govt. of the Rep. of India and the Royal Govt. of Bhutan Trade Agreement between India and Bangladesh Trade and Payment Agreement between His Majesty’s Govt. of Nepal and the Govt. of the People’s Rep. of Bangladesh Trade Agreement between His Majesty’s Govt. of Nepal and the Govt. of the Dem. Socialist Rep. of Sri Lanka Trade and Transit Agreement between the Govt. of the People’s Rep. of Bangladesh and the Royal Govt. of Bhutan Trade Agreement between the Govt. of the Rep. of India and the Govt. of the Rep. of Maldives Trade Agreement between the Govt. of Islamic Rep. of Pakistan and His Majesty’s Govt. of Nepal Free Trade Agreement between His Majesty’s Govt. of Nepal and the Govt. of India South Asian Preferential Trading Arrangement (SAPTA). Free Trade Agreement between the Rep. of India and the Dem. Socialist Rep. of Sri Lanka Free Trade Agreement between the Govt. of Islamic Rep. Pakistan and the Dem. Socialist Rep. of Sri Lanka South Asian Free Trade Area (SAFTA).

Jan 6, 2004; Seven member states Operational Jul 1, 2006 Note: 1. Bangladesh and Pakistan are in the process of discussion on pursuing an FTA between the two countries to help boost bilateral trade and investment (http://www.bilaterals.org/article.php3?id_article=4479&var_recherche=bangladesh+pakistan+fta). 2. Bhutan is discussing trade agreements with Nepal and Thailand. 3. Sri Lanka has mooted an FTA with Bangladesh citing its positive experience with the ones it has signed with India and Pakistan (http://www.bilaterals.org/article.php3?id_article=5269). 4. Pakistan has ratified the South Asia Free Trade Area (SAFTA), but the FTA between India and Pakistan was not initiated under this agreement, instead it is continued under the existing import regime (http://www.bilaterals.org/article.php3?id_article=3908). Source: Author’s compilation from various sources.

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240

Appendices

A2 GROWTH PROSPECTS IN SOUTH ASIA Bangladesh In Bangladesh, growth rebounded to 6.7 percent owing to stronger remittance inflows, vibrant services and manufacturing sector output and the waning impact on agricultural output of last year’s floods. One more encouraging note is that Bangladesh’s GDP growth has been a steady 5 percent for the past several years. With an estimated growth of 5.6 percent in 2008, the outlook is also optimistic. Bhutan Bhutan’s GDP to expand by an estimated 14 percent in 2006. Bhutan’s GDP growth posted strong gains of 14 percent in 2006, largely as a result of capacity expansion following the coming on stream of the Tala hydroelectric plant. Bhutan’s economic expansion remained strong, at about 12 percent in 2007, as the impacts of the Tala hydropower project continue to be felt. A continuation of sound macroeconomic and development policies is foreseen. With the current high non-inflationary GDP growth, the outlook is upbeat with a projected real GDP growth rate of 22.4 percent in 2008. India India, the largest economy in the region, led the way with GDP expanding by an estimated 8.7 percent in 2006 – backed by nonagricultural growth in excess of 10 percent. Very low real interest rates combined with an improved business climate and rising household savings have enabled higher investment rates, helping to sustain stronger growth. Following a slowdown in 2007, India’s economic growth is expected moderate to 8.0 percent in fiscal year (FY) 2008, against 8.7 percent in FY2007. The growth is expected to rebound to 8.5 percent in FY2009. Maldives In the Maldives, a rebound in tourism, post-tsunami reconstruction, and new resort construction helped increase GDP at a robust rate of 18 percent in 2006. The construction of 46 new resorts is expected to push the economy over the forecast horizon. The growth in 2007 was at nearly 12 percent, but it is likely to slow down by about one-half to 6 percent in 2008. Nepal Nepal’s GDP to expand by an estimated 1.9 percent in 2006. Economic activity in Nepal slowed to 1.9 percent because of the intensified conflict, a weather-related decline in agricultural production, and a trend decline in clothing exports. The growth is projected to strengthen, owing to diminished political uncertainties following the recent restoration of Parliament and the cessation of fighting with insurgents. After a cautious recovery at 2.8 percent growth in 2007, the outlook for Nepal is not predicted to change much – with a real growth rate of only 2.5 percent estimated for 2008, depending upon political conditions and peace. Pakistan The output in Pakistan is estimated to have slowed from 7.8 to 6.6 percent, following a return to more normal agricultural production in the wake of a bumper harvest in 2005. Neither fiscal nor monetary policies turned restrictive in the run up to the 2007 presidential election. As a result, GDP in Pakistan picked up to 7 percent in 2007 bolstered by an expansion in agricultural production and increased capacity following government infrastructure investments and private sector investments in the textile sector. The medium-term outlook for Pakistan remains positive at projected growth of 6.4 percent in 2008, but that calls for a need to maintaining macroeconomic and political stability, and addressing other structural issues. Sri Lanka Sri Lanka’s GDP to expand by an estimated 7 percent in 2006. In Sri Lanka, growth picked up to an estimated 7 percent, owing to a good harvest, post-tsunami recovery, and reconstruction activity (including tourism, despite increased political uncertainty). The growth is projected to be sustained at around 6 percent, supported by a number of post-tsunami infrastructure and reconstruction projects (ports, roads, buildings) and by an expected recovery in tourism. Source: Author’s compilation from The World Bank website, http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/SOUTHASIAEXT/0,,contentMDK:2116 0796~pagePK:146736~piPK:146830~theSitePK:223547,00.html; The World Factbook, 2008, https://www.cia.gov/library/publications/the-world-factbook/ (as of September 4, 2008); and ADB (2008).

Appendices

241

A3 EXPOSITION OF SOUTH ASIA PREFERENTIAL TRADE ARRANGEMENT (SAPTA) In December 1991, the Sixth Summit held in Colombo approved the establishment of an InterGovernmental Group (IGG) to formulate an agreement to establish a SAARC Preferential Arrangement (SAPTA) by 1997. Given the consensus within SAARC, the Agreement on SAPTA was signed in Dhaka on April 11, 1993 and entered into force on December 7, 1995. The SAPTA envisages to establish and promote regional preferential trading arrangement for strengthening intraregional economic cooperation and the development of national economies of the seven member states of the SAARC, namely, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. Objective The objective of the SAPTA is to promote and sustain mutual trade and the economic cooperation amongst the member states through exchange of trade concessions. SAPTA therefore is the first step towards higher levels of trade and economic cooperation in the region. Principles The basic principles underlying SAPTA are: Overall reciprocity and mutuality of advantages so as to benefit equitably all Contracting States, taking into account their respective level of economic and industrial development, the pattern of their external trade, and trade and tariff policies and systems; Negotiation of tariff reform step-by-step, improved and extended in successive stages through periodic reviews; Recognition of the special needs of the Least Developed Contracting States and agreement on concrete preferential measures in their favor; and Inclusion of all products, manufactures and commodities in their raw, semi-processed and processed forms. Components SAPTA has four main components: Tariff Paratariff Nontariff Direct Trade Measures. National Schedules of Concessions The process of negotiation on the schedule of concession, which forms an integral part of the Agreement, commenced in 1993. For this purpose, the Inter-Governmental Group (IGG) on Trade Liberalisation was set up. The IGG met on six occasions in various capitals. At the sixth meeting held in Katmandu on April 20 and 21, 1995, the delegations held intensive rounds of bilateral and multilateral negotiations and agreed on the National Schedule of concessions to be granted by individual member states to other member states under the SAPTA Agreement. The finalization of National Schedules of Concessions represents a modest but encouraging beginning. It is expected that through further rounds of negotiations the schedule of concessions will be expanded and the depth of tariff cut will be deepened further so that SAPTA could become an effective vehicle of regional economic cooperation. So far, four rounds of trade negotiations have been concluded under SAPTA covering over 6,000 commodities. Under the first Round of Negotiations tariff concessions were offered to 226 commodities in all countries. The second Round of Negotiations resulted in exchange of tariff concessions for 1,868 products reflecting an increase by almost ten-fold over first round. The Third and Fourth Round of Trade Negotiations under SAPTA resulted in exchange of tariff concessions on 3,456 items and 1,291 items, respectively. Each Round contributed to an incremental trend in the product coverage and the deepening of tariff concessions over previous Rounds. First Round of Negotiations Concluded in December 1995 following six rounds of IGG meetings. Product by product method of negotiations. Extended tariff cuts varied from 10-90%. Total of 226 items granted tariff concessions by all members.

242

Appendices

Second Round of Negotiations Concluded in December 1996 following four IGG meetings. Product by product method of negotiations. Extended tariff cuts varied from 10-90%. Total of 1,868 items granted tariff concessions by all members. Third Round of Negotiations Commenced in July 1997 with the 1st IGG Meeting (2nd IGG – January 1998). Product by product basis, sectoral basis, chapter and across the board basis. Identification of negative/sensitive products. Removal of structural impediments to trade. Successfully concluded at the 3rd IGG meeting held in Kathmandu from 21st-23rd November 1998. No nontariff barriers were applicable for the products on which tariff concessions were exchanged. Total of 3,456 items granted tariff concessions by all members. Fourth Round of Negotiations Bangladesh granted 92 items on November 1, 2002. Bhutan granted 45 items on April 1, 2003. India granted 364 items on December 20, 2004. Maldives granted a total of 91 items on November 1, 2002, out of which 45 were granted to Bangladesh, 7 to Bhutan, 4 to India, 17 to Nepal, 6 to Pakistan and 12 to Sri Lanka. Nepal granted 94 items on November 1, 2002. Pakistan granted 509 items on December 22, 2003. Sri Lanka granted 96 items on March 1, 2004. Total of 1,291 items granted tariff concessions by all members. Source: Author’s compilation. Retrieved June 5, 2007, from http://www.doc.gov.lk/regionaltrade.php?mode=inop&link=sapta, and http://www.saarcsec.org/main.php?t=2.1.5

Appendices

243

A4 REGIONAL TRADE AGREEMENTS NOTIFIED TO THE GATT/WTO Group AFTA

Agreement ASEAN Free Trade Area

ASEAN

Association of South East Asian Nations

BAFTA BANGKOK

Baltic Free-Trade Area Bangkok Agreement

CAN CARICOM

Andean Community Caribbean Community and Common Market

CACM

Central American Common Market Central European Free Trade Agreement

CEFTA

CEMAC

CER CIS

Economic and Monetary Community of Central Africa Closer Trade Relations Trade Agreement Commonwealth of Independent States

COMESA

Common Market for Eastern and Southern Africa

EAC EAEC

East African Cooperation Eurasian Economic Community European Communities

EC

ECO

Economic Cooperation Organization

EEA EFTA

European Economic Area European Free Trade Association Gulf Cooperation Council

GCC GSTP

General System of Trade Preferences among Developing Countries

Members Brunei Darussalam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam Brunei Darussalam, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam Estonia, Latvia, Lithuania Bangladesh, China, India, Republic of Korea, Laos, Sri Lanka Bolivia, Colombia, Ecuador, Peru, Venezuela Antigua & Barbuda, Bahamas, Barbados, Belize, Dominica, Grenada, Guyana, Haiti, Jamaica, Montserrat, Trinidad & Tobago, St. Kitts & Nevis, St. Lucia, St. Vincent & the Grenadines Surinam Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua Albania, Bosnia and Herzegovina, Croatia, Former Yugoslav, Republic of Macedonia (FYROM), Moldova, Montenegro, Serbia and United Nations Interim Administration Mission in Kosovo Cameroon, Central African Republic, Chad, Congo, Equatorial Guinea, Gabon Australia, New Zealand Azerbaijan, Armenia, Belarus, Georgia, Moldova, Kazakhstan, Russian Federation, Ukraine, Uzbekistan, Tajikistan, Kyrgyz Republic Angola, Burundi, Comoros, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Madagascar, Malawi, Mauritius, Namibia, Rwanda, Seychelles, Sudan, Swaziland, Uganda, Zambia, Zimbabwe Kenya, Tanzania, Uganda Belarus, Kazakhstan, Kyrgyz Republic, Russian Federation, Tajikistan Austria, Belgium, Bulgaria, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia Lithuania, Luxembourg, Malta, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, The Netherlands, United Kingdom Afghanistan, Azerbaijan, Iran, Kazakhstan, Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, Uzbekistan EC, Iceland, Liechtenstein, Norway Iceland, Liechtenstein, Norway, Switzerland Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates Algeria, Argentina, Bangladesh, Benin, Bolivia, Brazil, Cameroon, Chile, Colombia, Cuba, Democratic People's Republic of Korea, Ecuador, Egypt, Ghana, Guinea Guyana, India, Indonesia, Islamic Republic of Iran, Iraq, Libya, Malaysia, Mexico, Morocco, Mozambique, Myanmar, Nicaragua, Nigeria, Pakistan, Peru, Philippines, Republic of Korea, Romania, Singapore, Sri Lanka, Sudan, Thailand, Trinidad and Tobago, Tunisia,

244

Appendices

LAIA

Latin American Integration Association

MERCOSUR MSG

Southern Common Market Melanesian Spearhead Group North American Free Trade Agreement Overseas Countries and Territories

NAFTA OCT

PAN-ARAB

Pan-Arab Free Trade Area

PATCRA

Agreement on Trade and Commercial Relations between the Government of Australia and the Government of Papua New Guinea Protocol relating to Trade Negotiations among Developing Countries

PTN

SACU SADC

SAPTA SPARTECA

Southern African Customs Union Southern African Development Community South Asian Preferential Trade Arrangement South Pacific Regional Trade and Economic Cooperation Agreement

United Republic of Tanzania, Venezuela, Vietnam, Yugoslavia, Zimbabwe Argentina, Bolivia, Brazil, Chile, Colombia, Cuba, Ecuador, Mexico, Paraguay, Peru, Uruguay, Venezuela Argentina, Brazil, Paraguay, Uruguay Fiji, Papua New Guinea, Solomon Islands, Vanuatu Canada, Mexico, United States Greenland, New Caledonia, French Polynesia, French Southern and Antarctic Territories, Wallis and Futuna Islands, Mayotte, Saint Pierre and Miquelon, Aruba, Netherlands, Antilles Anguilla, Cayman Islands, Falkland Islands, South Georgia and South Sandwich Islands, Montserrat, Pitcairn, Saint Helena, Ascension Island, Tristan da Cunha, Turks and Caicos Islands, British Antarctic Territory, British Indian Ocean Territory, British Virgin Islands Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria, Tunisia ,United Arab Emirates, Yemen Australia, Papua New Guinea

Bangladesh, Brazil, Chile, Egypt, Israel, Mexico, Pakistan, Paraguay, Peru, Philippines, Republic of Korea, Romania, Tunisia, Turkey, Uruguay, Yugoslavia Botswana, Lesotho, Namibia, South Africa, Swaziland Angola, Botswana, Lesotho, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia, Zimbabwe Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Australia, New Zealand, Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia, Nauru, Niue, Papua New Guinea, Solomon Islands, Tonga, Tuvalu, Vanuatu, Western Samoa Brunei Darussalam, Chile, New Zealand, Singapore

Trans-Pacific Trans-Pacific Strategic SEP Economic Partnership TRIPARTITE Tripartite Agreement Egypt, India, Yugoslavia UEMOA West African Economic and Benin, Burkina Faso, Côte d'Ivoire, Guinea Bissau, WAEMU Monetary Union Mali, Niger, Senegal, Togo Source: WTO website. Retrieved October 26, 2007 from http://www.wto.org/english/tratop_e/region_e/region_areagroup_e.htm

Appendices

245

A5 TRADE, INVESTMENT, AND ECONOMIC COOPERATION AGREEMENTS TO WHICH SAFTA MEMBERS ARE PARTIES

√

√ √

√

√

Coverage

√

China, S. Korea, Laos

Goods

√

Myanmar, Thailand (Bangladesh, Bhutan and Nepal belong to the BIMSTEC Forum but are not signatories to the FTA)

Goods

√

√

BITS—Nepal

BITS—Pakistan

√

BITS—Sri Lanka

Includes

16 separate treaties with Investment Belgium, France, Germany, Netherlands, Japan, United Kingdom, United States, Italy, Indonesia, South Korea, Thailand, Switzerland, Philippines, Iran, Uzbekistan

√

BITS—India

Sri Lanka

Bilateral Investment Treaties (BITS)—Bangladesh

Nepal

√

Pakistan

Bay of Bengal Initiative for Multisectoral Technical and Economic Cooperation (BIMSTEC)

Maldives

√

India

Bangkok Agreement

Bhutan

Bangladesh

Agreement

Investment

4 separate treaties with Mauritius, United Kingdom, France, Germany

Investment

√

√

Separate treaties with 33 countries

√

√

Individual treaties with India and Pakistan; additional treaties with 22 countries Afghanistan, Azerbaijan, Iran, Goods Kazakhstan, Kyrgyz Republic, Pakistan, Tajikistan, Turkey, Turkmenistan, Uzbekistan

√

Economic Cooperation Organization (Preferential Agreement)

22 separate treaties with Egypt, France, Germany, Ghana, Switzerland, Indonesia, Italy, Korea, Netherlands, Kazakhstan, Oman, Portugal Spain, Sweden, Thailand, United Kingdom, Czech Republic, Australia, Austria, Belgium, Croatia, Denmark

EU–India Agreement on Sugarcane

√

European Union

Sugar quotas and guaranteed prices

EU–India Cooperation Agreement

√

EU

40 other countries

Broad commitments to MFN, maintenance of GSP preferences, reciprocity and non-discrimination in investment, "cooperation" in industrial, agricultural, and services sectors. Goods

Afghanistan

Goods

General System of Trade Preferences among Developing Countries

√

√

India–Afghanistan Preferential Trade Agreement

√

India–ASEAN Framework Agreement on Comprehensive Economic Cooperation

√

√

√

Framework for negotiations for an agreement to include goods, services, investment, and standards. "Early harvest scheme" provides for tariff reductions on goods while negotiations

246

Appendices

India–Chile Framework Agreement

√

Chile

India–GCC Framework Agreement

√

Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates

India–MERCOSUR Preferential Trade Agreement

√

Brazil, Argentina, Uruguay, Goods Paraguay

India–SACU Framework Agreement

√

South Africa, Lesotho, Swaziland, Namibia, Botswana

Provides for negotiations towards a preferential or free trade agreement

India–Singapore Comprehensive Economic Cooperation Agreement

√

Singapore

India–Sri Lanka Free Trade Agreement

√

Goods, services (including movement of natural persons), investment, standards Goods

India–Thailand FTA Framework Agreement

√

Thailand

Framework for negotiations for an agreement to include goods, services, investment, and standards. "Early harvest scheme" provides for tariff Goods (precursor to planned Pakistan-Malaysia Free Trade Agreement)

Pakistan–Malaysia Early Harvest Program Protocol relating to Trade Negotiations among Developing Countries (PTN)

√

United States–Pakistan Trade and Investment Framework Agreement United States–Sri Lanka Trade and Investment Framework Agreement

√

Malaysia

√

Brazil, Chile, Egypt, Israel, Goods Mexico, Paraguay, Peru, Philippines, Republic of Korea, Romania, Tunisia, Turkey, Uruguay, Yugoslavia (1971 borders)

√

Sri Lanka–Pakistan FTA Tripartite Agreement

√

Creates negotiating mechanism for preferential trade agreement; commits countries to "cooperate" in areas of trade in goods, trade in services Broad mandate for economic cooperation and formal commitment to explore feasibility of an India–GCC FTA

√

√

Goods Egypt, [former] Yugoslavia (1968 borders)

√

√

Source: Adapted and reproduced from USAID (2005), pp. 227-228.

Goods (Agreement dormant since early 1990s) Sets out "basic principles" of USPakistan trade and investment relationship Affirmations of shared interest in (1) liberalizing and expanding trade and investment, (2) upholding IPR laws, and (3) respecting

Appendices

247

A6 EXPORTS OF SAARC, 1970-2004 Rest of the regional Rest of the world groups US$ million % US$ million % US$ million % 1970 98.7 3.2 826.9 26.8 2,984.5 96.8 1971 101.6 3.3 800.5 25.8 2,997.3 96.7 1972 189.6 5.1 918.0 24.8 3,513.7 94.9 1973 295.5 6.3 1,332.8 28.5 4,375.5 93.7 1974 247.0 4.2 1,816.2 30.7 5,665.2 95.8 1975 293.0 4.7 2,188.5 34.7 6,005.7 95.3 1976 280.4 4.0 2,490.7 35.2 6,795.8 96.0 1977 370.2 4.5 2,677.6 32.3 7,924.4 95.5 1978 440.5 4.6 3,211.6 33.8 9,063.2 95.4 1979 549.9 4.8 3,712.4 32.7 10,819.5 95.2 1980 612.7 4.8 4,401.9 34.2 12,275.0 95.2 1981 566.3 4.9 3,944.4 34.0 11,049.3 95.1 1982 526.8 4.2 4,249.0 33.9 11,993.8 95.8 1983 459.7 3.6 4,689.5 36.6 12,341.6 96.4 1984 614.9 4.6 4,052.2 30.6 12,637.8 95.4 1985 600.8 4.5 3,994.2 29.8 12,824.4 95.5 1986 553.6 3.8 4,209.6 28.6 14,175.0 96.2 1987 614.8 3.5 4,958.0 28.2 16,940.7 96.5 1988 786.7 3.8 5,868.5 28.3 19,933.8 96.2 1989 862.3 3.7 7,349.8 31.2 22,709.8 96.3 1990 863.0 3.2 7,111.1 26.1 26,366.5 96.8 1991 1,013.1 3.6 8,747.5 30.9 27,340.1 96.4 1992 1,238.7 3.9 9,202.9 29.0 30,458.7 96.1 1993 1,191.5 3.6 10,443.6 31.4 32,037.3 96.4 1994 1,433.5 3.8 11,824.3 31.3 36,342.3 96.2 1995 2,023.7 4.4 14,146.7 30.9 43,808.0 95.6 1996 2,144.4 4.3 15,984.0 32.3 47,284.9 95.7 1997 2,173.9 4.2 15,913.1 30.6 49,801.7 95.8 1998 2,466.3 4.8 14,332.7 28.0 48,789.2 95.2 1999 2,180.0 4.0 15,521.1 28.7 51,960.3 96.0 2000 2,593.4 4.1 18,414.1 29.1 60,701.2 95.9 2001 2,826.7 4.3 19,715.9 30.1 62,771.4 95.7 2002 2,998.0 4.2 21,956.0 30.7 68,492.4 95.8 2003 4,773.3 5.6 29,620.8 34.9 79,994.1 94.4 2004 5,919.4 5.3 40,535.7 36.6 104,965.3 94.7 Avg 1,283.0 4.3 9,176.0 30.9 27,946.8 95.7 Source: Author’s compilation from UNCTAD Handbook of Statistics 2005. Year

Intra-SAARC

Total trade US$ million 3,083.2 3,098.9 3,703.3 4,671.0 5,912.2 6,298.7 7,076.2 8,294.6 9,503.7 11,369.3 12,887.7 11,615.6 12,520.6 12,801.2 13,252.7 13,425.2 14,728.6 17,555.4 20,720.5 23,572.0 27,229.4 28,353.3 31,697.4 33,228.7 37,775.8 45,831.6 49,429.4 51,975.6 51,255.5 54,140.3 63,294.6 65,598.1 71,490.4 84,767.4 110,884.7 29,229.8

248

Appendices

A7 TARIFFS AND IMPORTS BY PRODUCT GROUPS (SAARC) BANGLADESH Product Groups AVG

Animal products Dairy products Fruit, vegetables, plants Coffee, tea Cereals & preparations Oilseeds, fats & oils Sugars & confectionery Beverages & tobacco Cotton Other agricultural products Fish & fish products Minerals & metals Petroleum Chemicals Wood, paper , etc. Textiles Clothing Leather, footwear, etc. Non-electrical machinery Electrical machinery Transport equipment Manufactures, n.e.s.

192.6 149.8 191.2 187.5 191.8 186.5 190.6 200.0 200.0 184.0 33.6 31.7 34.3 38.1 37.5 3.0 48.6 26.5 20.1 22.1

Final Bound Duties Duty Free Max in %

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Binding in %

200.0 200.0 200.0 200.0 200.0 200.0 200.0 200.0 200.0 200.0 50.0 50.0 125.0 50.0 50.0 3.0 125.0 50.0 50.0 50.0

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 10.3 1.3 0.0 2.1 5.2 0.7 0.0 0.7 5.9 0.8 9.8 6.0

Final Bound Duties Duty Free in % Max

Binding in %

MFN Applied Duties Duty Free AVG Max in %

21.0 25.0 20.2 22.0 15.2 10.9 25.0 25.0 4.2 11.2 23.4 14.0 17.8 12.1 17.1 20.4 24.4 14.7 7.8 14.5 13.2 14.9

3.8 0.0 4.2 0.0 7.6 31.2 0.0 0.0 30 25.9 4.8 7.2 0.0 5.4 9.1 2.5 0.0 16 11.5 4.4 15.7 6.8

Imports Share in %

Duty Free in %

25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 6.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0 25.0

BHUTAN Product Groups AVG


MFN Applied Duties Duty Free in % AVG Max

30.0 50.0 46.0 38.3 36.1 44.2 30.0 82.5 20.0 33.1 30.0 22.0 19.5 14.0 21.3 25.0 30.0 25.7 10.2 12.1 16.1 19.5

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 2.7 3.8 0.1 0.0 2.1 18.3 13.2 10.7 0.2

Imports Share in %

Duty Free in %

30.0 50.0 50.0 50.0 50.0 100.0 30.0 100.0 50.0 30.0 30.0 30.0 20.0 50.0 50.0 50.0 30.0 50.0 50.0 20.0 30.0 100.0

INDIA Product Groups AVG


105.0 65.0 100.9 133.1 119.4 168.4 124.7 127.5 110.0 104.1 100.7 38.3 39.5 36.5 31.4 42.3 35.2 28.2 26.8 5.8 31.4


0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.1 0.0 0.0 0.0 0.0 7.1 27.4 0.0 20.2

150.0 150.0 150.0 150.0 150.0 300.0 150.0 150.0 150.0 150.0 150.0 55.0 60.0 40.0 261.0 118.0 40.0 40.0 40.0 40.9 40.0

Binding in %

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 13.1 58.8 0.0 89.0 62.1 70.2 54.9 50.7 94.5 93.8 70.5 42.5


33.0 35.0 31.5 56.3 37.3 52.5 48.4 68.9 17.0 27.1 30.0 15.4 14.0 15.0 13.5 20.2 22.4 15.4 14.3 12.3 24.8 13.9

0.0 0.0 0.0 0.0 7.6 0.0 0.0 0.0 0.0 7.7 0.0 0.1 0.0 0.2 2.4 0.0 0.0 0.6 4.2 17.7 0.0 7.5

100.0 60.0 105.0 100.0 160.0 100.0 100.0 182.0 30.0 70.0 30.0 55.0 1.0 100.0 15.0 268.0 103.0 70.0 15.0 15.0 100.0 15.0

Imports Share in %

Duty Free in %

0.0 0.0 1.1 0.1 0.1 2.5 0.3 0.2 0.2 0.5 0.0 33 25.6 8.7 2.3 1.6 0.0 0.6 9.0 7.0 3.9 3.1

0.0 0.0 0.0 0.0 1.6 0.0 0.0 0.0 0.0 3.9 0.0 0.0 0.0 0.0 0.4 0.0 0.0 0.1 27.6 53.8 0.0 32.4

MALDIVES Product Groups

Final Bound Duties

MFN Applied Duties

Imports

Appendices


249

AVG

Duty Free in %

Max

Binding in %

AVG

Duty Free in %

Max

Share in %

Duty Free in %

90.6 30.0 30.0 30.0 31.6 30.0 30.0 198.8 30.0 32.7 30.0 30.0 30.3 30.0 30.0 30.0 31.8 34.1 30.0 134.8 53.6

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 0.0 0.0 0.0 0.0

300.0 30.0 30.0 30.0 300.0 30.0 30.0 300.0 30.0 300.0 30.0 30.0 300.0 30.0 30.0 30.0 300.0 300.0 30.0 300.0 300.0

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 0.0 100.0 100.0 100.0 100.0 98.8 100.0 100.0 199.8 100.0 69.7 99.8

21.3 10.0 16.5 16.4 14.8 14.3 13.1 33.7 15.0 20.5 16.1 22.3 24.4 15.4 16.7 19.5 25.0 26.0 21.0 21.9 42.6 19.7

0.0 0.0 0.0 0.0 1.3 0.0 12.5 0.0 0.0 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

35.0 10.0 25.0 35.0 35.0 25.0 15.0 145.0 15.0 25.0 35.0 100.0 25.0 200.0 100.0 200.0 25.0 100.0 100.0 100.0 100.0 50.0

1.3 1.9 3.7 0.8 3.7 0.6 0.7 2.4 0.0 0.3 0.4 15.8 13.5 7.4 9.5 1.7 0.7 0.8 11.2 14.3 4.9 4.3

0.0 0.0 0.0 0.0 24.4 0.0 81.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0


Binding in %


Share in %

NEPAL Product Groups AVG


35.9 45.8 40.9 40.8 46.1 34.6 45.0 87.0 36.0 31.4 21.9 25.3 15.0 21.7 25.0 26.4 29.9 27.5 19.9 20.2 28.1 20.4

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.5 0.0 0.0 0.0 0.0 7.0 24.2 0.0 9.0

60.0 50.0 60.0 50.0 100.0 60.0 60.0 200.0 40.0 50.0 50.0 40.0 15.0 40.0 40.0 40.0 30.0 40.0 30.0 40.0 60.0 40.0

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 99.5 20.0 100.0 100.0 99.8 100.0 100.0 100.0 100.0 99.2 95.2


Binding in %

10.9 14.3 12.9 23.8 16.1 11.2 15.3 55.0 0.0 7.9 10.8 13.6 22.0 13.0 14.6 12.8 24.7 16.6 7.8 14.6 20.2 14.8

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 100.0 4.6 0.0 0.1 0.0 0.5 11.8 0.0 0.0 0.6 0.0 0.0 0.0 0.2

Imports Duty Free in %

25.0 15.0 25.0 40.9 40.9 25.9 25.9 184.9 0.9 25.9 40.9 40.9 40.9 40.9 40.9 40.9 25.9 40.9 25.9 40.9 80.9 80.9

PAKISTAN Product Groups AVG


93.0 100.0 100.0 108.3 102.5 97.2 112.5 100.0 13.0 83.9 65.0 64.7 66.4 57.0 7.9 23.3 25.0 66.4 61.1 64.1 61.6 64.7

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 0.0 0.0 0.0 0.0 0.0

100.0 100.0 200.0 150.0 150.0 100.0 150.0 100.0 25.0 100.0 100.0 75.0 75.0 100.0 75.0 75.0 75.0 75.0 75.0 75.0 77.0 75.0

93.5 100.0 100.0 100.0 100.0 100.0 100.0 63.0 100.0 99.3 100.0 100.0 100.0 99.9 100.0 100.0 100.0 100.0 99.6 100.0 68.3 99.8


Binding in %


Imports Share in %

Duty Free in %

25.0 25.0 119.0 25.0 25.0 41.0 25.0 100.0 10.0 25.0 20.0 35.0 28.0 72.0 25.0 35.0 25.0 35.0 35.0 35.0 90.0 35.0

0.0 0.1 1.3 1.0 1.0 5.0 1.8 0.1 1.9 0.6 0.0 15.6 19.9 13.9 1.8 3.1 0.1 1.5 12.4 9.7 7.0 2.2

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 0.0 0.0 0.0 0.0 0.0


Share in %

Duty Free in %

0.1 1.6

0.0 0.0

14.5 25.0 15.4 12.1 15.8 14.8 13.3 50.3 8.0 8.7 10.5 13.6 14.1 9.8 17.3 16.4 24.8 16.3 9.7 15.2 28.0 12.8

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 0.0 0.0 0.0 0.0 0.0

SRI LANKA Product Groups AVG

Animal products Dairy products

49.9 48.5

0.0 0.0

50.0 60.0

100.0 100.0

26.2 22.9

0.0 0.0

28.0 28.0

Imports

250

Appendices

Fruit, vegetables, plants Coffee, tea Cereals & preparations Oilseeds, fats & oils Sugars & confectionery Beverages & tobacco Cotton Other agricultural products Fish & fish products Minerals & metals Petroleum Chemicals Wood, paper , etc. Textiles Clothing Leather, footwear, etc. Non-electrical machinery Electrical machinery Transport equipment Manufactures, n.e.s.

50.4 50.0 50.3 49.7 50.0 51.9 50.0 49.6 50.0 48.8 28.3 9.1 30.8 10.0 17.5 50.0 7.7 34.1 18.3 33.9

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 0.0 0.0 0.0

66.0 60.0 60.0 60.0 50.0 251.0 50.0 50.0 50.0 75.0 45.0 60.0 60.0 50.0 18.0 50.0 60.0 60.0 45.0 100.0

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 95.3 6.7 50.0 5.4 14.5 95.3 100.0 8.9 14.1 12.1 4.9 18.9

24.0 28.0 23.7 21.4 18.2 58.2 0.0 13.2 15.0 10.6 8.4 5.3 13.8 3.9 14.8 19.3 4.9 12.0 9.9 14.0

0.5 0.0 0.0 0.0 0.0 0.0 100.0 15.5 0.0 5.1 46.0 7.5 7.4 77.4 1.5 0.0 0.0 0.0 0.0 0.0

60.0 50.0 28.0 28.0 28.0 250.0 0.0 28.0 28.0 28.0 28.0 42.0 28.0 33.0 15.0 63.0 28.0 28.0 28.0 28.0

1.4 0.3 3.9 1.4 1.6 0.8 0.0 0.6 0.8 16.7 13.6 8.3 3.5 19.8 1.2 1.5 7.3 6.0 7.0 2.7

2.5 0.0 0.0 0.0 0.0 0.0 100.0 4.0 0.0 13.0 82.5 21.1 21.8 97.1 41.8 0.0 0.0 0.0 0.0 0.0

Source: WTO website. Retrieved November 18, 2007 from http://www.wto.org/english/thewto_e/whatis_e/tif_e/org6_e.htm

A8 DESCRIPTION OF DUMMY VARIABLES

Countries that share common Border: Bangladesh, Bhutan, India, Nepal and Pakistan. Countries that share common Language: Bangladesh, India, and Pakistan. Countries that share common Currency: Bhutan and India. Countries that have Trade Agreement: (1) Bangladesh with Bhutan, India, Nepal; (2) Bhutan with Bangladesh and India; (3) India with Bangladesh, Bhutan, Maldives, Nepal, Sri Lanka; (4) Maldives with India; (5) Nepal with Bangladesh, India, Pakistan, Sri Lanka; (6) Pakistan with Nepal and Sri Lanka; and (7) Sri Lanka with India, Nepal and Pakistan. Countries that are in conflict: Bangladesh vs. India, India vs. Pakistan, and Nepal vs. Bhutan; Countries that have Membership in Other Regional Trade Group(s): Bangladesh, Bhutan, India, Pakistan and Sri Lanka Landlocked countries: Bhutan and Nepal. Island countries: Maldives and Sri Lanka. Countries with Seaports: Bangladesh, India, Maldives, Pakistan and Sri Lanka.

Source: Author’s compilation from various sources.

Appendices

251

A9 CONFLICT INTENSITY INDEX State of Violence

Intensity Group

Level of Intensity

Name of Intensity

Definition

A positional difference on definable values of national meaning is considered to be a latent Latent 1 conflict if respective demands are articulated Conflict by one of the parties and perceived by the other as such. NonLow A manifest conflict indicates the use of violent measures that are located in the preliminary stage to violent force. This includes for Manifest 2 Conflict example verbal pressure, threatening explicitly with violence, or the imposition of economic sanctions. A crisis is a tense situation in which at least Medium 3 Crisis one of the parties uses violent force in sporadic incidents. A conflict is considered to be a severe crisis if Severe 4 violent force is repeatedly used in an Crisis organised way. Violent A war is a type of violent conflict in which High violent force is used with a certain continuity in an organised and systemic way. The 5 War conflict parties exercise extensive measures, depending on the situation. The extent of duration is massive and of long duration. Source: Conflictbarometer (2005), Heidelberg Institute for International Conflict Research at the Department of Political Science, University of Heidelberg. Retrieved October 19, 2006 from http://www.hiik.de/en/barometer2005/ConflictBarometer2005.pdf

A10 TESTING FOR ENDOGENEITY AND OVERIDENTIFYING RESTRICTIONS Testing for Endogeneity 1. Estimate y1 = β 0 + β1 y 2 + β 2 z1 + β 3 z 2 + u1 to see if OLS and 2SLS estimates are practically 2.

different. Estimate y 2 = π 0 + π 1 z 2 + π 2 z 2 + π 3 z 3 + π 4 z 4 + ν 2 ; y2 is uncorrelated with u 1 if and only if

3.

v 2 is uncorrelated with u1 ; write u1 = δ 1ν 2 + e1 , where e1 is uncorrelated with v2 and has zero mean. Estimate y1 = β 0 + β1 y 2 + β 2 z1 + β 3 z 2 + δ 1νˆ2 + error using OLS, and test H 0 : δ 1 = 0 using t-statistic. If we reject H 0 at a small significance level, we conclude that y2 is endogenous because v 2 and u 1 are uncorrelated.

Testing for Overidentifying Restrictions 1. Estimate structural equation by 2SLS and obtain 2SLS residuals, uˆ 1 . 2

2

2.

Regress uˆ 1 on all exogenous variables. Obtain R , say R1 .

3.

Under the H 0 that all IVs are uncorrelated with u 1 , nR1 ~ χ q , where q is the number of IVs from 2

a

2

2

outside the model minus the total number of endogenous explanatory variables. If nR1 exceeds (say) the 5% critical value in the χ distribution, we reject H 0 and conclude that at least some of 2 q

the IVs are not exogenous. Note: For detailed explanation on testing for endogeneity and overidentification restrictions, see Wooldridge (2003: 505-509).

252

Appendices

A11 DATA SOURCE DETAILS Variable Descriptor

Data Availability

Source

Bilateral real exports

Variable X

Subscription-based and online

Gross domestic product Population

GDP

Subscription-based

POPN

Subscription-based

Distance

DIST

Free online

Depreciation of real bilateral exchange rate (US GDP deflator, nominal exchange rates, and consumer price indices) Conflict

DREX

Subscription-based

Direction of Trade Statistics (DOTS), IMF; UN Comtrade, United Nations (http://comtrade.un.org/db) World Development Indicators (WDI), World Bank World Development Indicators (WDI), World Bank Great Circle Distance Between Capital Cities, (www.chemicalecology.net/java/capitals.htm) and time and date.com (www.timeanddate.com/worldclock/di stance.html) International Financial Statistics (IFS), IMF

CONF

Free online

BORD, LANG, CURR, LLOCK, ILAND, PORT, INDLIB Source: Author’s compilation. Country-specific variables

Free online

Conflict Barometer 2005, Heidelberg Institute for International Conflict Research, Department of Political Science, University of Heidelberg (www.hiik.de/en/konfliktbarometer/in dex.html) CIA’s The World Factbook (www.cia.gov/library/publications/theworld-factbook/index.html), PerryCastañeda Library Map Collection (www.lib.utexas.edu/maps/world.html) , and relevant papers/websites

Appendices

A12 DEFINITION OF EXOGENOUS VARIABLES Code

Definition regional population pop slack variable in the mktcltrd equation psaveslack price of savings in region r psave world price index of primary factors pfactwld pcgdswld world average price of capital goods (net investment weights) profitslack slack variable in the zero profit equation incomeslack slack variable in the regional income equation endwslack slack variable in endowment market clearing condition cgdslack slack variable in the rorglobal equation tradslack slack variable for the savings price equation ams addition of import-augmenting “technical change” in the Armington nest atm tech change in mode m, worldwide atf tech change shipping of i, worldwide ats tech change shipping from region r atd tech change shipping to s aosec output tech change of sector j, worldwide aoreg output tech change in region r avasec value added tech change of sector j, worldwide avareg value added tech change in region r afcom intermediate tech change of input i, worldwide afsec intermediate tech change of sector j, worldwide afreg intermediate tech change in region r afecom factor input tech change of input i, worldwide afesec factor input tech change of sector j, worldwide afereg factor input tech change in region r aoall output augmenting technical change in sector j of r afall intermediate input i augmenting tech change by j in r afeall primary factor i augmenting tech change sector j in r au input-neutral shift in utility function dppriv private consumption distribution parameter dpgov government consumption distribution parameter dpsave saving distribution parameter to output (or income) tax/output subsidy in region r tp comm.-, source-gen. shift in tax on private cons. tm source-gen. change in tax on imports of i into s qo industry output of commodity i in region r tx dest.-gen. change in subsidy on exports of i from r txs dest.-spec. change in subsidy on exports of i from r to s tms source-spec. change in tax on imports of i from r into s Source: RunGTAP, version 3.40.

253

254

Appendices

A13 IMPORT TAXES BY SOURCE, % AD VALOREM RATE (RTMS): SAARC AS SINGLE ENTITY S/N

Sector

SAARC

CHN

JPN

KOR

USA

EU

ROW


17.6 15.6 50.9 18.8 14.6 5.8 94.9 17.6 15.2 18.4 17.8 38.7 18.5 16.1 11.9 0.0 0.0 0.0 0.0 0.0

20.9 17.1 33.5 22.6 1.9 26.5 97.5 26.5 26.9 27.4 24.3 22.3 27.4 17.2 18.4 0.0 0.0 0.0 0.0 0.0

22.0 15.3 47.3 13.5 0.0 27.1 39.6 27.9 17.6 12.3 25.8 43.6 26.9 15.1 19.4 0.0 0.0 0.0 0.0 0.0

21.1 12.4 32.6 5.6 0.0 17.8 28.3 24.2 20.1 16.2 21.0 41.5 25.3 15.9 19.3 0.0 0.0 0.0 0.0 0.0

16.3 15.7 28.8 8.0 0.0 17.4 98.6 23.8 14.8 27.2 25.5 35.3 32.2 14.5 19.1 0.0 0.0 0.0 0.0 0.0

24.5 7.2 26.7 7.8 2.0 6.8 104.5 31.6 18.4 25.0 26.5 40.5 30.5 15.1 19.8 0.0 0.0 0.0 0.0 0.0

41.7 11.6 28.9 6.6 13.6 15.8 61.6 24.7 18.9 23.1 20.4 36.3 28.6 12.1 20.0 0.0 0.0 0.0 0.0 0.0


13.0 7.1 24.1 9.2 11.8 0.6 50.1 12.1 11.7 9.3 12.1 21.0 9.7 11.6 11.9 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

22.9 9.7 18.0 10.0 13.6 2.8 53.5 16.9 21.9 10.7 12.6 42.3 8.0 10.5 13.0 0.0 0.0 0.0 0.0 0.0

19.1 12.9 13.7 10.0 15.0 2.6 45.4 17.0 20.4 11.7 11.7 47.7 9.4 11.3 12.9 0.0 0.0 0.0 0.0 0.0

79.5 6.9 13.1 2.2 13.8 2.5 55.3 8.9 17.4 14.8 11.4 30.3 5.3 10.2 10.3 0.0 0.0 0.0 0.0 0.0

23.0 12.3 15.4 0.3 6.9 2.0 38.3 14.0 18.2 9.9 11.9 36.8 8.7 10.8 12.1 0.0 0.0 0.0 0.0 0.0

44.2 4.6 16.7 0.2 14.6 0.2 43.4 8.1 21.0 7.8 14.5 32.4 6.7 9.1 12.0 0.0 0.0 0.0 0.0 0.0


5.6 0.1 14.5 0.6 3.5 0.1 30.8 0.1

23.8 4.3 15.7 1.1 4.7 0.0 31.8 0.7

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

10.9 0.6 53.0 3.6 5.4 0.1 23.6 1.7

39.9 4.8 55.7 0.0 5.1 0.0 9.8 1.0

15.8 2.6 63.1 0.2 3.5 0.1 18.2 1.4

26.9 4.3 42.3 0.1 3.1 0.0 20.0 1.4

SAARC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 CHN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 JPN 1 2 3 4 5 6 7 8

Appendices 9 10 11 12 13 14 15 16 17 18 19 20

255


4.8 4.7 1.6 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0

9.4 11.4 0.2 0.0 0.3 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 0.0 0.0

9.5 11.8 3.0 0.0 0.9 0.0 0.2 0.0 0.0 0.0 0.0 0.0

8.4 12.9 1.2 0.0 1.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0

9.7 15.8 1.1 0.0 0.8 0.0 0.1 0.0 0.0 0.0 0.0 0.0

7.2 10.9 0.8 0.0 0.4 0.0 0.1 0.0 0.0 0.0 0.0 0.0


62.9 12.1 48.4 2.5 12.7 1.2 2.3 6.1 8.3 4.4 8.1 8.0 3.9 0.7 6.0 0.0 0.0 0.0 0.0 0.0

166.8 5.9 28.9 4.0 15.4 1.2 59.1 7.1 11.0 8.3 7.0 7.2 4.6 2.5 6.4 0.0 0.0 0.0 0.0 0.0

19.9 5.7 11.5 2.4 16.9 2.6 38.9 6.1 8.9 6.4 7.0 8.0 3.9 1.7 6.4 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

161.6 3.3 36.1 2.0 19.6 1.7 35.1 6.6 9.0 4.6 6.8 7.9 4.0 0.6 3.9 0.0 0.0 0.0 0.0 0.0

35.1 4.3 30.2 3.0 17.1 2.1 24.9 6.4 10.5 7.1 7.3 8.0 4.4 1.2 5.7 0.0 0.0 0.0 0.0 0.0

63.9 3.9 34.0 1.8 12.6 3.9 64.0 4.6 8.7 6.9 5.7 7.8 3.3 0.7 5.3 0.0 0.0 0.0 0.0 0.0


1.0 1.2 1.6 0.0 0.1 0.0 1.0 0.1 10.7 8.0 1.9 0.0 0.5 0.1 0.2 0.0 0.0 0.0 0.0 0.0

3.1 0.3 4.6 0.6 0.5 0.2 2.6 1.3 9.0 14.9 3.6 1.3 2.8 0.4 2.5 0.0 0.0 0.0 0.0 0.0

3.8 0.1 3.1 0.5 0.2 0.1 1.9 1.4 8.3 9.5 2.5 2.4 2.3 0.5 1.4 0.0 0.0 0.0 0.0 0.0

3.9 0.4 10.2 0.2 0.0 0.0 3.3 2.8 13.2 11.1 2.8 2.4 1.9 0.2 1.4 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.9 0.1 12.1 0.2 0.6 0.0 1.5 1.0 9.3 7.4 2.6 2.2 1.8 0.3 0.9 0.0 0.0 0.0 0.0 0.0

2.4 0.1 3.4 0.0 0.1 0.0 1.1 0.2 8.6 9.4 0.9 0.1 0.6 0.1 0.3 0.0 0.0 0.0 0.0 0.0

KOR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 USA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

256

Appendices

EU 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


10.2 2.8 19.4 0.6 2.6 0.0 21.1 0.2 4.5 2.6 0.8 3.0 0.5 0.9 0.3 0.0 0.0 0.0 0.0 0.0

24.5 3.0 13.3 0.4 1.0 0.0 7.5 2.5 10.3 9.1 2.6 0.9 3.3 1.3 0.8 0.0 0.0 0.0 0.0 0.0

9.1 0.3 10.0 0.0 3.0 0.1 9.2 1.7 7.4 5.7 3.7 8.6 3.1 2.2 2.5 0.0 0.0 0.0 0.0 0.0

15.0 0.6 9.2 0.0 0.1 0.0 24.2 3.0 9.3 9.0 4.6 10.3 5.4 1.7 1.8 0.0 0.0 0.0 0.0 0.0

9.7 1.6 26.3 0.5 8.4 0.0 11.3 1.0 7.8 4.7 2.9 6.4 2.5 0.3 1.5 0.0 0.0 0.0 0.0 0.0

1.4 1.8 1.6 0.0 0.3 0.0 1.5 0.3 0.7 0.5 0.3 0.3 0.5 0.1 0.2 0.0 0.0 0.0 0.0 0.0

11.5 0.9 36.2 0.1 2.4 0.0 8.5 0.5 4.0 5.3 1.4 2.3 1.6 0.7 0.9 0.4 0.0 0.0 0.0 0.0


10.3 7.5 5.1 1.9 9.5 1.6 73.9 3.5 13.6 5.9 6.8 14.0 8.9 1.7 7.3 0.0 0.0 0.0 0.0 0.0

8.0 3.2 15.8 3.7 1.4 1.0 17.5 5.6 16.5 5.3 6.1 8.1 5.7 2.3 6.7 0.0 0.0 0.0 0.0 0.0

5.0 0.9 9.3 6.6 0.9 2.0 16.8 6.4 11.2 3.4 5.6 14.8 7.3 1.3 5.3 0.0 0.0 0.0 0.0 0.0

12.4 6.8 19.7 2.9 5.7 1.2 57.9 7.3 15.2 5.1 6.1 19.5 5.7 2.7 10.7 0.0 0.0 0.0 0.0 0.0

8.6 4.3 18.8 0.8 0.7 0.7 43.1 8.1 6.4 4.3 2.8 1.8 1.9 1.4 2.3 0.1 0.0 0.0 0.0 0.0

15.4 7.7 22.1 3.0 2.4 2.3 23.5 5.9 11.9 7.8 5.5 11.8 6.0 3.7 5.1 0.5 0.0 0.0 0.0 0.0

10.3 2.8 12.5 2.4 4.8 1.4 25.0 6.5 13.4 7.9 4.8 11.6 3.7 1.7 4.8 0.2 0.0 0.0 0.0 0.0

ROW 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Note: Highest three import tax rates are highlighted in bold. Source: Author’s computation from GTAP6 database.

Appendices

257

A14 IMPORT TAXES BY SOURCE % AD VALOREM RATE (RTMS): SAARC AS INDIVIDUAL COUNTRIES S/N

Sector

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW


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 0.0 0.0 0.0

12.7 25.2 33.9 11.9 25.0 9.5 37.5 23.4 16.7 24.0 19.7 18.1 16.7 22.2 13.0 0.0 0.0 0.0 0.0 0.0

29.2 12.5 31.0 0.0 0.0 0.0 0.0 34.6 29.7 0.0 19.4 0.0 28.6 0.0 18.7 0.0 0.0 0.0 0.0 0.0

8.0 0.0 13.3 13.3 23.6 11.5 37.5 26.0 32.0 20.8 19.1 22.3 30.5 9.1 11.6 0.0 0.0 0.0 0.0 0.0

10.6 5.1 23.3 8.8 0.0 6.0 37.5 28.7 35.1 34.5 15.5 12.1 23.9 17.3 11.7 0.0 0.0 0.0 0.0 0.0

17.2 25.0 18.2 0.0 0.0 6.0 0.0 30.0 28.8 6.2 17.8 24.7 17.5 12.4 12.0 0.0 0.0 0.0 0.0 0.0

25.1 25.0 20.6 2.0 0.0 23.9 37.5 27.8 33.6 12.5 18.8 19.4 17.9 14.5 8.0 0.0 0.0 0.0 0.0 0.0

4.0 24.0 10.3 5.0 0.0 0.2 37.5 9.3 27.6 37.5 8.9 8.9 19.4 10.8 6.5 0.0 0.0 0.0 0.0 0.0

11.3 12.1 32.8 1.9 2.5 13.5 36.9 22.3 31.7 15.3 11.8 16.0 19.0 12.4 7.9 0.0 0.0 0.0 0.0 0.0

11.4 12.2 31.6 2.8 12.7 27.8 36.5 27.3 29.2 25.6 20.5 16.0 18.5 11.5 12.2 0.0 0.0 0.0 0.0 0.0


18.4 12.3 64.3 0.0 0.2 0.0 35.0 14.1 14.4 18.7 11.5 105.0 33.8 15.4 9.0 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

20.9 27.6 73.9 23.0 0.0 29.7 100.0 24.4 28.1 28.7 27.0 35.0 34.8 14.6 23.0 0.0 0.0 0.0 0.0 0.0

37.2 20.1 58.1 8.2 0.0 1.6 80.9 28.5 21.9 22.2 28.5 35.0 33.0 10.4 25.7 0.0 0.0 0.0 0.0 0.0

35.8 17.9 59.1 25.9 0.0 27.4

37.5 17.8 58.8 0.0 0.0 29.4 59.7 34.0 24.9 26.7 30.0 39.6 34.3 17.0 26.1 0.0 0.0 0.0 0.0 0.0

27.1 11.0 34.2 8.2 0.0 6.4 35.0 27.0 28.1 27.1 31.8 35.3 34.3 16.4 25.3 0.0 0.0 0.0 0.0 0.0

24.0 14.4 56.7 7.8 0.0 20.1 150.8 26.5 28.7 27.7 30.4 36.9 33.9 15.2 21.4 0.0 0.0 0.0 0.0 0.0

41.8 7.3 32.7 10.7 2.3 6.6 33.6 29.1 26.9 33.2 43.7 34.0 17.1 24.1 0.0 0.0 0.0 0.0 0.0

58.6 12.9 59.0 6.7 18.9 16.1 76.2 27.8 24.9 25.6 26.6 41.2 33.3 13.4 25.6 0.0 0.0 0.0 0.0 0.0


4.5 0.0 0.0 0.0 0.0 0.0 0.0 9.3

21.5 13.6 14.3 14.5 8.5 5.4 98.4 8.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

17.4 11.6 10.7 0.0 8.0 1.1 0.0 4.7

4.6 6.5 23.6 0.0 0.0 5.1 90.8 8.6

2.8 9.0 12.1 0.0 0.0 6.3 250.0 10.1

4.2 13.7 19.7 4.0 0.0 4.4 62.9 8.0

20.4 3.5 16.4 0.1 0.7 2.0 30.3 3.4

13.4 10.4 11.0 3.0 6.2 0.3 76.6 7.8

BDG 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 IND 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

117.2

30.7 31.5 30.9 28.7 35.2 33.5 17.9 29.7 0.0 0.0 0.0 0.0 0.0

164.9

LKA 1 2 3 4 5 6 7 8

11.1 6.0 24.6 0.0 0.0 3.2 250.0

12.1

258 9 10 11 12 13 14 15 16 17 18 19 20

Appendices Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

3.1 5.0 5.2 4.5 7.9 2.1 8.2 0.0 0.0 0.0 0.0 0.0

1.8 16.1 6.4 9.6 4.4 1.8 5.3 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

4.0 11.4 3.9 8.5 9.3 0.7 5.5 0.0 0.0 0.0 0.0 0.0

0.9 22.5 8.8 8.4 9.9 6.4 7.3 0.0 0.0 0.0 0.0 0.0

1.4 10.2 9.0 11.5 11.9 1.4 5.2 0.0 0.0 0.0 0.0 0.0

1.6 11.7 8.5 14.0 7.7 6.4 8.0 0.0 0.0 0.0 0.0 0.0

2.2 18.5 6.3 9.9 7.0 2.4 5.1 0.0 0.0 0.0 0.0 0.0

1.0 16.0 7.8 12.1 8.7 2.2 5.1 0.0 0.0 0.0 0.0 0.0

1.6 19.1 7.9 8.6 6.0 2.8 6.1 0.0 0.0 0.0 0.0 0.0


11.9 4.7 21.1 0.0 0.0 0.0 84.1 15.9 25.6 18.1 18.1 111.3 16.5 12.7 14.7 0.0 0.0 0.0 0.0 0.0

20.5 9.7 14.0 85.8 17.3 6.0 131.6 14.8 14.3 19.7 15.4 71.9 13.3 14.1 16.4 0.0 0.0 0.0 0.0 0.0

16.5 9.9 15.4 17.4 20.6 10.1 22.5 22.4 21.0 19.0 13.4 49.6 19.9 14.6 20.9 0.0 0.0 0.0 0.0 0.0

11.2 4.2 21.6 17.5 18.8 9.0 0.0 17.1 15.9 26.3 15.9 58.9 12.5 15.7 8.6 0.0 0.0 0.0 0.0 0.0

13.7 5.5 14.2 8.4 8.3 5.8 0.0 21.1 15.3 26.1 16.5 27.9 16.1 15.6 14.9 0.0 0.0 0.0 0.0 0.0

14.2 14.2 12.4 15.6 0.0 13.8 32.7 18.4 14.7 18.9 14.8 73.4 17.2 13.5 13.7 0.0 0.0 0.0 0.0 0.0

21.4 8.7 16.5 0.0 0.0 5.0 25.6 17.4 21.9 22.8 13.9 68.9 17.3 14.9 15.8 0.0 0.0 0.0 0.0 0.0

15.3 7.1 22.9 16.2 0.0 9.1 51.1 11.5 16.5 16.5 11.1 43.5 19.0 13.2 11.4 0.0 0.0 0.0 0.0 0.0

16.7 7.1 17.2 15.4 0.8 8.3 40.6 15.8 18.6 13.5 16.1 45.4 16.4 14.1 13.3 0.0 0.0 0.0 0.0 0.0

30.2 4.2 16.2 12.8 7.1 15.0 48.4 17.3 18.1 11.4 13.5 38.2 14.7 10.3 14.7 0.0 0.0 0.0 0.0 0.0


10.0 7.9 0.0 0.0 0.0 0.0 45.0 14.9 14.5 5.7 12.8 56.7 14.8 11.2 10.3 0.0 0.0 0.0 0.0 0.0

13.5 7.2 25.7 12.8 11.5 0.6 49.7 12.1 12.3 10.1 12.1 18.9 9.6 9.9 12.1 0.0 0.0 0.0 0.0 0.0

12.3 0.0 0.0 0.0 16.9 0.3 0.0 9.7 18.3 17.9 31.9 20.0 21.3 14.0 15.1 0.0 0.0 0.0 0.0 0.0

10.9 5.1 7.0 0.0 14.9 0.3 57.0 14.1 11.3 9.1 11.9 39.0 15.0 11.2 8.8 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

22.9 9.7 18.0 10.0 13.6 2.8 53.5 16.9 21.9 10.7 12.6 42.3 8.0 10.5 13.0 0.0 0.0 0.0 0.0 0.0

19.1 12.9 13.7 10.0 15.0 2.6 45.4 17.0 20.4 11.7 11.7 47.7 9.4 11.3 12.9 0.0 0.0 0.0 0.0 0.0

79.5 6.9 13.1 2.2 13.8 2.5 55.3 8.9 17.4 14.8 11.4 30.3 5.3 10.2 10.3 0.0 0.0 0.0 0.0 0.0

23.0 12.3 15.4 0.3 6.9 2.0 38.3 14.0 18.2 9.9 11.9 36.8 8.7 10.8 12.1 0.0 0.0 0.0 0.0 0.0

44.2 4.6 16.7 0.2 14.6 0.2 43.4 8.1 21.0 7.8 14.5 32.4 6.7 9.1 12.0 0.0 0.0 0.0 0.0 0.0

RSA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 CHN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Appendices

259

JPN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


3.4 0.0 49.2 0.3 5.4 0.0 0.0 0.0 0.5 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5.0 0.1 15.6 0.8 4.1 0.1 29.7 0.1 5.4 10.2 1.7 0.0 1.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0

4.7 0.0 6.3 1.2 3.5 0.0 5.9 0.4 6.6 8.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

21.7 0.0 6.3 0.0 3.8 0.0 53.1 0.7 3.9 9.9 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

23.8 4.3 15.7 1.1 4.7 0.0 31.8 0.7 9.4 11.4 0.2 0.0 0.3 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

10.9 0.6 53.0 3.6 5.4 0.1 23.6 1.7 9.5 11.8 3.0 0.0 0.9 0.0 0.2 0.0 0.0 0.0 0.0 0.0

39.9 4.8 55.7 0.0 5.1 0.0 9.8 1.0 8.4 12.9 1.2 0.0 1.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0

15.8 2.6 63.1 0.2 3.5 0.1 18.2 1.4 9.7 15.8 1.1 0.0 0.8 0.0 0.1 0.0 0.0 0.0 0.0 0.0

26.9 4.3 42.3 0.1 3.1 0.0 20.0 1.4 7.2 10.9 0.8 0.0 0.4 0.0 0.1 0.0 0.0 0.0 0.0 0.0


5.5 6.9 0.0 0.0 0.0 0.0 0.0 6.2 9.9 2.7 7.8 8.0 1.1 0.0 2.4 0.0 0.0 0.0 0.0 0.0

44.9 12.4 50.3 2.9 14.6 1.2 0.0 5.8 8.1 4.8 8.2 8.0 3.9 0.8 6.0 0.0 0.0 0.0 0.0 0.0

6.4 5.6 0.0 0.0 0.0 2.7 0.0 7.8 8.9 7.6 5.7 7.8 4.9 0.1 7.5 0.0 0.0 0.0 0.0 0.0

270.0

166.8

12.0 22.9 0.0 15.0 0.5 8.0 8.0 8.3 4.9 6.4 0.0 5.6 7.8 7.7 0.0 0.0 0.0 0.0 0.0

5.9 28.9 4.0 15.4 1.2 59.1 7.1 11.0 8.3 7.0 7.2 4.6 2.5 6.4 0.0 0.0 0.0 0.0 0.0

19.9 5.7 11.5 2.4 16.9 2.6 38.9 6.1 8.9 6.4 7.0 8.0 3.9 1.7 6.4 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

161.6 3.3 36.1 2.0 19.6 1.7 35.1 6.6 9.0 4.6 6.8 7.9 4.0 0.6 3.9 0.0 0.0 0.0 0.0 0.0

35.1 4.3 30.2 3.0 17.1 2.1 24.9 6.4 10.5 7.1 7.3 8.0 4.4 1.2 5.7 0.0 0.0 0.0 0.0 0.0

63.9 3.9 34.0 1.8 12.6 3.9 64.0 4.6 8.7 6.9 5.7 7.8 3.3 0.7 5.3 0.0 0.0 0.0 0.0 0.0

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical

0.0 0.0 8.3 0.0 1.8 0.0 0.0 3.9 11.6 9.9 1.6

1.1 1.5 1.3 0.0 0.0 0.0 0.7 0.0 9.4 6.1 1.7

0.8 1.1 0.5 0.2 0.0 0.4 1.1 1.9 12.5 10.7 4.3

2.1 0.1 3.6 0.0 0.0 0.0 6.9 0.9 10.3 5.6 0.5

3.1 0.3 4.6 0.6 0.5 0.2 2.6 1.3 9.0 14.9 3.6

3.8 0.1 3.1 0.5 0.2 0.1 1.9 1.4 8.3 9.5 2.5

3.9 0.4 10.2 0.2 0.0 0.0 3.3 2.8 13.2 11.1 2.8

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.9 0.1 12.1 0.2 0.6 0.0 1.5 1.0 9.3 7.4 2.6

2.4 0.1 3.4 0.0 0.1 0.0 1.1 0.2 8.6 9.4 0.9

KOR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 USA 1 2 3 4 5 6 7 8 9 10 11

260

Appendices

12 13 14 15 16 17 18 19 20

Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

0.9 1.9 0.2 0.9 0.0 0.0 0.0 0.0 0.0

0.0 0.5 0.1 0.2 0.0 0.0 0.0 0.0 0.0

1.3 2.3 0.3 2.8 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

1.3 2.8 0.4 2.5 0.0 0.0 0.0 0.0 0.0

2.4 2.3 0.5 1.4 0.0 0.0 0.0 0.0 0.0

2.4 1.9 0.2 1.4 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

2.2 1.8 0.3 0.9 0.0 0.0 0.0 0.0 0.0

0.1 0.6 0.1 0.3 0.0 0.0 0.0 0.0 0.0


0.3 0.0 0.0 0.0 0.0 0.0 39.4 0.1 0.1 0.0 0.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

12.1 3.8 23.7 0.7 3.0 0.0 21.7 0.1 8.1 2.9 0.8 3.1 0.5 1.0 0.4 0.0 0.0 0.0 0.0 0.0

4.4 0.0 5.2 0.2 3.5 0.0 10.1 0.1 9.2 5.9 1.0 0.2 0.3 0.1 0.6 0.0 0.0 0.0 0.0 0.0

14.3 1.5 4.2 0.0 0.0 0.1 10.2 1.3 0.7 2.3 0.2 0.0 0.2 0.0 0.1 0.0 0.0 0.0 0.0 0.0

24.5 3.0 13.3 0.4 1.0 0.0 7.5 2.5 10.3 9.1 2.6 0.9 3.3 1.3 0.8 0.0 0.0 0.0 0.0 0.0

9.1 0.3 10.0 0.0 3.0 0.1 9.2 1.7 7.4 5.7 3.7 8.6 3.1 2.2 2.5 0.0 0.0 0.0 0.0 0.0

15.0 0.6 9.2 0.0 0.1 0.0 24.2 3.0 9.3 9.0 4.6 10.3 5.4 1.7 1.8 0.0 0.0 0.0 0.0 0.0

9.7 1.6 26.3 0.5 8.4 0.0 11.3 1.0 7.8 4.7 2.9 6.4 2.5 0.3 1.5 0.0 0.0 0.0 0.0 0.0

1.4 1.8 1.6 0.0 0.3 0.0 1.5 0.3 0.7 0.5 0.3 0.3 0.5 0.1 0.2 0.0 0.0 0.0 0.0 0.0

11.5 0.9 36.2 0.1 2.4 0.0 8.5 0.5 4.0 5.3 1.4 2.3 1.6 0.7 0.9 0.4 0.0 0.0 0.0 0.0


10.3 2.4 20.3 2.5 4.7 0.0 58.4 5.2 10.7 3.2 10.1 8.9 4.8 0.9 1.1 0.0 0.0 0.0 0.0 0.0

10.3 8.0 5.1 2.1 11.7 2.1 73.9 3.2 14.2 6.5 6.7 14.1 8.9 1.5 7.9 0.0 0.0 0.0 0.0 0.0

16.4 4.6 22.5 2.4 1.6 3.9 98.1 2.4 20.8 13.2 8.2 4.7 5.2 4.5 2.9 0.0 0.0 0.0 0.0 0.0

5.8 4.9 2.5 0.6 10.4 0.1 48.5 10.1 12.4 6.5 7.3 8.0 8.2 3.3 4.2 0.0 0.0 0.0 0.0 0.0

8.0 3.2 15.8 3.7 1.4 1.0 17.5 5.6 16.5 5.3 6.1 8.1 5.7 2.3 6.7 0.0 0.0 0.0 0.0 0.0

5.0 0.9 9.3 6.6 0.9 2.0 16.8 6.4 11.2 3.4 5.6 14.8 7.3 1.3 5.3 0.0 0.0 0.0 0.0 0.0

12.4 6.8 19.7 2.9 5.7 1.2 57.9 7.3 15.2 5.1 6.1 19.5 5.7 2.7 10.7 0.0 0.0 0.0 0.0 0.0

8.6 4.3 18.8 0.8 0.7 0.7 43.1 8.1 6.4 4.3 2.8 1.8 1.9 1.4 2.3 0.1 0.0 0.0 0.0 0.0

15.4 7.7 22.1 3.0 2.4 2.3 23.5 5.9 11.9 7.8 5.5 11.8 6.0 3.7 5.1 0.5 0.0 0.0 0.0 0.0

10.3 2.8 12.5 2.4 4.8 1.4 25.0 6.5 13.4 7.9 4.8 11.6 3.7 1.7 4.8 0.2 0.0 0.0 0.0 0.0

EU 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ROW 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Note: Highest three import tax rates are highlighted in bold. Source: Author’s computation from GTAP6 database.

Appendices

261

A15 EXPORT SUBSIDIES BY DESTINATION, % AD VALOREM RATE (RTXS): SAARC AS SINGLE ENTITY S/N

Sector

SAARC

CHN

JPN

KOR

USA

EU

ROW


0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 -1.9 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 0.0 0.1 -0.7 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0

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

0.0 0.0 0.1 0.0 0.0 -0.1 0.0 0.0 -3.1 0.2 -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 0.0 -6.2 0.1 -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 0.0 -0.1 0.1 -0.3 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 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 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 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 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 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -25.2 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 0.0 0.0 -23.8 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 0.0 0.0 -0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


0.2 0.0 0.3 0.0 0.0 0.0 0.0 0.0

0.5 0.0 0.3 0.0 0.0 0.0 0.0 0.0

1.2 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

0.8 0.0 0.1 0.0 0.0 0.0 0.0 0.0

0.6 0.0 0.2 0.0 0.0 0.0 0.0 0.0

0.5 0.0 0.2 0.0 0.0 0.0 0.0 0.0

SAARC 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 CHN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 KOR 1 2 3 4 5 6 7 8

262 9 10 11 12 13 14 15 16 17 18 19 20

Appendices Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

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 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 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

-0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

-14.5 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 0.0 0.0 0.0 0.0 0.0 0.0


0.0 0.0 1.4 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

0.0 0.0 0.9 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

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

0.0 0.0 0.4 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

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 0.0 0.0 0.0

0.0 0.0 1.2 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

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 0.0


4.3 0.4 27.5 0.0 0.0 0.0 0.9 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

5.5 0.6 17.0 0.0 0.0 0.0 0.9 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

3.4 0.4 12.1 0.0 0.0 0.0 0.9 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

3.1 0.6 13.5 0.0 0.0 0.0 0.9 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

3.1 0.2 22.9 0.0 0.0 0.0 0.9 0.0 -0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.3 0.0 0.5 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

4.5 0.5 27.1 0.0 0.0 0.0 0.9 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

USA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 EU 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Appendices

263

ROW 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20


0.1 0.0 0.1 0.0 0.0 -0.2 0.0 -0.2 0.0 -0.3 -2.3 -0.2 -0.2 -0.1 -0.6 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.1 0.0 0.0 -0.4 0.0 -0.5 0.0 -0.2 -1.8 0.2 -0.5 0.0 -0.9 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.2 0.0 0.0 -0.3 0.0 -0.4 0.0 -2.2 -0.9 0.1 -0.4 0.0 -0.1 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.1 0.0 0.0 -0.3 0.0 -0.3 -0.2 -1.5 -1.2 0.2 -0.3 0.0 -0.2 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.5 0.0 0.0 -0.2 0.0 -0.1 -0.2 -0.3 -0.8 0.0 -0.2 0.0 -0.1 0.0 0.0 0.0 0.0 0.0

0.2 0.1 2.1 0.0 0.0 -2.8 0.0 -0.3 -3.1 -3.3 -1.9 0.0 -0.4 -0.1 -0.3 0.0 0.0 0.0 0.0 0.0

0.1 0.0 0.2 0.0 0.0 -1.8 0.0 -0.2 0.0 -0.9 -1.3 -0.2 -0.4 -0.1 -0.4 0.0 0.0 0.0 0.0 0.0

Source: Author’s computation from GTAP6 database.

A16 EXPORT SUBSIDIES BY DESTINATION, % AD VALOREM RATE (RTXS): SAARC AS INDIVIDUAL COUNTRIES S/N

Sector

BDG

IND

LKA

RSA

CHN

JPN

KOR

USA

EU

ROW


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 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 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 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 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 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 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 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 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -7.7 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 0.0 0.0 -6.9 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 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

Crops Livestock Diary Forestry Fishing

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 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 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

IND 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 LKA 1 2 3 4 5

264 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Appendices Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

0.0 0.0 0.0 -0.1 0.5 -10.8 1.2 0.0 0.3 -0.2 0.0 0.0 -0.9 0.0 0.0

3.4 0.0 0.0 0.0 0.5 -11.5 1.2 0.0 0.3 -0.2 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


0.0 0.0 0.4 0.0 0.0 0.1 0.0 0.0 0.0 0.3 -5.8 0.1 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 -0.2 0.0 0.0 0.0 0.3 -6.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport

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 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 0.0 0.0

1.7 0.0 0.0 -0.1 0.5 1.2 0.0 0.3 -0.2 0.0 0.0 0.0 0.0 0.0

3.9 0.0 0.0 0.0 0.5 -11.2 1.2 0.0 0.3 -0.2 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.2 0.0 0.0 -0.2 0.0 0.0 0.0 0.3 -6.2 0.1 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 0.3 0.0 0.0 0.0 0.3 -4.9 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 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

-11.6

7.0 0.0 0.0 -0.2 0.5 1.2 0.0 0.3 -0.2 0.0 0.0 0.0 0.0 0.0

0.6 0.0 0.0 0.0 0.5 -9.9 1.2 0.0 0.3 -0.2 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.4 0.0 0.0 0.3 0.0 0.0 0.0 0.3 -6.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.3 -5.9 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.4 0.0 0.0 -0.1 0.0 0.0 0.0 0.3 -3.3 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.5 0.0 0.0 -0.2 0.0 0.0 -2.2 0.3 -3.9 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.4 0.0 0.0 -0.1 0.0 0.0 -15.3 0.3 -5.5 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.1 0.0 0.0 -0.2 0.0 0.0 -0.1 0.3 -5.7 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 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 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 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 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -23.8 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 -0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

-10.4

0.0 0.0 0.0 -0.2 0.5 1.2 0.0 0.3 -0.2 0.0 0.0 0.0 0.0 0.0

2.3 0.0 0.0 -0.2 0.5 -11.4 1.2 0.0 0.3 -0.2 0.0 0.0 -0.1 0.0 0.0

2.5 0.0 0.0 -0.2 0.5 -10.2 1.2 0.0 0.3 -0.2 0.0 0.0 0.0 0.0 0.0

-10.4

RSA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 CHN 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

-25.2

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

19 20

Appendices

265

Construction Services

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 0.0

0.0 0.0


0.1 0.0 0.3 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

0.3 0.0 0.3 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

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

0.5 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

0.5 0.0 0.3 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

1.2 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

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 0.0 0.0 0.0

0.8 0.0 0.1 0.0 0.0 0.0 0.0 0.0 -0.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.6 0.0 0.2 0.0 0.0 0.0 0.0 0.0 -14.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.5 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


0.0 0.0 2.2 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

0.0 0.0 1.5 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

0.0 0.0 4.0 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 0.0

0.0 0.0 0.8 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

0.0 0.0 0.9 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

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

0.0 0.0 0.4 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

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 0.0 0.0 0.0

0.0 0.0 1.2 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

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 0.0

Crops Livestock Dairy Forestry Fishing Mining Beverages Manufactures Textiles

3.3 0.7 24.7 0.0 0.0 0.0 0.9 0.0 0.0

2.3 0.4 29.2 0.0 0.0 0.0 0.9 0.0 0.0

3.8 0.2 30.0 0.0 0.0 0.0 0.9 0.0 0.0

7.7 0.6 28.4 0.0 0.0 -0.3 0.7 0.0 0.0

5.5 0.6 17.0 0.0 0.0 0.0 0.9 0.0 0.0

3.4 0.4 12.1 0.0 0.0 0.0 0.9 0.0 0.0

3.1 0.6 13.5 0.0 0.0 0.0 0.9 0.0 0.0

3.1 0.2 22.9 0.0 0.0 0.0 0.9 0.0 -0.9

0.3 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0

4.5 0.5 27.1 0.0 0.0 0.0 0.9 0.0 0.0

KOR 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 USA 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 EU 1 2 3 4 5 6 7 8 9

266

Appendices

10 11 12 13 14 15 16 17 18 19 20

Leather Chemical Automobile Metals Electronics Machinery Utility Trade Transport Construction Services

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 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 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 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 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 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 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


0.3 0.0 0.0 -0.2 0.0 -0.4 0.0 -0.1 0.0 -0.1 -0.6 0.0 -0.3 -0.6 -0.2 0.0 0.0 -0.1 0.0 0.0

0.1 0.0 0.1 0.0 0.0 -0.3 0.0 -0.2 0.0 -0.4 -3.7 -0.2 -0.2 -0.1 -0.4 0.0 0.0 -0.5 0.0 0.0

0.3 0.0 0.0 0.0 0.0 -0.2 0.0 -0.3 0.0 -0.1 -1.6 0.0 -0.1 -0.3 -0.2 0.0 0.0 -0.1 0.0 0.0

0.2 0.0 0.1 -0.2 0.0 -0.6 0.0 -0.4 -0.1 0.0 -4.1 -0.1 -0.1 -0.1 -0.2 0.0 0.0 -0.3 0.0 0.0

0.0 0.0 0.1 -0.2 0.0 -0.8 0.0 -0.5 0.0 -0.2 -1.7 0.2 -0.5 0.0 -0.3 0.0 0.0 -1.9 0.0 0.0

0.0 0.0 0.2 -0.5 0.0 -0.4 0.0 -0.2 0.0 -2.2 -0.7 0.1 -0.4 0.0 -0.1 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.1 -0.3 0.0 -0.4 0.0 -0.2 -0.2 -1.5 -1.4 0.2 -0.3 0.0 -0.1 0.0 0.0 -0.1 0.0 0.0

0.0 0.0 0.5 0.0 0.0 -0.3 0.0 -0.1 -0.2 -0.3 -0.6 0.0 -0.2 0.0 0.0 0.0 0.0 -0.1 0.0 0.0

0.2 0.1 2.1 -0.6 0.0 -3.0 0.0 -0.2 -3.1 -3.3 -1.2 0.0 -0.4 -0.1 -0.2 0.0 0.0 -0.1 0.0 0.0

0.1 0.0 0.2 -0.4 0.0 -1.7 0.0 -0.1 0.0 -0.9 -1.3 -0.2 -0.4 -0.1 -0.3 0.0 0.0 -0.2 0.0 0.0

ROW 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Source: Author’s computation from GTAP6 database.

Appendices

267

A17 PLURILATERAL FTA: AMONG SAARC COUNTRIES S/N 1 2

Sim Code S0 S1a

Simulation Scenarios: Experiments Base run: Shock pfactwld = uniform 10 (numeraire shock), Gragg 2-4-6 All traded commodities – fixed tariffs of 10%: All TRAD_COMM, BDG-IND, …, RSA-LKA, Shock tms target 10%, Gragg 2-4-6 3 S1b All traded commodities – fixed tariffs of 5%: All TRAD_COMM, BDG-IND, …, RSA-LKA, Shock tms target 5%, Gragg 2-4-6 4 S1c All traded commodities – fixed tariffs of 0%: All TRAD_COMM, BDG-IND, …, RSA-LKA, Shock tms target 0%, Gragg 2-4-6 5 S2a 3 sectors with highest import tax – equal tariffs of 10%-10%: BDG-IND (dai bev aut), BDG-LKA (mfs met mac), BDG-RSA (bev tex aut); IND-BDG (liv dai bev), IND-LKA ( crp bev lth), IND-RSA (for bev aut); LKA-BDG (dai mfs tex), LKA-IND (dai aut met), LKA-RSA (bev tex aut); RSA-BDG (bev tex met), RSAIND (dai bev met), RSA-LKA (crp liv lth); Shock tms target 10%-10%, Gragg 24-6 6 S2b 3 sectors with highest import tax – equal tariffs of 5%-5%: BDG-IND (dai bev aut), BDG-LKA (mfs met mac), BDG-RSA (bev tex aut); IND-BDG (liv dai bev), IND-LKA ( crp bev lth), IND-RSA (for bev aut); LKA-BDG (dai mfs tex), LKAIND (dai aut met), LKA-RSA (bev tex aut); RSA-BDG (bev tex met), RSA-IND (dai bev met), RSA-LKA (crp liv lth); Shock tms target 5%-5%, Gragg 2-4-6 7 S2c 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-IND (dai bev aut), BDG-LKA (mfs met mac), BDG-RSA (bev tex aut); IND-BDG (liv dai bev), IND-LKA ( crp bev lth), IND-RSA (for bev aut); LKA-BDG (dai mfs tex), LKAIND (dai aut met), LKA-RSA (bev tex aut); RSA-BDG (bev tex met), RSA-IND (dai bev met), RSA-LKA (crp liv lth); Shock tms target 0%-0%, Gragg 2-4-6 8 S3a1 3 sectors with highest import tax – varying tariffs of 30%-20%: BDG-IND (dai bev aut), BDG-LKA (mfs met mac), BDG-RSA (bev tex aut); IND-BDG (liv dai bev), IND-LKA (crp bev lth), IND-RSA (for bev aut); LKA-BDG (dai mfs tex), LKA-IND (dai bev aut), LKA-RSA (bev mfs aut); RSA-BDG (bev tex met), RSA-IND (crp dai bev), RSA-LKA (crp liv lth); Shock tms target BDG & RSA 30%, IND & LKA 20%, Gragg 2-4-6 9 S3b 3 sectors with highest import tax – varying tariffs of 10%-5%: BDG-IND (dai bev aut), BDG-LKA (mfs met mac), BDG-RSA (bev tex aut); IND-BDG (liv dai bev), IND-LKA (crp bev lth), IND-RSA (for bev aut); LKA-BDG (dai mfs tex), LKA-IND (dai bev aut), LKA-RSA (bev mfs aut); RSA-BDG (bev tex met), RSA-IND (crp dai bev), RSA-LKA (crp liv lth); Shock tms target BDG & RSA 10%, IND & LKA 5%, Gragg 2-4-6 10 S3c 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-IND (dai bev aut), BDG-LKA (mfs met mac), BDG-RSA (bev tex aut); IND-BDG (liv dai bev), IND-LKA (crp bev lth), IND-RSA (for bev aut); LKA-BDG (dai mfs tex), LKA-IND (dai bev aut), LKA-RSA (bev mfs aut); RSA-BDG (bev tex met), RSA-IND (crp dai bev), RSA-LKA (crp liv lth); Shock tms target BDG & RSA 5%, IND & LKA 0%, Gragg 2-4-6 Note: pfactwld=world price index of primary factors, All TRAD_COMM=all trade commodities, tms= source-spec. change in tax on imports of i from r into s, Gragg 2-4-6=Gragg 2-4-6 steps extrapolation (automatic accuracy) solution method, BDG=Bangladesh, IND=India, LKA=Sri Lanka, RSA=Rest of South Asia, dai=dairy, bev=beverages, aut=automobile, mfs=manufactures, met=metals, mac=machinery, tex=textiles, aut=automobile, liv=livestock, crp=crops, lth=leather. Source: Author.

1 From S3a to S3c: India and Sri Lanka provide tariff concessions to LDCs, viz., Bangladesh and RSA.

268

Appendices

A18 BILATERAL FTA: AMONG SAARC COUNTRIES S/N 1 2

Sim Code S0 S1a

3

S1b

4

S1c

5

S2a

6

S2b

7

S2c

8

S3a

9

S3b

10

S3c

11

S4a

12

S4b

13

S4c

14

S5a

15

S5b

16

S5c

17

S6a

18

S6b

19

S6c

20

S7a

21

S7b

22

S7c

23

S8a

24

S8b

25

S8c

26

S9a

27

S9b

28

S9c

29

S10a

30

S10b

31

S10c

Simulation Scenarios: Experiments Base run: Shock pfactwld = uniform 10 (numeraire shock), Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: BDG-IND FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: BDG-IND FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: BDG-IND FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: BDG-LKA FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: BDG-LKA FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0-0%: BDG-LKA FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: BDG-RSA FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: BDG-RSA FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0-0%: BDG-RSA FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: IND-LKA FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: IND-LKA FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: IND-LKA FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: IND-RSA FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: IND-RSA FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: IND-RSA FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: LKA-RSA FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: LKA-RSA FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: LKA-RSA FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: BDG-IND (dai bev aut), IND-BDG (liv dai bev), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: BDG-IND (dai bev aut), IND-BDG (liv dai bev), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-IND (dai bev aut), IND-BDG (liv dai bev), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: BDG-LKA (mfs met mac), LKA-BDG (dai mfs tex), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariff of 5%-5%: BDG-LKA (mfs met mac), LKA-BDG (dai mfs tex), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-LKA (mfs met mac), LKA-BDG (dai mfs tex), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10-10%: BDG-RSA (bev tex aut), RSA-BDG (bev tex met), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: BDG-RSA (bev tex aut), RSA-BDG (bev tex met), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-RSA (bev tex aut), RSA-BDG (bev tex met), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: IND-LKA (crp bev lth), LKA-IND (dai bev aut), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: IND-LKA (crp bev lth), LKA-IND (dai bev aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: IND-LKA (crp

Appendices

269

bev lth), LKA-IND (dai bev aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: IND-RSA (for bev aut), RSA-IND (crp dai bev), Shock tms target 10%-10%, Gragg 2-4-6 33 S11b 3 sectors with highest import tax – equal tariffs of 5%-5%: IND-RSA (for bev aut), RSA-IND (crp dai bev), Shock tms target 5%-5%, Gragg 2-4-6 34 S11c 3 sectors with highest import tax – equal tariffs of 0%-0%: IND-RSA (for bev aut), RSA-IND (crp dai bev), Shock tms target 0%-0%, Gragg 2-4-6 35 S12a 3 sectors with highest import tax – equal tariffs of 10%-10%: LKA-RSA (bev mfs aut), RSA-LKA (crp liv lth), Shock tms target 10%-10%, Gragg 2-4-6 36 S12b 3 sectors with highest import tax – equal tariffs of 5%-5%: LKA-RSA (bev mfs aut), RSA-LKA (crp liv lth), Shock tms target 5%-5%, Gragg 2-4-6 37 S12c 3 sectors with highest import tax – equal tariffs of 0%-0%: LKA-RSA (bev mfs aut), RSA-LKA (crp liv lth), Shock tms target 0%-0%, Gragg 2-4-6 38 S13a 3 sectors with highest import tax – varying tariffs of 30%-20%: BDG-IND (dai bev aut), IND-BDG (liv dai bev), Shock tms target 30%-10%, Gragg 2-4-6 39 S13b 3 sectors with highest import tax – varying tariffs of 10%-5%: BDG-IND (dai bev aut), IND-BDG (liv dai bev), Shock tms target 10%-5%, Gragg 2-4-6 40 S13c 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-IND (dai bev aut), IND-BDG (liv dai bev), Shock tms target 5%-0%, Gragg 2-4-6 41 S14a 3 sectors with highest import tax – varying tariffs of 30%-20%: BDG-LKA (mfs met mac), LKA-BDG (dai mfs tex), Shock tms target 30%-10%, Gragg 2-4-6 42 S14b 3 sectors with highest import tax – varying tariffs of 10%-5%: BDG-LKA (mfs met mac), LKA-BDG (dai mfs tex), Shock tms target 10%-5%, Gragg 2-4-6 43 S14c 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-LKA (mfs met mac), LKA-BDG (dai mfs tex), Shock tms target 5%-0%, Gragg 2-4-6 2 44 S15a 3 sectors with highest import tax – varying tariffs of 20%-15%: BDG-RSA (bev tex aut), RSA-BDG (bev tex met), Shock tms target 20%-15%, Gragg 2-4-6 45 S15b 3 sectors with highest import tax – varying tariffs of 15%-10%: BDG-RSA (bev tex aut), RSA-BDG (bev tex met), Shock tms target 15%-10%, Gragg 2-4-6 46 S15c 3 sectors with highest import tax – varying tariffs of 10%-5%: BDG-RSA (bev tex aut), RSA-BDG (bev tex met), Shock tms target 10%-5%, Gragg 2-4-6 3 47 S16a 3 sectors with highest import tax – varying tariffs of 10%-15%: IND-LKA (crp bev lth), LKA-IND (dai bev aut), Shock tms target 10%-15%, Gragg 2-4-6 48 S16b 3 sectors with highest import tax – varying tariffs of 5%-10%: IND-LKA (crp bev lth), LKA-IND (dai bev aut), Shock tms target 5%-10%, Gragg 2-4-6 49 S16c 3 sectors with highest import tax – varying tariffs of 0%-5%: IND-LKA (crp bev lth), LKA-IND (dai bev aut), Shock tms target 0%-5%, Gragg 2-4-6 50 S17a 3 sectors with highest import tax – varying tariffs of 20%-30%: IND-RSA (for bev aut), RSA-IND (crp dai bev), Shock tms target 10%-30%, Gragg 2-4-6 51 S17b 3 sectors with highest import tax – varying tariffs of 5%-10%: IND-RSA (for bev aut), RSA-IND (crp dai bev), Shock tms target 5%-10%, Gragg 2-4-6 52 S17c 3 sectors with highest import tax – varying tariffs of 0%-5%: IND-RSA (for bev aut), RSA-IND (crp dai bev), Shock tms target 0%-5%, Gragg 2-4-6 4 53 S18a 3 sectors with highest import tax – varying tariffs of 20%-30%: LKA-RSA (bev mfs aut), RSA-LKA (crp liv lth), Shock tms target 20%-30%, Gragg 2-4-6 54 S18b 3 sectors with highest import tax – varying tariffs of 10%-5%: LKA-RSA (bev mfs aut), RSA-LKA (crp liv lth), Shock tms target 10%-5%, Gragg 2-4-6 55 S18c 3 sectors with highest import tax – varying tariffs of 5%-0%: LKA-RSA (bev mfs aut), RSA-LKA (crp liv lth), Shock tms target 5%-0%, Gragg 2-4-6 Note: pfactwld=world price index of primary factors, All TRAD_COMM=all trade commodities, tms= source-spec. change in tax on imports of i from r into s, Gragg 2-4-6=Gragg 2-4-6 steps extrapolation (automatic accuracy) solution method, BDG=Bangladesh, IND=India, LKA=Sri Lanka, RSA=Rest of South Asia, dai=dairy, bev=beverages, aut=automobile, mfs=manufactures, met=metals, mac=machinery, tex=textiles, aut=automobile, liv=livestock, crp=crops, lth=leather. Source: Author. 32

S11a

2 As RSA includes one non-LDC (Pakistan); tariff concession is given to Bangladesh in this experiment. 3 India being larger and stronger economy provides tariff concession to Sri Lanka. 4 Since RSA’s welfare improved in earlier experiments, therefore, Sri Lanka is given a tariff concession by RSA.

270

Appendices

A19 PLURILATERAL FTA: SAARC AS SINGLE ENTITY AND +5 S/N 1 2

Sim Code S0 S1a

3

S1b

4

S1c

5

S2a

6

S2b

7

S2c

8

S3a

9

S3b

Simulation Scenarios: Experiments Base run: Shock pfactwld = uniform 10 (numeraire shock), Gragg 2-4-6 All traded commodities – fixed tariffs of 10%: All TRAD_COMM, SAARCCHN, …, EU-KOR, Shock tms target 10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%: All TRAD_COMM, SAARCCHN, …, EU-KOR, Shock tms target 5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%: All TRAD_COMM, SAARCCHN, …, EU-KOR, Shock tms target 0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: SAARC-CHN (dai bev aut); SAARC-JPN (dai bev); SAARC-KOR (crp dai fsh); SAARC-USA (tex); SAARC-EU (crp dai bev); CHN-SAARC (dai bev lth ); CHN-JPN (crp dai bev); CHN-KOR (crp dai bev); CHN-USA (tex lth che); CHN-EU (crp dai tex); JPN-SAARC (dai bev aut); JPN-CHN (crp bev aut); JPN-KOR (crp fsh bev); JPN-USA (-); JPN-EU (-); KOR-SAARC (dai bev aut); KOR-CHN (bev tex aut); KOR-JPN (dai bev lth); KOR-USA (dai tex lth); KOR-EU (crp bev aut); USASAARC (bev aut met); USA-CHN (crp bev aut); USA-JPN (crp dai lth); USAKOR (crp dai bev); USA-EU (dai bev); EU-SAARC (bev mfs aut); EU-CHN (crp bev aut); EU-JPN (crp dai bev); EU-KOR (crp dai bev); EU-USA (dai); Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: SAARC-CHN (dai bev aut); SAARC-JPN (crp dai bev); SAARC-KOR (crp dai fsh); SAARC-USA (tex lth); SAARC-EU (crp dai bev); CHN-SAARC (dai bev lth ); CHN-JPN (crp dai bev); CHN-KOR (crp dai bev); CHN-USA (tex lth che); CHN-EU (crp dai tex); JPN-SAARC (dai bev aut); JPN-CHN (crp bev aut); JPN-KOR (crp fsh bev); JPN-USA (tex lth); JPN-EU (crp dai bev); KOR-SAARC (dai bev aut); KORCHN (bev tex aut); KOR-JPN (dai bev lth); KOR-USA (dai tex lth); KOR-EU (crp bev aut); USA-SAARC (bev aut met); USA-CHN (crp bev aut); USA-JPN (crp dai lth); USA-KOR (crp dai bev); USA-EU (crp dai bev); EU-SAARC (bev mfs aut); EU-CHN (crp bev aut); EU-JPN (crp dai bev); EU-KOR (crp dai bev); EU-USA (dai tex lth); Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: SAARC-CHN (dai bev aut); SAARC-JPN (crp dai bev); SAARC-KOR (crp dai fsh); SAARC-USA (tex lth che); SAARC-EU (crp dai bev); CHN-SAARC (dai bev lth ); CHN-JPN (crp dai bev); CHN-KOR (crp dai bev); CHN-USA (tex lth che); CHN-EU (crp dai tex); JPN-SAARC (dai bev aut); JPN-CHN (crp bev aut); JPN-KOR (crp fsh bev); JPN-USA (crp tex lth); JPN-EU (crp dai bev); KOR-SAARC (dai bev aut); KOR-CHN (bev tex aut); KOR-JPN (dai bev lth); KOR-USA (dai tex lth); KOREU (crp bev aut); USA-SAARC (bev aut met); USA-CHN (crp bev aut); USAJPN (crp dai lth); USA-KOR (crp dai bev); USA-EU (crp dai bev); EU-SAARC (bev mfs aut); EU-CHN (crp bev aut); EU-JPN (crp dai bev); EU-KOR (crp dai bev); EU-USA (dai tex lth); Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-20% + removal of agricultural subsidies by USA & EU: SAARC-CHN (dai bev aut); SAARC-JPN (bev); SAARC-KOR (crp dai); SAARC-USA (-); SAARC-EU (bev); CHNSAARC (dai bev lth ); CHN-JPN (crp bev); CHN-KOR (crp dai bev); CHN-USA (-); CHN-EU (crp); JPN-SAARC (dai bev aut); JPN-CHN (crp bev aut); JPNKOR (bev); JPN-USA (-); JPN-EU (-); KOR-SAARC (dai aut); KOR-CHN (bev tex aut); KOR-JPN (dai bev); KOR-USA (-); KOR-EU (bev); USA-SAARC (bev aut met); USA-CHN (crp bev aut); USA-JPN (crp dai); USA-KOR (crp dai bev); USA-EU (dai); EU-SAARC (bev mfs aut); EU-CHN (crp bev aut); EU-JPN (crp dai bev); EU-KOR (crp dai bev); EU-USA (-); Shock tms target 30%-10%; USASAARC txs 0% (dai); EU-SAARC txs 0% (crp liv dai); Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%+ removal of agricultural subsidies by USA & EU: SAARC-CHN (dai bev aut); SAARC-JPN (crp dai bev); SAARC-KOR (crp dai fsh); SAARC-USA (tex lth); SAARC-EU (bev); CHN-SAARC (dai bev lth ); CHN-JPN (crp dai bev); CHN-KOR (crp dai bev); CHN-USA (tex lth che); CHN-EU (crp dai tex); JPN-SAARC (dai bev aut); JPN-CHN (crp bev aut); JPN-KOR (crp fsh bev); JPN-USA (tex lth); JPN-EU (crp dai bev); KOR-SAARC (dai bev aut); KOR-CHN (bev tex aut); KOR-JPN (dai bev lth); KOR-USA (dai tex lth); KOR-EU (crp bev aut); USA-SAARC (bev aut met); USA-CHN (crp bev aut); USA-JPN (crp dai lth); USA-KOR (crp dai

Appendices

271

bev); USA-EU (crp dai bev); EU-SAARC (bev mfs aut); EU-CHN (crp bev aut); EU-JPN (crp dai bev); EU-KOR (crp dai bev); EU-USA (dai tex lth); Shock tms target 10%-5%; USA-SAARC txs 0% (dai); EU-SAARC txs 0% (crp liv dai); Gragg 2-4-6 10 S3c 3 sectors with highest import tax – varying tariffs of 5%-0% + removal of agricultural subsidies by USA & EU: SAARC-CHN (dai bev aut); SAARC-JPN (crp dai bev); SAARC-KOR (crp dai fsh); SAARC-USA (tex lth che); SAARCEU (crp dai bev); CHN-SAARC (dai bev lth ); CHN-JPN (crp dai bev); CHNKOR (crp dai bev); CHN-USA (tex lth che); CHN-EU (crp dai tex); JPN-SAARC (dai bev aut); JPN-CHN (crp bev aut); JPN-KOR (crp fsh bev); JPN-USA (crp tex lth); JPN-EU (crp dai bev); KOR-SAARC (dai bev aut); KOR-CHN (bev tex aut); KOR-JPN (dai bev lth); KOR-USA (dai tex lth); KOR-EU (crp bev aut); USASAARC (bev aut met); USA-CHN (crp bev aut); USA-JPN (crp dai lth); USAKOR (crp dai bev); USA-EU (crp dai bev); EU-SAARC (bev mfs aut); EU-CHN (crp bev aut); EU-JPN (crp dai bev); EU-KOR (crp dai bev); EU-USA (dai tex lth); USA-SAARC txs 0% (dai); EU-SAARC txs 0% (crp liv dai); Shock tms target 5%-0%, Gragg 2-4-6 Note: In case if the tariff duties are 10% or below, only two experiments are performed; reducing the tariff rates to 5% and then to 0%, e.g., FTAs with Japan, US and EU. The last set of simulations is the removal of agricultural subsidies by the US and EU. Source: Author.

A20 BILATERAL FTA: SAARC AS SINGLE ENTITY AND +5 S/N 1 2

Sim Code S0 S1a

3

S1b

4

S1c

5

S25

6

S3a

7

S3b

8

S3c

9

S46

10

S57

11

S6a

12

S6b

13

S6c

14

S7a

Simulation Scenarios: Experiments Base run: Shock pfactwld = uniform 10 (numeraire shock), Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: SAARC-CHN FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: SAARC-CHN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: SAARC-CHN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: SAARC-JPN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10-10%: SAARC-KOR FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: SAARC-KOR FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0-0%: SAARC-KOR FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0-0% + removal of agricultural subsidies: SAARC-USA FTA, All TRAD_COMM, Shock tms target 0%-0%, USA subsidies 0% (dai), Gragg 2-4-6 All traded commodities – fixed tariffs of 0-0% + removal of agricultural subsidies: SAARC-EU FTA, All TRAD_COMM, Shock tms target 0%-0%, EU subsidies 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: SAARC-CHN (dai bev aut), CHN-SAARC (dai bev lth), Shock tms target 10%-10%, Gragg 2-46 3 sectors with highest import tax – equal tariffs of 5%-5%: SAARC-CHN (dai bev aut), CHN-SAARC (dai bev lth), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: SAARC-CHN (dai bev aut), CHN-SAARC (dai bev lth), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: SAARC-JPN (crp

5 Japan’s average import tax rates to SAARC is 5.2%, hence only one simulation is performed with 0% tariff rate. 6 The United States’ average import tax rates to SAARC is only 1.8%, hence only one simulation is performed. 7 The EU’s average import tariffs to SAARC is only 4.6%, hence only one simulation is performed.

272

Appendices

15

S6b

16

S8a

17

S8b

18

S8c

19

S9a8

20

S9b

21

S10a

22

S10b

23

S10c

24

S11a

25

S11b

26

S11c

27

S12a

28

S12b

29

S12c

30

S13a

31

S13b

32

S13c

33

S14a

34

S14b

35

S14c

36

S15a

37

S15b

dai bev), JPN-SAARC (dai bev aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: SAARC-JPN (crp dai bev), JPN-SAARC (dai bev aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: SAARC-KOR (crp dai fsh), KOR-SAARC (dai bev aut), Shock tms target 10%-10%, Gragg 2-46 3 sectors with highest import tax – equal tariffs of 5%-5%: SAARC-KOR (crp dai fsh), KOR-SAARC (dai bev aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: SAARC-KOR (crp dai fsh), KOR-SAARC (dai bev aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: SAARC-USA (tex lth), USA-SAARC (bev aut met), Shock tms target 5%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: SAARC-USA (tex lth che), USA-SAARC (bev aut met), Shock tms target 0%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%% + removal of agricultural subsidies: SAARC-EU (crp dai bev), EU-SAARC (bev mfs aut), Shock tms target 10-10%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: SAARC-EU (crp dai bev), EU-SAARC (bev mfs aut), Shock tms target 5%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: SAARC-EU (crp dai bev), EU-SAARC (bev mfs aut), Shock tms target 0%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-20%: SAARC-CHN (dai bev aut), CHN-SAARC (dai bev), Shock tms target 30%-20%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: SAARC-CHN (dai bev aut), CHN_SAARC (dai bev lth), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: SAARC-CHN (dai bev aut), CHN_SAARC (dai bev lth), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30-5%: SAARC-JPN (crp dai bev), JPN-SAARC (dai bev aut), Shock tms target 30%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: SAARC-JPN (crp dai bev), JPN-SAARC (dai bev aut), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: SAARC-JPN (crp dai bev), JPN-SAARC (dai bev aut), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-20%: SAARC-KOR (crp dai), KOR-SAARC (dai aut), Shock tms target 30%-20%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: SAARC-KOR (crp dai fsh), KOR-SAARC (dai bev aut), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: SAARC-KOR (crp dai fsh), KOR-SAARC (dai bev aut), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-5% + removal of agricultural subsidies: SAARC-USA (tex lth), USA-SAARC (bev aut met), Shock tms target 3%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5% + removal of agricultural subsidies: SAARC-USA (tex lth che), USA-SAARC (bev aut met), Shock tms target 10%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: SAARC-USA (tex lth che), USA-SAARC (bev aut met), Shock tms target 5%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-5% + removal of agricultural subsidies: SAARC-EU (crp dai bev), EU-SAARC (bev mfs aut), Shock tms target 30%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5% + removal of agricultural subsidies: SAARC-EU (crp dai bev), EU-SAARC (bev mfs aut), Shock tms target 10%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6

8 Note that in the first experiment in S9a at 5%-5% tariff combination, only textile and leather are included; chemical is excluded because the United States’ import tariff on chemical is 1.9%. Experiment S8b at 0%-0% combination includes all three sectors.

Appendices

38

S15c

S/N 1 2

Sim Code S0 S1a

3

S1b

4

S1c

5

S2a

6

S2b

7

S3a

8

S3b

9

S4a9

10

S4b

11

S5a

12

S5b

13

S6a

14

S6b

15

S6c

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3 sectors with highest import tax – varying tariffs of 5%-0% + removal of agricultural subsidies: SAARC-EU (crp dai bev), EU-SAARC (bev mfs aut), Shock tms target 5%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 Note: pfactwld= world price index of primary factors, All TRAD_COMM=all trade commodities, tms= source-spec. change in tax on imports of i from r into s, Gragg 2-4-6= Gragg 2-4-6 steps extrapolation (automatic accuracy) solution method, BDG=Bangladesh, IND=India, LKA=Sri Lanka, RSA=Rest of South Asia, dai=dairy, bev=beverages, aut=automobile, mfs=manufactures, met=metals, mac=machinery, tex=textiles, aut=automobile, liv=livestock, crp=crops, lth=leather, fsh=fishing, che=chemical. Source: Author.

A21 BILATERAL FTA: SAARC AS INDIVIDUAL COUNTRIES AND +5 Simulation Scenarios: Experiments Base run: Shock pfactwld = uniform 10 (numeraire shock), Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: BDG-CHN FTA, All TRAD_COMM, Shock tms target 10%10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: BDG-CHN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: BDG-CHN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: BDG-JPN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: BDG-JPN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: BDG-KOR FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: BDG-KOR FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: BDG-USA FTA, All TRAD_COMM, Shock tms target 5%-5%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: BDG-USA FTA, All TRAD_COMM, Shock tms target 0%-0%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: BDG-EU FTA, All TRAD_COMM, Shock tms target 5%-5%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: BDG-EU FTA, All TRAD_COMM, Shock tms target 0%-0%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: IND-CHN FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: IND-CHN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: IND-CHN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: IND-JPN FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: IND-JPN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: IND-JPN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: IND-KOR FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: IND-KOR FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: IND-KOR FTA, All

9 In most of the FTAs with SAARC countries, the tariff level for Japan, South Korea, the United States and the EU starts from 5% because these countries have tariff levels (on an average) below 10%, while the SAARC countries in most cases have above 10%. Therefore, another set of experiments are performed successively with varying tariffs.

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TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: IND-USA FTA, All TRAD_COMM, Shock tms target 5%-5%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: IND-USA FTA, All TRAD_COMM, Shock tms target 0%-0%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10% + removal of subsidies: IND-EU FTA, All TRAD_COMM, Shock tms target 10%-10%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: IND-EU FTA, All TRAD_COMM, Shock tms target 5%-5%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: IND-EU FTA, All TRAD_COMM, Shock tms target 0%-0%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: LKA-CHN FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: LKA-CHN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: LKA-CHN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: LKA-JPN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: LKA-JPN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: LKA-KOR FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: LKA-KOR FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: LKA-USA FTA, All TRAD_COMM, Shock tms target 5%-5%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: LKA-USA FTA, All TRAD_COMM, Shock tms target 0%-0%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: LKA-EU FTA, All TRAD_COMM, Shock tms target 5%-5%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: LKA-EU FTA, All TRAD_COMM, Shock tms target 0%-0%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: RSA-CHN FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: RSA-CHN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: RSA-CHN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: RSA-JPN FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: RSA-JPN FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: RSA-JPN FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 10%-10%: RSA-KOR FTA, All TRAD_COMM, Shock tms target 10%-10%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5%: RSA-KOR FTA, All TRAD_COMM, Shock tms target 5%-5%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0%: RSA-KOR FTA, All TRAD_COMM, Shock tms target 0%-0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: RSA-USA FTA, All TRAD_COMM, Shock tms target 5%-5%, USA txs 0%, Gragg 2-4-6

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All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: RSA-USA FTA, All TRAD_COMM, Shock tms target 0%-0%, USA txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 5%-5% + removal of subsidies: RSA-EU FTA, All TRAD_COMM, Shock tms target 5%-5%, EU txs 0%, Gragg 2-4-6 All traded commodities – fixed tariffs of 0%-0% + removal of subsidies: RSA-EU FTA, All TRAD_COMM, Shock tms target 0%-0%, EU txs 0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: BDG-CHN (bev mfs aut), CHN-BDG (mfs tex lth), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: BDG-CHN (bev mfs aut), CHN-BDG (mfs tex lth), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-CHN (bev mfs aut), CHN-BDG (mfs tex lth), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: BDG-JPN (crp dai fsh), JPN-BDG (liv mfs tex), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-JPN (crp dai fsh), JPN-BDG (liv mfs tex), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: BDG-KOR (tex che aut), KOR-BDG (crp mfs tex), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: BDG-KOR (tex che aut), KOR-BDG (crp mfs tex), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: BDG-USA (dai tex lth), USA-BDG (liv bev tex), Shock tms target 5%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: BDG-USA (dai tex lth), USA-BDG (liv bev tex), Shock tms target 0%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: BDG-EU (bev), EU-BDG (dai bev tex), Shock tms target 5%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: BDG-EU (crp bev che), EU-BDG (dai bev tex), Shock tms target 0%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: IND-CHN (dai bev aut), CHN-IND (crp dai bev), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: IND-CHN (dai bev aut), CHN-IND (crp dai bev), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: IND-CHN (dai bev aut), CHN-IND (crp dai bev), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: IND-JPN (dai bev lth), JPN-IND (dai bev aut), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: IND-JPN (dai bev lth), JPN-IND (dai bev aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: IND-JPN (dai bev lth), JPN-IND (dai bev aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: IND-KOR (crp dai fsh), KOR-IND (dai aut met), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: IND-KOR (crp dai fsh), KOR-IND (dai aut met), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: IND-KOR (crp dai fsh), KOR-IND (dai aut met), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: IND-USA (dai tex lth), USA-IND (dai bev aut), Shock tms target 5%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: IND-USA (dai tex lth), USA-IND (dai bev aut), Shock

10 Note that in the first combination, only beverages and tobacco products are included, while crops and chemicals are excluded because the EU levies an import tax of 0.3% and 0.8%, respectively. However, in the second combination, all three sectors are included.

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tms target 0%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10% + removal of agricultural subsidies: IND-EU (crp dai bev), EU-IND (crp bev aut), EU txs 0% (crp liv dai), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: IND-EU (crp dai bev), EU-IND (crp bev aut), Shock tms target 5%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: IND-EU (crp dai bev), EU-IND (crp bev aut), Shock tms target 0%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: LKA-CHN (tex che met), CHN-LKA (dai mfs lth), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: LKA-CHN (tex che met), CHN-LKA (dai mfs lth), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: LKA-CHN (tex che met), CHN-LKA (dai mfs lth), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: LKA-JPN (dai tex lth), JPN-LKA (dai bev met), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: LKA-JPN (dai tex lth), JPN-LKA (dai bev met), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: LKA-KOR (mfs lth aut), KOR-LKA (dai lth aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: LKA-KOR (mfs lth aut), KOR-LKA (dai lth aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: LKA-USA (tex lth), USA-LKA (dai lth aut), Shock tms target 5%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: LKA-USA (tex lth che), USA-LKA (dai lth aut), Shock tms target 0%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: LKA-EU (dai bev tex), EU-LKA (crp liv bev), Shock tms target 5%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: LKA-EU (dai bev tex), EU-LKA (crp liv bev), Shock tms target 0%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: RSA-CHN (fsh bev aut), CHN-RSA (mfs lth aut), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: RSA-CHN (fsh bev aut), CHN-RSA (mfs lth aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: RSA-CHN (fsh bev aut), CHN-RSA (mfs lth aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: RSA-JPN (crp bev lth), JPN-RSA (dai lth aut), Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: RSA-JPN (crp bev lth), JPN-RSA (dai lth aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 10%-10%: RSA-KOR (crp dai fsh), KOR-RSA (bev lth aut), Shock tms target 10%-10%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5%: RSA-KOR (crp dai fsh), KOR-RSA (bev lth aut),Shock tms target 5%-5%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0%: RSA-KOR (crp dai fsh), KOR-RSA (bev lth aut), Shock tms target 0%-0%, Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of agricultural subsidies: RSA-USA (bev tex lth), USA-RSA (dai bev aut), Shock tms target 5%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: RSA-USA (bev tex lth), USA-RSA (dai bev aut), Shock tms target 0%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 5%-5% + removal of

11 Note that chemical is excluded in the first combination because the EU levies an import tax of only 4.3%, but it is included in the second combination along with crops and beverages.

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agricultural subsidies: RSA-EU (crp bev), EU-RSA (bev tex aut), Shock tms target 5%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – equal tariffs of 0%-0% + removal of agricultural subsidies: RSA-EU (crp dai bev), EU-RSA (bev tex aut), Shock tms target 0%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-20%: BDG-CHN (bev aut), CHN-BDG (tex lth), Shock tms target 30%-20%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: BDG-CHN (bev mfs aut), CHN-BDG (mfs tex lth), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-CHN (bev mfs aut), CHN-BDG (mfs tex lth), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-5%: BDG-JPN (dai fsh), JPN-BDG (liv mfs tex), Shock tms target 20%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-JPN (crp dai fsh), JPN-BDG (liv mfs tex), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-5%: BDG-KOR (tex che aut), KOR-BDG (crp mfs tex), Shock tms target 20%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-KOR (tex che aut), KOR-BDG (crp mfs tex), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-5% + removal of agricultural subsidies: BDG-USA (dai tex lth), USA-BDG (liv bev tex), Shock tms target 20%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: BDG-USA (dai tex lth), USA-BDG (liv bev tex), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-5% + removal of agricultural subsidies: BDG-EU (bev), EU-BDG (dai bev tex), Shock tms target 30%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0% + removal of agricultural subsidies: BDG-EU (crp bev che), EU-BDG (dai bev tex), Shock tms target 5%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-10%: IND-CHN (dai bev aut), CHN-IND (crp dai bev), Shock tms target 20%-10%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-5%: IND-CHN (dai bev aut), CHN- IND (crp dai bev), Shock tms target 15%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-0%: IND-CHN (dai bev aut), CHN- IND (crp dai bev), Shock tms target 10%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-10%: IND-JPN (dai bev lth), JPN-IND (dai bev aut), Shock tms target 20%-10%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-5%: IND-JPN (dai bev lth), JPN-IND (dai bev aut), Shock tms target 15%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-0%: IND-JPN (dai bev lth), JPN-IND (dai bev aut), Shock tms target 10%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-10%: IND-KOR (crp dai fsh), KOR-IND (dai aut met), Shock tms target 20%-10%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-5%: IND-KOR (crp dai fsh), KOR-IND (dai aut met), Shock tms target 15%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: IND-KOR (crp dai fsh), KOR-IND (dai aut met), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-5% + removal of agricultural subsidies: IND-USA (dai tex lth), USA-IND (dai bev aut), Shock tms target 20%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0: IND-USA (dai tex

12 Note also that in the first experiment, dairy is excluded because the EU levies an import tax of only 4.2%. The second experiment includes dairy along with crops and beverages. 13 Bangladesh’s tariffs to the United States are below 30%; hence, the next maximum tariff of 20% is taken. 14 In order to test the extreme, the tariff rate was lowered to 5% without removal of export subsidies because Bangladesh gains considerably in the case of both fixed and equal tariffs. 15 In the case of IND-CHN FTA and IND-KOR FTA, not many variations in tariff combinations are considered. India’s terms of trade are worse than that of China and South Korea; hence, China and South Korea provide slightly higher tariff concessions to India.

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143

S59a

lth), USA-IND (dai bev aut), Shock tms target 10%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-5% + removal of agricultural subsidies: IND-EU (crp dai bev), EU-IND (crp bev aut), Shock tms target 20%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-0% + removal of agricultural subsidies: IND-EU (crp dai bev), EU-IND (crp bev aut), Shock tms target 10%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0% + removal of agricultural subsidies: IND-EU (crp dai bev), EU-IND (crp bev aut), Shock tms target 5%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-10%: LKA-CHN (tex che met), CHN-LKA (dai mfs lth), Shock tms target 15%-10%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: LKA-CHN (tex che met), CHN-LKA (dai mfs lth), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: LKA-CHN (tex che met), CHN-LKA (dai mfs lth), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-5%: LKA-JPN (dai tex lth), JPN-LKA (bev), Shock tms target 20%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: LKA-JPN (dai tex lth), JPN-LKA (dai bev met), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: LKA-KOR (mfs lth aut), KOR-LKA (dai lth aut), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: LKA-KOR (mfs lth aut), KOR-LKA (dai lth aut), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-5% + removal of agricultural subsidies: LKA-USA (tex lth), USA-LKA (dai lth aut), Shock tms target 15%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-0% + removal of agricultural subsidies: LKA-USA (tex lth che), USA-LKA (dai lth aut), Shock tms target 10%-0%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 15%-5% + removal of agricultural subsidies: LKA-EU (dai bev tex), EU-LKA (crp liv bev), Shock tms target 15%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-0% + removal of agricultural subsidies: LKA-EU (dai bev tex), EU-LKA (crp liv bev), Shock tms target 10%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-10%: RSA-CHN (fsh bev aut), CHN-RSA (mfs lth aut), Shock tms target 20%-10%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: RSA-CHN (fsh bev aut), CHN-RSA (mfs lth aut), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: RSA-CHN (fsh bev aut), CHN-RSA (mfs lth aut), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-5%: RSA-JPN (crp bev lth), JPN-RSA (bev aut), Shock tms target 30%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-0%: RSA-JPN (crp bev lth), JPN-RSA (dai lth aut), Shock tms target 10%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 20%-10%: RSA-KOR (crp dai fsh), KOR-RSA (bev lth aut), Shock tms target 20%-10%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5%: RSA-KOR (crp dai fsh), KOR-RSA (bev lth aut), Shock tms target 10%-5%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 5%-0%: RSA-KOR (crp dai fsh), KOR-RSA (bev lth aut), Shock tms target 5%-0%, Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 30%-5% + removal of agricultural subsidies: RSA-USA (bev tex lth), USA-RSA (bev aut), Shock tms

16 Since India gains considerably in the first experiment, the second experiment is performed with lower tariff of 5% and no removal of subsidies by the United States. 17 China provides slightly larger tariff concession to Sri Lanka. 18 The tariff combination is determined based on the actual (existing) tariffs imposed to partner country. Sri Lanka imposes below 20% to Japan, the United States and the EU; and below 15% to South Korea. 19 RSA levies tariffs below 30% to China.

Appendices

279

target 30%-5%, USA txs 0% (dai), Gragg 2-4-6 3 sectors with highest import tax – varying tariffs of 10%-5% + removal of agricultural subsidies: RSA-USA (bev tex lth), USA-RSA (dai bev aut), Shock tms target 10%-5%, USA txs 0% (dai), Gragg 2-4-6 145 S59c 3 sectors with highest import tax – varying tariffs of 5%-0%: RSA-USA (bev tex lth), USA-RSA (dai bev aut), Shock tms target 5%-0%, Gragg 2-4-6 146 S60a 3 sectors with highest import tax – varying tariffs of 30%-10% + removal of agricultural subsidies: RSA-EU (crp bev), EU-RSA (bev tex aut), Shock tms target 30%-10%, EU txs 0% (crp liv dai), Gragg 2-4-6 147 S60b 3 sectors with highest import tax – varying tariffs of 10%-5% + removal of agricultural subsidies: RSA-EU (crp dai bev), EU-RSA (bev tex aut), Shock tms target 10%-5%, EU txs 0% (crp liv dai), Gragg 2-4-6 148 S60c 3 sectors with highest import tax – varying tariffs of 5%-0% + removal of agricultural subsidies: RSA-EU (crp dai bev), EU-RSA (bev tex aut), Shock tms target 5%-0%, EU txs 0% (crp liv dai), Gragg 2-4-6 Note: pfactwld=world price index of primary factors, All TRAD_COMM=all trade commodities, tms= source-spec. change in tax on imports of i from r into s, Gragg 2-4-6=Gragg 2-4-6 steps extrapolation (automatic accuracy) solution method, dai=dairy, bev=beverages, aut=automobile, mfs=manufactures, met=metals, mac=machinery, tex=textiles, aut=automobile, liv=livestock, crp=crops, lth=leather. Source: Author. 144

S59b

A22 RESULTS OF EV FOR PLURILATERAL FTA: AMONG SAARC COUNTRIES EV BDG IND LKA RSA CHN JPN KOR USA EU ROW

S0

S1a

S1b

S1c

S2a

S2b

S2c

S3a

S3b

S3c

0.00 0.00 0.00 0.00 0.02 -0.03 0.01 0.08 -0.04 0.00

-36.90 126.86 28.59 262.04 -1.72 -26.83 -13.96 -57.10 -40.94 -64.90

-104.06 235.41 57.89 352.85 -7.70 -44.37 -24.57 -85.24 -77.60 -140.87

-232.48 345.34 82.84 448.54 -15.77 -66.95 -37.80 -120.17 -124.50 -234.85

-14.21 75.61 19.38 169.60 0.22 -17.92 -9.63 -35.55 -21.61 -26.92

-36.08 55.18 27.86 222.69 -0.67 -23.03 -12.55 -47.86 -29.47 -36.43

-74.25 10.93 37.68 290.15 -1.91 -29.52 -16.17 -63.43 -39.67 -47.94

1.47 61.54 -0.18 78.31 3.10 -6.11 -3.51 -13.71 -2.39 -5.93

-13.88 53.94 -3.94 177.52 0.49 -13.72 -9.12 -31.45 -5.68 -23.34

-35.52 26.34 -11.36 225.73 -0.83 -16.64 -11.47 -38.72 -6.33 -30.27

Source: Author’s simulation.

A23 RESULTS OF EV FOR BILATERAL FTA: AMONG SAARC COUNTRIES EV

S0

S1a

S1b

S1c

S2a

S2b

S2c

S3a

S3b

S3c

BDG IND LKA RSA CHN JPN KOR USA EU ROW EV

0.00 0.00 0.00 0.00 0.02 -0.03 0.01 0.08 -0.04 0.00 S4a

-28.45 101.51 -0.96 -1.49 -5.42 -5.10 -5.01 0.50 -8.40 -30.62 S4b

-85.14 208.33 -2.04 -3.10 -11.42 -10.58 -9.93 1.92 -17.04 -61.82 S4c

-197.02 348.17 -3.48 -5.24 -19.57 -17.45 -16.23 5.17 -27.61 -100.52 S5a

-0.62 -0.21 3.40 -0.07 -0.01 -0.25 -0.13 -0.65 -0.51 -0.74 S5b

-1.45 -0.36 5.85 -0.11 -0.01 -0.46 -0.24 -1.25 -1.00 -1.22 S5c

-3.49 -0.56 9.26 -0.17 -0.01 -0.76 -0.40 -2.09 -1.67 -1.85 S6a

-11.47 -5.58 -0.25 44.09 -6.76 -2.06 -3.64 -1.59 -1.52 -10.66 S6b

-29.45 -8.57 -0.41 68.41 -9.65 -3.15 -5.35 -2.88 -2.37 -16.63 S6c

-64.66 -12.53 -0.64 101.02 -13.44 -4.59 -7.58 -4.23 -3.32 -24.42 S7a

BDG IND LKA RSA CHN JPN

0.46 -31.53 14.44 -0.24 2.10 1.32

0.17 -5.94 30.25 -1.06 2.29 -1.64

-0.23 24.44 39.41 -2.11 2.47 -5.61

0.08 62.61 -0.91 226.02 7.54 -20.61

-0.29 44.05 -1.51 296.90 9.45 -27.78

-0.77 -8.10 -2.28 371.67 11.57 -37.17

0.12 0.27 13.17 -3.18 0.20 -0.42

0.13 -0.54 26.47 -1.41 0.05 -1.42

0.13 -1.58 41.70 -3.14 -0.18 -2.78

-1.54 5.24 -0.07 -0.14 0.17 0.20

280

Appendices

KOR USA EU ROW EV

2.04 -5.77 1.79 7.24 S7b

0.54 -12.17 -7.02 -6.05 S7c

-1.45 -20.60 -18.71 -23.24 S8a

-7.49 -46.93 -30.98 -30.92 S8b

-9.54 -65.41 -46.79 -54.42 S8c

-12.13 -89.52 -67.61 -84.27 S9a

-0.24 -3.58 -1.90 -0.81 S9b

-1.25 -7.14 -4.74 -5.00 S9c

-2.58 -11.88 -8.56 -10.49 S10a

0.05 0.51 0.58 -3.90 S10b


-3.99 7.21 -0.10 -0.21 0.21 0.30 0.06 0.81 0.84 -5.49 S10c

-7.93 9.44 -0.14 -0.30 0.26 0.42 0.08 1.21 1.16 -7.43 S11a

-0.35 -0.05 1.01 -0.03 -0.06 -0.06 -0.06 -0.21 -0.16 -0.19 S11b

-0.71 -0.08 1.60 -0.04 -0.08 -0.11 -0.09 -0.39 -0.30 -0.29 S11c

-1.44 -0.13 2.44 -0.07 -0.12 -0.19 -0.13 -0.63 -0.49 -0.43 S12a

-12.44 -5.44 -0.25 42.13 -6.83 -1.85 -3.49 -1.06 -1.15 -10.35 S12b

-32.08 -7.96 -0.38 62.93 -9.91 -2.65 -5.05 -1.10 -1.43 -15.15 S12c

-67.03 -11.30 -0.57 91.39 -13.95 -3.69 -7.08 -0.82 -1.63 -21.50 S13a

-0.17 7.54 -0.32 -0.51 0.15 0.56 0.14 -0.75 1.00 -1.34 S13b

-0.28 11.91 -3.78 -0.77 0.19 0.89 0.23 -1.01 1.63 -2.02 S13c


-0.42 17.09 -10.43 -1.08 0.22 1.29 0.33 -1.19 2.42 -2.75 S14a

0.60 71.48 -0.37 101.78 5.34 -12.30 -5.46 -23.88 -7.35 -7.98 S14b

0.80 55.92 -0.42 120.08 6.56 -14.71 -6.33 -29.71 -8.66 -8.63 S14c

1.05 25.39 -0.47 141.70 8.02 -17.46 -7.32 -36.74 -10.15 -9.12 S15a

0.01 -0.41 0.50 2.11 0.21 -0.06 -0.10 -1.07 -0.16 -0.08 S15b

0.01 -0.79 -0.65 4.17 0.35 -0.08 -0.17 -1.82 -0.25 -0.16 S15c

0.00 -1.24 -3.31 6.60 0.53 -0.10 -0.25 -2.75 -0.37 -0.24 S16a

0.74 0.45 0.00 -0.07 0.04 0.00 0.01 -0.07 0.04 -0.26 S16b

-0.89 4.77 -0.06 -0.20 0.18 0.18 0.05 0.39 0.57 -3.82 S16c

-3.00 6.35 -0.09 -0.28 0.24 0.27 0.07 0.63 0.82 -5.37 S17a


-0.15 0.01 -0.06 0.00 -0.01 0.03 0.00 0.07 0.06 0.00 S17b

-0.24 -0.06 1.03 -0.03 -0.05 -0.08 -0.06 -0.27 -0.21 -0.19 S17c

-0.54 -0.09 1.60 -0.04 -0.07 -0.14 -0.09 -0.47 -0.36 -0.29 S18a

1.71 -2.13 -0.09 15.94 -2.72 -0.77 -1.41 -0.64 -0.59 -4.06 S18b

-2.25 -3.55 -0.16 26.94 -4.49 -1.25 -2.32 -0.91 -0.88 -6.75 S18c

-12.08 -5.44 -0.26 42.00 -6.83 -1.87 -3.51 -1.12 -1.19 -10.35

-0.08 3.76 0.99 -0.30 0.10 0.28 0.07 -0.50 0.47 -0.75

-0.17 7.30 -0.12 -0.54 0.16 0.55 0.14 -0.81 0.96 -1.37

-0.28 11.48 -3.47 -0.82 0.20 0.87 0.22 -1.09 1.57 -2.06

0.45 60.17 -0.18 77.56 3.41 -6.13 -3.38 -13.82 -2.65 -5.08

BDG IND LKA RSA CHN JPN KOR USA EU ROW

0.92 42.30 -0.34 132.64 6.57 -12.96 -5.91 -28.84 -6.59 -7.47

1.18 9.56 -0.37 158.67 8.03 -15.54 -6.87 -35.76 -7.82 -7.77

0.00 0.14 -0.26 -1.00 -0.03 0.00 0.03 0.24 0.11 0.09

0.01 -0.41 1.14 1.66 0.23 -0.13 -0.14 -1.29 -0.35 -0.13

0.02 -0.78 0.24 3.38 0.39 -0.17 -0.21 -2.13 -0.51 -0.21


281

Appendices

A24 RESULTS OF EV FOR PLURILATERAL FTA: SAARC AS SINGLE ENTITY AND +5 EV SAARC

CHN JPN KOR USA EU ROW

S0

S1a

S1b

S1c

S2a

S2b

S2c

S3a

S3b

S3c

0.00

-4035.6

-2189.9

-622.4

-40.3

386.7

878.4

-20.87

579.9

1203.2

0.02

-5761.8

1423.3

7735.6

1807.9

4455.1

7556.3

2036.00

4465.6

7571.0

-0.03

-6388.3

2654.7

10724.4

2575.0

2307.9

1768.3

2503.59

2294.2

1754.0

0.01

239.2

4556.7

8490.7

4472.8

5236.0

6202.4

3826.16

5233.7

6200.0

0.06

-7286.1

3247.9

9929.4

5673.0

6365.1

6232.8

3813.06

6287.6

6130.7

-0.03

-6555.9

1002.8

4172.6

668.8

758.2

285.9

35.37

583.2

95.8

-0.02

23599.1

2516.5

-20577.9

-3014.7

-4918.5

-7050.3

-1880.16

-4829.0

-6951.0


A25 RESULTS OF EV FOR BILATERAL FTA: SAARC AS SINGLE ENTITY AND +5 EV SAARC CHN JPN KOR USA EU ROW EV

S0

S1a

S1b

S1c

S2

S3a

S3b

S3c

S4

0.00

-170.74

-402.40

-977.34

-1027.66

-100.96

-257.75

-612.40

696.75

0.02

604.68

990.15

1439.74

-149.69

-56.95

-109.08

-179.60

-336.93

-0.03

11.32

9.20

6.25

1508.93

7.56

8.44

10.79

-176.73

0.01

-24.38

-41.75

-63.43

-82.92

100.76

301.37

546.00

-103.9

0.06

61.76

78.80

101.16

-32.24

-30.74

-45.59

-62.80

1384.76

-0.03

91.92

139.53

207.68

-153.37

-47.18

-92.74

-150.07

-588.45

-0.02

-438.54

-761.16

-1171.53

-624.03

-191.63

-341.56

-538.70

-1518.41

S5

S6a

S6b

S6c

S7a

S7b

S8a

S8b

S8c

SAARC CHN JPN KOR USA EU ROW EV

-1324.98

-1.86

-7.31

-17.47

-40.04

-63.40

127.05

142.19

154.36

-254.82

10.66

14.94

20.03

-18.74

-20.67

5.65

7.11

9.16

-172.31

-0.11

-0.17

-0.25

169.85

169.88

-5.37

-7.29

-9.58

-125.37

-0.71

-0.96

-1.26

-19.97

-23.86

-261.32

-363.92

-506.37

15.5

0.41

0.62

0.92

-1.81

-10.74

-49.36

-61.89

-77.56

3996.87

-1.03

-1.28

-1.43

-19.66

-26.42

-23.93

-30.58

-38.87

-1948.83

-3.41

-5.04

-7.09

-38.64

-53.94

-36.27

-44.94

-55.09

S9a

S9b

S10a

S10b

S10c

S11a

S11b

S11c

S12a

SAARC CHN JPN KOR USA EU ROW EV

875.65

1964.45

-70.57

-224.51

-326.78

0.90

-8.70

-5.28

-40.04

-99.66

-204.36

-20.09

-20.41

-22.60

1.51

15.50

13.65

-18.74

-14.54

-31.34

-24.94

-30.84

-35.59

-0.01

-0.15

-0.19

169.85

-15.58

-29.74

-24.65

-28.78

-32.76

-0.17

-0.96

-0.95

-19.97

-98.13

-632.42

32.54

31.76

34.04

-0.01

0.78

0.38

-1.81

-99.31

-205.32

761.00

943.38

1086.80

-0.19

-1.11

-1.54

-19.66

-387.93

-740.51

-225.48

-280.82

-360.16

-0.24

-4.70

-5.53

-38.64

S12b

S12c

S13a

S13b

S13c

S14a

S14b

S14c

S15a

-2.70

-8.29

98.92

169.69

196.79

976.14

1934.80

2079.54

127.25

SAARC CHN JPN KOR USA EU ROW EV SAARC CHN JPN KOR USA EU ROW

-14.92

-16.29

6.30

9.96

12.78

-92.45

-219.26

-199.84

3.97

135.00

129.94

-0.08

-5.43

-7.55

-1.83

-53.46

-23.51

-5.91

-16.75

-20.50

-161.82

-393.65

-543.65

-10.44

-66.36

-26.58

-4.75

-2.23

-11.41

-31.68

-62.84

-78.86

-281.73

-260.49

-732.95

-10.72

-17.31

-24.31

-12.85

-29.74

-38.11

-70.29

-178.79

-190.81

31.79

-31.86

-46.45

-16.84

-38.74

-47.35

-305.69

-760.22

-702.27

-35.76

S15b

S15c

3.19

-326.78

-14.57

-22.60

-24.55

-35.59

-23.88

-32.76

19.67

34.04

720.13

1086.80

-238.68

-360.16


282

Appendices

A26 RESULTS OF EV FOR BILATERAL FTA: SAARC AS INDIVIDUAL COUNTRIES AND +5 EV

S0

S1a

S1b

S1c

S2a

S2b

S3a

S3b

S4a


0.00 0.00 0.00 0.00 0.02 -0.03 0.01 0.08 -0.04 0.00 S4b

0.66 -28.62 -1.41 -10.17 151.48 -7.70 -21.23 37.06 20.89 -79.45 S5a

-58.82 -40.58 -1.99 -13.12 221.90 -12.88 -29.03 49.65 26.15 -108.87 S5b

-168.38 -54.52 -2.74 -16.42 306.79 -19.23 -37.96 66.04 32.85 -142.38 S6a

-49.00 -15.29 -0.30 -1.62 -12.13 87.54 -8.82 9.22 0.20 -35.67 S6b

-92.34 -24.21 -0.44 -2.45 -20.48 141.07 -14.58 9.91 -4.01 -57.26 S6c

-108.16 -26.93 -1.48 -6.82 -53.49 4.90 196.59 41.06 13.89 -111.54 S7a

-220.62 -39.64 -2.19 -9.43 -76.54 6.69 291.18 59.40 18.20 -161.85 S7b

252.38 2.92 -2.65 -2.07 -17.59 -9.89 -0.70 -92.06 -30.25 -48.71 S7c


569.25 7.02 -4.72 -3.18 -30.68 -25.24 -0.90 -258.30 -78.86 -98.15 S8a

-330.52 -15.81 -0.43 -0.21 -5.36 8.67 -8.14 96.70 233.59 -34.64 S8b

-137.54 -23.71 -0.48 -2.80 -18.12 -8.24 -12.21 22.65 177.28 -71.61 S8c

2.04 -192.77 2.12 -3.92 454.05 15.10 0.67 17.92 64.20 -361.11 S9a

2.37 -391.62 2.68 -5.24 712.15 22.66 0.00 29.91 110.04 -625.10 S9b

3.03 -849.70 3.55 -6.83 1015.62 31.88 -1.37 44.48 173.90 -959.66 S10a

4.24 -438.98 2.97 -5.12 -39.59 583.60 -28.36 -2.74 -30.85 -160.10 S10b

4.15 -613.65 2.86 -7.45 -66.79 887.36 -42.08 -38.07 -84.83 -298.09 S10c

4.18 -958.15 2.79 -10.40 -103.44 1249.33 -58.85 -81.59 -148.48 -479.96 S11a

-1.31

0.22

-870.68 2.45 -43.84 -192.20 -137.20 -82.60 -108.08 3337.97 1500.62

2.00 -4.91 0.58 -11.42 0.96 2.01 -1.44 0.67

BDG

-0.59

-1.27

-2.06

3.35

-11.03

IND LKA RSA CHN JPN KOR USA EU

-143.57 -0.32 -3.63 -41.12 6.84 178.73 -34.48 -51.44

-270.46 -0.77 -5.97 -68.19 6.63 316.23 -56.47 -89.26

-530.44 -1.29 -9.09 -105.28 7.02 482.17 -84.60 -137.38

-965.19 2.04 -15.91 -98.49 -84.60 -47.38 1395.99 -191.32

-646.09 -8.07 -27.92 -177.84 -152.92 -76.99 1765.92 -466.12

34.14 1691.94 18.48 -10.14 -57.63 -13.44 -30.86 213.05 2289.43

18.72 1252.64 11.47 -24.44 -116.83 -68.95 -54.74 69.00 2948.72

ROW EV

-134.31 S11b

-224.69 S11c

-343.85 S12a

-451.47 S12b

-925.10 S13a

-506.51 S13b

-966.90 S14a


0.15 -0.14 3.50 0.10 1.61 -1.09 0.32 -3.23 -2.41 0.82 S15b

0.04 -3.02 5.98 -0.52 18.79 -3.86 -1.98 -5.49 -6.34 -8.61 S16a

-0.03 -1.40 -8.54 0.10 -0.63 4.29 0.16 3.43 2.20 -0.79 S16b

0.07 -4.10 10.09 -0.36 -2.21 14.56 -1.45 -5.19 -7.43 -13.04 S16c

0.15 0.04 0.72 0.27 0.77 -0.18 -0.64 -2.29 -1.69 2.35 S17a

-0.06 -3.33 5.85 -0.55 -3.73 -0.93 20.48 -1.94 -6.10 -13.90 S17b

BDG IND LKA RSA CHN JPN

-2.19 -8.90 170.93 -4.60 -7.18 -14.38

-0.11 -1.90 -0.45 -5.24 44.43 -3.85

-0.26 -4.82 -1.06 9.37 81.91 -11.62

-0.47 -8.67 -1.84 2.53 115.15 -22.32

-0.10 -6.60 -0.12 19.91 -7.67 73.98

-0.22 -8.75 -0.32 19.80 -14.13 119.35

S14b

8.02 S15a

-3.03 1.91 232.90 -3.97 -12.59 -8.49 2.87 -120.59 -42.27 -55.94 S17c

-4.99 2.35 473.90 -7.58 -21.87 -20.78 3.25 -265.88 -94.38 -110.60 S18a

-1.34 -2.14 6.26 -1.13 -5.28 0.48 2.27 0.08 3.71 -3.91 S18b

-0.38 -11.40 -0.59 0.12 -22.96 171.77

0.40 -28.74 -4.79 585.21 39.23 1.68

0.37 -34.40 -6.01 688.82 38.71 -0.63

Appendices

283

KOR USA EU ROW EV

-0.34 -60.45 -44.81 -49.81 S18c

-2.81 -1.08 -3.27 -20.25 S19a

-9.51 -11.08 -13.60 -44.40 S19b

-18.60 -25.02 -27.64 -74.95 S20a

-6.76 2.90 -7.81 -24.40 S20b

-9.20 -4.04 -20.45 -45.59 S21a

-12.30 -12.54 -36.13 -73.12 S21b

-626.87 -191.24 -70.54 -58.99 S21c

-826.24 -217.44 -88.41 -76.07 S22a


0.33 -41.02 -7.39 794.76 36.48 -3.48 -246.81 -110.09 -97.38 S22b

-5.67 -12.57 -3.85 163.07 -25.47 -6.34 -6.54 -14.11 -9.67 -83.42 S23a

-12.21 -27.99 -8.29 437.13 -49.53 -18.44 -14.33 -120.83 -56.84 -189.26 S23b

3.87 -2.82 0.21 -341.78 -8.57 -7.86 -3.29 61.13 312.59 -109.99 S24a

-0.16 -19.96 -1.68 -142.82 -24.26 -23.37 -10.30 12.99 381.47 -222.23 S24b

6.13 -24.41 -1.39 -10.11 128.30 -4.78 -19.37 38.79 22.43 -72.12 S25a

-36.80 -30.67 -1.92 -12.87 167.22 -6.08 -24.35 53.75 30.63 -92.86 S25b

-104.97 -37.20 -2.57 -15.87 211.09 -7.54 -29.55 72.14 40.52 -115.83 S26a

-17.18 -2.97 -0.15 -1.02 -5.55 24.01 -2.85 5.66 2.75 -10.66 S26b


-33.66 -4.37 -0.23 -1.51 -8.23 35.23 -4.21 8.31 4.16 -15.91 S26c

-75.61 -18.89 -1.27 -6.43 -46.59 5.66 155.01 37.81 14.91 -88.07 S27a

-194.91 -252.59 -34.34 -100.18

S27b

290.91 5.05 -2.49 -1.76 -15.03 -10.54 0.33 -110.51 -37.30 -45.48 S27c

588.35 11.44 -4.66 -2.89 -27.17 -21.58 1.64 -298.42 -72.83 -79.25 S28a

-27.27 -2.98 -0.12 -1.09 -6.00 0.07 -2.18 6.91 35.97 -9.99 S28b

-46.22 -4.52 -0.18 -1.60 -8.78 0.16 -3.14 10.43 50.45 -15.95 S28c

0.07 -7.31 0.05 -0.49 8.23 -0.50 -0.81 0.09 2.79 -3.38 S29a

0.10 -14.68 0.06 -0.67 10.39 -0.72 -1.11 0.09 3.86 -4.68 S29b


0.13 -26.79 0.09 -0.92 12.67 -1.02 -1.51 0.09 5.24 -6.37 S30a

0.30 -17.92 0.27 -0.45 -4.87 58.90 -9.02 -3.65 -7.32 -11.32 S30b

0.36 -36.99 0.34 -0.62 -6.71 74.93 -11.14 -4.78 -9.29 -14.52 S30c

0.43 -65.24 0.42 -0.84 -9.00 92.35 -13.42 -6.13 -11.50 -18.23 S31a

-0.93 9.99 -0.73 -1.30 -7.26 0.90 -58.05 -20.93 -23.76 -59.60 S31b

-1.61 54.70 -1.02 -0.46 2.41 -2.10 -185.21 -24.61 -15.04 -29.34 S31c

-1.61 54.70 -1.02 -0.46 2.41 -2.10 -185.21 -24.61 -15.04 -29.34 S32a

-7.55 286.08 -5.17 -6.95 -28.46 -2.43 -6.45 -29.44 -18.70 -92.09 S32b

-19.39 771.80 -13.38 -14.94 -71.06 -0.69 -12.81 -217.50 -43.12 -223.37 S33a


0.50 4.92 0.24 -0.37 -1.54 -11.23 -7.82 0.78 130.79 -30.42 S33b

-0.40 12.60 -0.35 -0.55 0.56 -12.57 -8.74 -2.97 136.48 -49.93 S34a

-1.60 14.32 -1.11 -0.76 3.29 -13.80 -9.52 -7.92 113.47 -73.79 S34b

0.01 -0.03 2.44 -0.09 0.82 -0.35 -0.18 -0.55 -0.80 -0.52 S35a

0.02 -0.11 4.05 -0.15 1.24 -0.64 -0.35 -1.12 -1.65 -1.03 S35b

0.04 -0.21 5.69 -0.23 1.30 -1.04 -0.57 -1.92 -2.79 -1.69 S36a

0.02 -0.26 1.22 -0.01 -0.20 1.03 -0.06 -0.46 -0.43 -0.77 S36b

0.05 -0.47 4.29 -0.02 -0.79 0.82 -0.10 -1.77 -1.45 -1.49 S36c

0.00 -0.06 0.05 -0.01 -0.04 -0.07 0.35 0.00 -0.10 -0.10 S37a

BDG IND LKA RSA CHN

0.00 -0.11 0.02 -0.03 -0.08

-2.94 1.80 246.85 -4.21 -12.25

-5.01 4.04 476.91 -7.25 -20.88

-1.00 -0.56 58.11 -1.57 -2.69

-1.00 -0.56 58.11 -1.57 -2.69

-0.04 -0.43 0.00 -7.78 11.26

-0.05 -0.61 0.00 -15.76 17.13

-0.07 -0.82 0.00 -28.98 24.69

-0.17 -6.89 -0.14 48.67 -7.36

-1073.73

-1463.88

-56.06 5541.39 -108.49 -155.41 -2625.98

284

Appendices

JPN KOR USA EU ROW EV

-0.13 0.67 -0.02 -0.22 -0.21 S37b

-9.78 2.23 -118.90 -49.48 -60.24 S38a

-19.35 4.16 -281.43 -91.18 -102.86 S38b

-3.15 0.70 -21.74 -16.80 -7.98 S38c

-3.15 0.70 -21.74 -16.80 -7.98 S39a

-1.45 -0.43 -0.16 -0.74 -1.85 S39b

-2.12 -0.64 -0.19 -1.05 -2.75 S40a

-2.95 -0.90 -0.20 -1.39 -3.78 S40b

59.19 -6.99 -8.94 -18.16 -17.26 S41a


-0.21 -7.40 -0.17 45.94 -7.93 61.48 -7.59 -11.12 -20.47 -19.17 S41b

0.57 -27.43 -3.35 595.69 45.70 -0.28 -660.02 -197.86 -72.70 -39.39 S41c

0.76 -30.75 -3.65 697.33 53.73 -2.82 -896.75 -225.05 -85.17 -35.53 S42a

1.00 -34.32 -3.94 818.10 63.08 -6.04 -255.75 -99.57 -29.15 S42b

-5.44 -11.17 -3.63 217.40 -18.28 -4.13 -5.06 -80.24 -16.04 -62.56 S43a

-11.63 -23.69 -7.65 499.81 -38.27 -8.28 -10.15 -260.93 -33.57 -127.67 S43b

0.16 -4.79 -0.69 -35.31 -1.84 -10.74 -4.36 -0.57 49.04 -10.09 S44a

0.15 -6.18 -1.18 -44.70 -1.92 -12.51 -5.67 -1.72 52.05 -14.60 S44b

18.50 -3.81 -0.16 -1.54 17.84 -0.78 -3.01 4.29 2.61 -10.51 S45a


-36.80 -30.67 -1.92 -12.87 167.22 -6.08 -24.35 53.75 30.63 -92.86 S45b

-105.16 -37.20 -2.57 -15.87 211.18 -7.51 -29.55 72.21 40.57 -115.84 S46a

0.19 -0.63 -0.03 -0.21 -1.14 4.75 -0.63 0.92 0.44 -2.25 S46b

-15.31 -2.91 -0.14 -1.00 -5.43 22.77 -2.85 5.05 2.51 -10.57 S46c

6.74 -5.85 -0.33 -2.01 -14.22 1.34 44.52 9.60 3.53 -25.98 S47a

-74.28 -18.87 -1.26 -6.43 -46.62 5.53 154.73 37.50 14.71 -87.98 S47b

299.33 6.33 -2.45 -1.30 -12.37 -10.01 1.36 -124.44 -37.12 -41.17 S47c

598.43 12.31 -4.63 -2.57 -25.34 -21.24 2.34 -307.98 -72.69 -76.19 S48a

-6.78 0.38 0.01 0.02 -0.03 -0.08 -0.05 -0.35 5.38 2.56 S48b


-4.79 -0.32 -0.01 -0.22 -1.30 -0.15 -0.52 0.59 10.83 -0.20 S48c

0.02 0.80 0.01 -0.26 4.52 -0.21 -0.39 0.01 1.14 -2.06 S49a

0.02 -0.81 0.01 -0.35 5.61 -0.33 -0.57 -0.01 1.72 -3.08 S49b

0.02 -4.10 0.02 -0.48 6.43 -0.49 -0.79 -0.05 2.45 -4.42 S50a

0.14 3.73 0.13 -0.23 -2.55 31.75 -5.33 -2.26 -4.67 -6.61 S50b

0.25 -14.97 0.24 -0.45 -5.15 57.40 -9.04 -4.01 -8.00 -11.73 S50c

0.17 -10.46 0.21 -0.45 -5.53 54.01 -9.06 -4.55 -9.02 -12.32 S51a

-1.16 61.65 -0.81 -0.55 0.26 0.85 -124.11 -19.38 -15.50 -33.65 S51b

-1.49 66.82 -1.06 -0.87 -0.82 1.00 -170.27 -25.91 -22.25 -48.42 S51c


-1.71 0.69 -1.27 -1.87 -8.27 1.70 -185.61 -35.55 -36.86 -86.15 S52a

-7.71 307.86 -5.29 -5.74 -27.43 0.32 -4.89 -51.79 -16.19 -89.11 S52b

-19.47 789.56 -13.44 -14.47 -70.49 0.71 -12.01 -230.51 -41.80 -221.87 S53a

-1.18 97.26 -0.77 -0.03 2.94 -6.13 -4.28 -5.18 48.08 -33.37 S53b

-3.42 126.95 -2.11 0.68 8.83 3.59 2.61 -12.95 -104.12 -30.84 S54a

-2.00 80.17 -1.33 -0.51 4.62 -11.48 -7.88 -9.19 70.19 -66.15 S54b

0.01 0.05 2.02 -0.06 0.20 -0.27 -0.14 -0.47 -0.39 -0.18 S55a

0.02 -0.11 4.05 -0.15 1.24 -0.64 -0.35 -1.12 -1.65 -1.03 S55b

0.02 -0.11 4.05 -0.15 1.24 -0.64 -0.35 -1.12 -1.65 -1.03 S56a

BDG IND LKA RSA

0.00 0.12 0.67 0.00

0.04 -0.16 4.54 -0.02

0.00 -0.02 0.07 0.00

0.00 -0.06 0.16 -0.02

-2.94 1.84 246.93 -4.20

-5.01 4.09 477.18 -7.23

-1.00 -0.23 60.34 -1.45

-2.43 -0.78 165.88 -3.74

-0.01 -0.14 0.00 -1.20

-1189.49

Appendices CHN JPN KOR USA EU ROW EV

-0.13 -0.65 0.03 -0.29 -0.20 0.26 S56b

-0.73 -0.60 -0.02 -1.67 -1.29 -0.68 S56c

-0.01 -0.03 0.11 -0.02 -0.04 -0.03 S57a

-0.04 -0.08 0.33 -0.05 -0.14 -0.11 S57b

-12.23 -9.73 2.23 -119.22 -49.40 -60.14 S58a

-20.85 -19.27 4.17 -281.90 -91.07 -102.73 S58b


-0.04 -0.43 0.00 -7.76 11.26 -1.45 -0.43 -0.16 -0.74 -1.85 S59c21

-0.05 -0.61 0.00 -15.74 17.11 -2.12 -0.64 -0.19 -1.05 -2.75 S60a

-0.09 -4.64 -0.11 56.50 -4.03 29.32 -4.46 -6.09 -11.41 -10.08 S60b

-0.17 -6.68 -0.16 58.38 -6.60 48.14 -6.67 -9.83 -17.82 -16.18

0.57 -27.43 -3.35 595.69 45.70 -0.28 -660.02 -197.86 -72.70 -39.39

0.79 -30.27 -3.62 710.44 54.99 -1.95 -908.32 -225.23 -84.32 -32.71

S60c

BDG IND LKA RSA CHN JPN KOR USA EU ROW

-11.62 -23.63 -7.63 504.66 -38.03 -7.89 -10.01 -264.48 -33.29 -126.82

0.09 -1.18 0.26 -6.69 0.82 -3.32 -0.52 -2.76 12.03 0.67

0.12 -4.01 -0.42 -19.96 -0.77 -9.09 -3.36 -2.12 36.95 -7.33

0.10 -5.34 -0.82 -22.57 -0.58 -10.83 -4.49 -3.81 37.11 -11.24

-2.56 -3.33 0.70 -21.86 -20.74 -7.51 S58c

20

1.04 -33.79 -3.91 836.09 64.57 -5.11 -1203.05

-255.97 -98.63 -25.76

285

-6.15 -8.53 1.85 -55.65 -85.24 -19.16 S59a

3.13 -0.48 -0.13 -0.06 -0.21 -0.41 S59b

-5.44 -10.98 -3.61 223.76 -17.81 -3.16 -4.70 -87.37 -15.31 -60.99

-5.44 -11.13 -3.62 220.25 -18.11 -3.83 -4.95 -82.77 -15.84 -61.98

22


20 Finding that the RSA gains significantly, an additional experiment was performed by reversing the tariff to 0%-20%; it was found that RSA still gains about US$392 million, while South Korea loses US$275 million. 21 An additional experiment (not shown) was performed by reversing the tariff combination to 0%-5%; it was found that RSA still gains US$212.68 while the United States loses US$76.69, while all others lose. 22 Since RSA was found to be a loser in the earlier three experiments, two more experiments were performed with tariff combinations of (i) 20%-0% and (ii) 10%-0%. RSA and the EU gain US$9.86 and US$7.12 million, respectively in (i). In (ii), RSA loses US$7.2 and the EU gains US$25 million. This concludes that RSA should stick to import tax not lower than 20% to gain from RSA-EU FTA (not reported).

Regional Integration and Free Trade Agreements

Regional Integration and Free Trade Agreements

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