Can SAFTA be a Successful Free Trade Bloc? : Evidence from Asian FTAs. Abstract

Transcription

1 Can SAFTA be a Successful Free Trade Bloc? : Evidence from Asian FTAs. Dr. Sandeep Kaur Bhatia* Abstract Due to unprecedented pace of globalization, the dynamic South a new breed of dynamically growing developing countries has emerged. This has enhanced a spectacular growth of SouthSouth trade and investment flows especially in Asia, opening windows of opportunities for all countries, regardless of their level of development. This fact cannot be denied that amongst various nearly 51 regional trade blocs operate like Spaghetti Bowl in this region. In East Asia, ASEAN Free Trade Agreement (AFTA) emerges as the most successful bloc after achieving strong economic independence particularly through structural changes and market driven integrated policy. South Asia is still the least integrated region, even after completion of seven years of South Asia Free Trade Agreement (SAFTA). The present paper is an attempt to discuss position of SAFTA, amongst various SouthSouth FTAs in Asia and gives answers of certain important questions: Whether SAFTA have potential of trade as its member countries have similar production structure and lack of complementarities in trade? Does its Spaghetti Bowl work as a hindrance for trade creation? What would be the effective short sensitivity list, in the existence of long list of products in its agreement? Using Gravity model of trade, the paper reveals that in SAFTA there is not any evidence of negative effect of overlapping agreements on trade during and also, its Trade Diversion is meagre. There is untapped potential of trade of all members nations especially between India and Pakistan. With the help of Revealed Comparative Advantage, the paper produces a new effective sensitive list, excluding the products of advantage which needs to be implemented to explore existing intact its potential. *Assistant Professor, Centre for Economic Studies, Central University of Punjab, Bathinda.

2 Introduction: Due to unprecedented pace of globalization, the dynamic South a new breed of dynamically growing developing countries has emerged. This has enhanced a spectacular growth of SouthSouth trade and investment flows especially in Asia, opening windows of opportunities for all countries, regardless of their level of development, which has changed their trade pattern through the participation in regional integration arrangements. Its wide spread has given a birth of a new wave of regional integration, known as South South wave. It is well known fact that GATT s Article 24 generated the spread of regionalism across Europe, Latin America known as north north regionalism and proved as successful stories. This process has given encouragement to developing countries for participation in regionalism among themselves, but could not succeed in many cases due to lack of resources and political hurdles. So the realization of this fact has reassured them to take joint imitative with developed countries. This NorthSouth Wave known as new wave of regional integration, has given a major jump in world FDI and trade. So, WTO has received 612 notifications of RTAs (counting goods, services and accessions separately) ( Having peeped into the literature, it was found that the major similarities between previous two waves and new regionalism is difficulties in establishing and maintaining a common external tariff and major difference between previous waves and third wave of regionalism is that later is a qualitatively new phenomenon and was found more responsive to new form of globalization. Previous two waves mainly focused on unidimensional models that were narrow focus on free trade arrangements and security alliances and badly influenced by cold war politics. On the other hand new regionalism is multidimensional and inclusive, mostly influenced by global economic interdependence. New Regionalism is driven by the interaction of both state and non state actors like multinational corporations, nongovernmental organizations. The other major difference between previous waves and third wave is that former were dominated by the bipolar cold war structure and later is dominated by interrelated structural transformations of the global system. The most important feature of third wave is more universal and automatic in tariff liberalization process and on the other hand previous waves were characterized by significant selectivity in the same process. The other advantage of the third wave on the previous waves is that it is designed to attract foreign investment. Trends of South South Regional Trade Agreements ( Second Wave) There are currently 61 existing RTAs in Asia. Another 34 RTAs are at various stages of negotiation or ratification. Out of these, 3 RTAs namely SAFTA, AFTA and BIMSTEC have framed country wise free trade agreement. BIMSTEC is a new entry, so study concentrates on SAFTA with comaprison of AFTA. Table 1 : Number of FTAs Enforced in Asia RTAs Bilateral Plurilateral CountryRTAs Total Intraregional SouthSouth RTAs Interregional SouthSouth RTAs Intraregional NorthSouth RTAs Interregional NorthSouth RTAs Total Source : WTO, Note: Details are mentioned in Appendix A. In 2007, the level of intraasean trade and intrasaarc trade is low in comparison with EU and NAFTA (61.8 % for intra EU trade, 51.3% for NAFTA), which are successful trade blocs in the world.

3 There is no significant change in the intraasean trade. The intraasean trade was the lowest in 2000(23.0 percent) during This was the period when ASEAN had to face financial crisis. That could be the reason for low intraasean trade in this period. However, the performance of ASEAN in terms of intratrade is worthy as compared ECO and SAARC. But SAARC s performance is not satisfactory as compared to other blocs of Asia. Its intra trade ranged between 4.6 percent in 2000 to6.8 per cent in 2005 during This paper wants to examine the reasons of low intra trade of SAFTA through examining trends,patterns and potential of this bloc. Table 2: Intra trade of Region/Trade Blocs (% of Total Exports with in Bloc) Blocs EU NAFTA APTA ASEAN ECO SAFTA GCC Source: World Bank, World Development Indicators: Various Issues. Complementarities in Production and Trade between ASEAN Countries: This section studies complementarities in production and trade of ASEAN countries by estimating different indices and shares. ASEAN countries accounted for 9 percent of world population, The share in GDP of ASEAN countries was merely 3.76 percent Among ASEAN countries, Seven countries had deficit trade account balance in 2012 namely Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines and Thailand. The openness ratio among ASEAN countries ranged from percent in Myanmar to percent in Singapore( Appendix B.1). Structure of production measured through the shares in GDP shows that most of ASEAN countries were dominated by service sector in 1995, namely Malaysia (46.8 percent), Philippines (46.3 percent), Singapore(66.8 percent),thailand (49.7 percent), and Vietnam (44.1 percent) while GDP of Brunei (54.3 percent) and Indonesia (41.8 percent) was dominated by Industrial sector & GDP of Cambodia, Lao PDR and Myanmar dominated by agriculture sector (49.6 percent, 55.0 percent and 60.0 percent). In 2000, same pattern was followed except in Malaysia (again same countries dominated service sector, namely Philippines, Singapore, Thailand and Vietnam). In 2000, share of the service sector in GDP was maximum in Singapore (66.3 percent), shares of industrial sector in GDP was maximum in Brunei (63.7 percent), the share of agriculture sector in GDP was maximum in Myanmar (57.2 percent). In 2012, most of ASEAN countries were dominated by the service sector. Share of the service sector in GDP was maximum in Singapore (73.6 percent) followed by Philippines (56.9 percent)and Thailand( 51.0 percent) while other countries were dominated by industrial sector as Brunei (72.7 percent) followed by Indonesia (48.1 percent) and Malaysia (45.6 percent). In 2012, GDP of Cambodia (36.0 per cent) and Myanmar(30.6 per cent) were dominated by agriculture sector. The dissimilar production structure of these countries indicating existence of complementarities in production at sectoral level between ASEAN nations.(appendix B.2) Complementarities in Trade In 1995, export basket for most of ASEAN countries had been dominated by manufactured goods. In Singapore, manufactures accounted for 83.6 percent of total exports, 74.5 percent in Malaysia and 70.9 percent in Thailand. In manufactures, other manufactured goods played an important role. In spite of these, machinery and transport also constituted a large part in export basket. Chemical products as well as ores and metals amounted for a minimum share in the export basket for all the ASEAN countries and India. In 2012, exports of manufactures of all ASEAN countries (except Thailand and Vietnam) declined as compared to Import basket for all ASEAN countries had been dominated by manufactured goods

4 in In imports, maximum share was of Malaysia (83.4 percent) followed by Singapore (82.6 percent) and Brunei (81.8 percent). However, in case of machinery and transport, maximum share was of Malaysia (60.0 percent) followed by Singapore (57.9 percent) and Thailand (47.5 percent). In 2006 and 2012, import basket for all of the ASEAN countries again had been dominated by manufactured goods. Thus the huge complementarities in trade had been found lacking at section level. (Appendix B.3) RCA indices of ten top export items had also been measured for the year In 2012, out of the 59 export commodities with highest revealed comparative advantage, Brunei had revealed comparative advantage in narrow range of commodities (2 out of 10 top exported items). Countries namely Malaysia, Philippines and Singapore had revealed comparative advantage in 4 (out of 10) range of commodities. Thailand and Indonesia had revealed comparative advantage in 5 and 6 (out of 10) range of commodities. Country namely Myanmar had revealed comparative advantage in 7 (out of 10) range of commodities while in Vietnam, Lao PDR and Cambodia had revealed comparative advantage in 9 (out of 10) range of commodities. Most of items with highest revealed comparative advantage entered under miscellaneous manufactured articles section (20 out of 59) followed by machinery and transport equipment (12 items). In food and live animals, Vietnam had the highest revealed comparative advantage in rice (41.11), Myanmar in veg.etable and vegetable products nes (32.90). In animals and vegetable oils and fats, RCA of Indonesia was highest in fixed veg fat and oil, excl. soft (43.07) and Malaysia also in fixed veg. fat and oil excl. soft (26.15). In miscellaneous manufactured goods, Cambodia had highest revealed comparative advantage in female clothing, knitted crocheted (48.85) and Lao PDR in male clothing, woven (27.9) respectively indicating existence of complementarities in export structure in ( Appendix B.4) Trade intensity indices highlight the importance of secular changes in bilateral trade flows. In 1995, trade intensity index of Cambodia with ASEAN was maximum, i.e percent, followed by Lao PDR (8.80percent) and Myanmar (6.18 percent).countrywise analysis shows that Brunei s trade was most intense with Singapore (9.92 percent),cambodia with Vietnam (41.23 percent), Indonesia with Myanmar (5.93 percent), Lao PDR with Vietnam (78.63 percent), Malaysia with Singapore (8.18 percent), Myanmar with Singapore (12.71 percent), Philippines with Singapore (2.50 percent),singapore with Brunei (12.57 percent), Thailand with Lao PDR (32.89 percent), Vietnam with Lao PDR (81.64 percent), As compared to 1995, intensity index of all ASEAN countries (except Brunei, Cambodia, Myanmar & Vietnam) has increased in Cambodia witnessed a rapid decline from percent in 1995 to 4.00 percent in In 2012, index of Lao PDR was maximum with ASEAN i.e percent followed by Cambodia and Myanmar. Countrywise analysis shows that Brunei s trade was more intense with Indonesia (19.16 percent), of Cambodia with Vietnam (33.08 percent), of Indonesia with Brunei (22.40percent), of Lao PDR with Thailand (51.82 percent), of Malaysia with Singapore (8.33 percent), of Myanmar with Thailand (33.47 percent), of Philippines with Singapore (5.12 percent), of Singapore with Malaysia (9.42 percent), of Thailand with Lao PDR (56.24 percent), of Vietnam with Cambodia (30.59 percent )in In 2012, their intensity found decline.most of the ASEAN countries depend on only one or two markets like Philippines depends only on Singapore, Thailand and Vietnam only on Lao PDR, This has concluded that there is a lack of complementarities in trade.but in ASEAN some of the countries do not depend on only one market like Brunei, Cambodia, Indonesia etc. indicating that there exist complementarities at trade level.( Appendix B.5) The picture of the spaghetti bowl of ASEAN countries shows that most of the ASEAN nations have made free trade agreement with non member nations.. As we know that Thailand, Indonesia, Philippines, Malaysia, Singapore, Brunei, Cambodia, Laos, Myanmar, Vietnam are the members of ASEAN (1967) Indonesia which is a founder member of ASEAN has made Free trade agreement with Japan in July Malaysia also a founder member of ASEAN made a Free trade agreement with India in July 2011 and with Japan, New Zealand, Australia, Pakistan, Chile in 2006,2010,2013,2008,2012 respectively. Singapore which is also a major member country of this Organization also came up with maximum agreements with nonmember countries like with China in 2009, Costa Rica in 2013, EFTA in 2003, US in 2004, Japan in 2008,Pakistan in 2008, Australia in 2009, New Zealand , Chinese Taipei in

5 2009, Jordan in 2006, etc. Philippines have also made a free trade agreement with Japan in Dec, Vietnam, Myanmar, Brunei, Cambodia and Laos have not made any agreement with nonmember countries till now. Complementarities in Production and Trade among SAFTA The establishment of the South Asian Associations for Regional Cooperation (SAARC) in 1985 by the seven countries named as Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka was an attempt to bring about convergence in the region and reverse the conflicting tendencies that arose in the postindependence period in the region. However, in retrospect it was pre mature and a top down attempt at promoting regional cooperation, since ground realities in terms of trade and investment flows and political will were not really in place to support such efforts. Due to geopolitical challenges, the progress of cooperative efforts among the South Asian nations has been rather slow. The formation of South Asian Preferential trade arrangement (SAPTA) in 1995 was the first major political breakthrough for the SAARC since it was the first regional agreement on economic cooperation. But it was found that In South Asia, intraregional trade failed in achieving fullest pace under the SAPTA due to limited product coverage and tariff preferences. So as a result a decade after the preferential trade arrangement the South Asian Free Trade Agreement (SAFTA) was signed in 2004 and came into affect 2006, in place of SAPTA. The key motivation behind the creation of SAFTA was to enhance intraregional economic cooperation to maximize the potentiality of trade and development in the region (SAFTA Agreement, 2004) Investment has been left out of the ambit of the agreement. Even though the members were convinced that investment is important in strengthening the regional economy. This section studies complementarities in production and trade among SAARC countries by estimating export and import shares of different commodities, trade intensity indices and revealed comparative advantage indices. SAARC countries accounted for about 25 percent of world population but their share in GDP was merely 5.74 percent, in world exports was about 1.88 percent and in imports share was about 1.32percent. With in SAARC, India had the highest population and GDP whereas Maldives had the least population and GDP. Similarly, export earnings were also highest in India. All SAARC countries had deficit trade account balance in Openness ratio (trade to GDP ratio) ranged from percent in India to percent in Maldives.( Appendix C.1) Structure of production measured in terms of intersectoral composition of output given shows that in 1985, GDP of all SAARC countries except Bhutan and Nepal had been dominated by service sector. In Bhutan and Nepal, GDP had been dominated by agriculture sector (the share being 52.6 percent and 51.7 percent) in The share of service sector in GDP was maximum in Pakistan (49 percent) followed by Sri Lanka (48.8 percent) and Bangladesh (47.0 percent). In 1995, in all SAARC countries, share of agriculture in GDP had decreased and share of manufacturing (industry) and service sector in GDP had increased. The rapid decline in share of agriculture in GDP was in case of Bhutan and Nepal (share declined from 52.6 percent and 51.7 percent in 1985 to 34.0 percent and 40.8 percent in 1995 respectively) and the rapid increase in share of service sector in GDP was in case of India (share increased from 38.8 percent in 1985 to 43.6percent in 1995). In 2005 and 2012, GDP of all SAARC countries (except Bhutan) again had been dominated by service sector.in Bhutan, share of industry in GDP was maximum (42 percent). Share of service sector in GDP was maximum in Maldives (73 percent) followed by Sri Lanka (57 percent), India( 56 per cent ),Pakistan (54 percent) and Bangladesh (54 percent). In 2012, share of all SAARC members had decreased in agriculture sector while in industry sector, it had increased (except India) as compared to Similar production structure of SAARC nations indicated lack of complementarities in production at sectoral level.(appendix C.2) Complementarities in Trade Complementarities in trade measured in terms of trade structure of SAARC countries shows that in 1990, export basket for all SAARC countries had been dominated by manufactured goods. In Nepal, manufacturers accounted for 83 percent of total exports, 79 percent in Pakistan and 78 percent in

6 Bangladesh. In manufacturers, other manufactured goods played an important role in these countries. Other manufactured goods accounted for 83 percent, 78 percent and 76 per cent of total exports in Nepal, Pakistan and Bangladesh respectively. Fuels accounted for minimum share in export basket for all of the SAARC countries. In 1995, the situation remained same. The maximum share of manufactures in total exports was of Bangladesh (85.2 percent) followed by Nepal (83.7 percent) and Pakistan (83 percent). Again, other manufactured goods accounted for 82.4 percent, 81.8 percent and 80.5 percent of total exports in Nepal, Pakistan and Bangladesh respectively. In 2005, export basket of the SAARC countries except Maldives had been dominated by manufactured goods. Among SAARC nations, again Bangladesh constituted major share in manufacture i.e percent in total exports. In Maldives, all food items accounted for 66.7 percent share in total exports. As compared to 1990, share of exports of manufactures of all SAARC nations (except India, Maldives, Pakistan and Nepal) had increased in Import basket for all SAARC nations had been dominated by manufactured goods in In imports, maximum share was of Sri Lanka(64.8 percent) followed by Bangladesh (55.8 percent) and Pakistan (54.1 percent). In imports of manufactured goods, other manufactured goods played an important role in all SAARC nations except Nepal and Pakistan. In 1995, all SAARC nations share of manufactured goods in import basket had increased. However the rapid increase was found in Bangladesh (share increased from 55.8 percent in 1990 to 69.1 percent in 1995). Imports of other manufactured goods witnessed decline or remained stable in all SAARC nations except Bangladesh. The share of machinery and transport in imports of all SAARC nations (except Bangladesh) had increased or remained stable. In 2005 and 2012, import basket for all of the SAARC countries again had been dominated by manufactured goods. The maximum share was of Sri Lanka(60.7percent) followed by Bangladesh (60.2 percent )and Maldives (43.3 percent) in Again in it, other manufactured goods played an important role by contributing in Bangladesh (27.4 percent), Nepal (23.7percent) and Sri Lanka (28.2 percent) while for other SAARC nations, machinery and transport played an important role by contributing in India (16.9percent), Maldives (17.1 percent) and Pakistan (18.5 percent) in Thus the huge complementarities in trade had been found lacking at section level. ( Appendix C.4) Revealed Comparative Advantage In 2012, out of 46 export commodities with highest revealed comparative advantage, Sri Lanka had revealed comparative advantage in 9 (out of 10) range of commodities while number was 4 in Other SAARC country s condition also improved because all countries had revealed comparative advantage in at least 7 (out of 10) range of commodities. Most of the items with highest revealed comparative advantage entered under miscellaneous manufactured goods (23 out of 46) followed by machinery and transport (11) and food and live animals (7). In food and live animals, Sri Lanka again had highest revealed comparative advantage again in tea and mate (333.0), Maldives in Fish, dried salted or smoked (190.25) and Pakistan in rice (67.33) (same position as in 2002). In miscellaneous manufactured articles, Bangladesh had highest comparative advantage in male clothing, knitted crocheted (64.71), Pakistan in male clothing knitted crocheted (37.5), Maldives and Sri Lanka in female clothing in knitted crocheted (36.32) and (25.08) respectively indicating existence of complementarities in the export structure at commodity level in Trade intensity indices of different SAARC countries shows that in 1985, trade intensity index of Nepal with SAARC was maximum i.e percent followed by Maldives (13.05percent) and Bangladesh (6.82 percent). Countrywise analysis shows that Bangladesh s trade was most intense with Pakistan (24.69 percent), India with Nepal (39.67 percent), of Maldives with Sri Lanka ( percent), of Nepal with India (35.5 percent), of Pakistan with Bangladesh (18.70percent), and of Sri Lanka with Maldives (148.29percent). As compared to 1985, intensity index of Bangladesh, Nepal, Pakistan and Sri Lanka with SAARC experienced decline in Nepal witnessed a rapid decline from percent in 1985 to 8.09 percent in In 2005, index of Nepal with SAARC was maximum i.e percent followed by Bhutan (18.12percent) and Sri Lanka (7.16percent). Countrywise analysis shows that Bangladesh s trade was most intense with Bhutan (6.42percent), of Bhutan with Bangladesh (26.45percent), of India with Nepal (47.18percent), of Maldives with Sri Lanka (140.66percent), of Nepal with India (31.25percent), of

7 Pakistan with Sri Lanka (11.61percent) and of Sri Lanka with Maldives (73.28percent). Heavy dependence on either one or two markets lead to reduction in intrasaarc exports of countries like Bhutan, Maldives and Nepal. Thus, there is a lack of complementarities in production and trade among SAARC countries. Heavy dependence on either one or two markets lead to reduction in intrasaarc exports of countries. Now question comes whether spaghetti bowl of SAFTA work as a hinderance or not. Member countries of SAFTA have free trade agreement with nonmember countries. For example India has made a free trade agreement with Sri Lanka in 2001, Afghanistan in 2003, and Japan in 2005, Singapore in 2005, Bhutan in 2006, MERCOSUR in 2009, Nepal in 2009, ASEAN in (Goods) 2010, (Services)2015, Korea in 2010, Malaysia in 2011, Chile in Pakistan has also made free trade agreement with Sri Lanka in 2005, China in 2007, Mauritius in 2015 and Malaysia in Bangladesh and Maldives has no free trade agreement with nonmembers as well as member countries till now. Nepal came up with a Free trade agreement with India in 2009, and Bhutan with India in Afghanistan and Sri Lanka has also made a free trade agreement with India in 2003, 2001 respectively. Its further analysis has been done in gravity model of trade. The comparison of sensitive list of SAFTA member countries in Phase II with Phase I shows that SAFTA members are declining the number of goods in sensitive list to increase trade liberalization. Under SAFTA phase II, the SAARC nations have largely trimmed down their reserved list for both the LDCs and the NonLDCs member nations. The nations together have slashed their sensitive list by about 20 percent. In the phasei, each of the eight member nations had on average commodities on their reserved list for the NonLDCs member nations and on an average commodities for the LDCs. Contrastingly the phaseii crusade for pruning the reserved list saw some interesting results, as on average now each member nation has about commodities on the reserved list for the NonLDCs and about commodities for the LDCs. Moreover, most of these commodities belong to the group where the neither the reporting nation, nor the exporting nation is seen as. In this regard, the region should hope more decrease in the commodities on the reserved list of the nations. Analyzing the sensitive list of the member nations maintained against each of the country in the SAARC region, four categories of 99 commodities sections have been made on the basis of RCA. The reporting country is more than the counterpart exporting country (yellow box), ness of both the reporting and the exporting country for such commodities at parity( green box), both the countries are not at all(red box) and the exporting country is more ( blue box). Summary of the analysis mentioned in Appendix D is presented in below table. Given table shows the number of categories of commodities that are plausible for the time being for each of the reporting nations to contain in the reserved list. For these categories of commodities, the RCA for the reporting nation is less than unity in contrast to RCA greater than unity for the exporting nation. Table 3: No. of Categories of Commodities to be retained on the Sensitive List Country Afghanistan Bangladesh Bhutan India Maldives Nepal Pakistan Sri Lanka Source : prepared from Appendix D.

8 Pakistan in its sensitive list has more of the commodities in which neither Pakistan itself and nor the exporting country is. These commodities thus need to be removed from the sensitive list of Pakistan for a greater trade and greater ness in a phased manner. India unlike Pakistan is less in larger number of commodities than the other SAARC countries, which would take time to be removed from the India s sensitive list. Similarly unlike Pakistan, it has greater competiveness in most of the commodities in contrast to other SAARC countries that could easily be pruned out from its sensitive list. Moreover, both India and the other SAARC countries in greater sections of commodities are not at all. This also provides greater scope for trimming down the sensitive list of India. The nations also require doing away with the large number of commodities on its positive list where it is at parity in competiveness with the other SAARC nations.bhutan with its lesser share in the interregional SAARC trade has lesser number of commodities that require to be shunned from its reserved list. It has its more of the commodities in the list wherein it like other SAARC countries possesses either no comparative advantage at all or has in contrast to these countries a greater comparative advantage. So, most of the commodities can be obviated from the reserved list more easily. In case of Bangladesh, most of its commodities fall in the list where it like the other SAARC countries is not at all. Such commodities need to be removed from its sensitive list as to try to gain ness in such commodities and foster a greater trade in the region. After this a large number of commodities fall in the category where the nation is less than the other SAARC nations, such commodities need to be removed with the passage of time in a phased manner.afghanistan being the newer member of SAARC contributes marginally to the trade in the region. Most of the commodities on the sensitive list of the nation fall in the category, where like other SAARC nations it has got no ness. The country thus requires doing away with such commodities from its sensitive list, as to achieve a greater share in this regional trade. The country also with great number of its commodities on its reserved list has got greater advantage. In this case there should be no hesitation from removing these commodities from its reserved list. Most of the commodities on the Maldives s negative list are those wherein the nation likewise the other SAARC nations has got no ness at all. Such commodities pose a bright opportunity of inflated trade in the region if they are removed from the reserved list, plus the nation might be able to gain a hand in such commodities. After these commodities, the nation has a greater section of commodities on its reserved list, in which the nation possesses a advantage in contrast to other countries of the region. Such categories of commodities could be easily removed from the reserved list.nepal has a large no of commodities on its reserved list, wherein it possesses no ness like the other SAARC nations. These commodities need to be obviated from the reserved list for a greater share of the nation in the regional trade and the nation might with the passage of time become in these commodities in contrast to other SAARC nations. Also on its reserved list are a large number of commodities in the nation is more in contrast to the other SAARC nations. The nation could thus easily remove such commodities from its list and acquire a greater share in the regional trade. Nepal has also some commodities on its reserved list for which it is less in contrast to the other SAARC nations, such commodities need shunning from the list in a phased manner in the future. Sri Lanka has a hefty number of commodities on its reserved list in which it like the other SAARC nations possesses no advantage at all. These commodities greatly require to be removed from the list, for it would inflate the regional trade and the nation might with the time gain ness in some of these commodities. The country also has large number of commodities on its reserved list in which the nation possesses clear ness; such commodities require to be removed first from the list. Also a great number of such commodities fall in the category, where the nation is at parity with the other SAARC nations in regard to ness. These commodities also require removal from the list. Removal in a phased manner is required the by commodities on the nations reserved list, wherein the nation is less than other SAARC countries.

9 Trade Potential of SAFTA through Gravity Model of Trade Classical and new trade theory explain the reasons for trade without explaining the quantity of the trade flows. Gravity theory of trade has tried to overcome this limitation by analyzing patterns and performances of international trade. This model of trade is similar to the Newton s Law. This model of trade is as follows, The trade flow between two countries is proportional to the product of each country s economic mass, generally measured by GDP, each to the power of quantities to be determined, divided by the distance between the countries respective economic centers of gravity generally their capitals raised to power of another quantity to be determined (Christie, 2002). Therefore, among the above mentioned trade theories, the gravity model has been chosen to quantify the selected South Asian countries intraexports (Bangladesh, India,Nepal, Pakistan and Sri Lanka). The study has used the following gravity model specification: L ne ijt= + 1L ny it+ 2L ny jt+ 3L npop it+ 4L npop jt+ 5Rer it+ 6Rer jt+ 7L ndis ij+ 8 or ij+ 9Lan ij+e ijt (1) E ijt = Export flows in year t from country I to country j (SAARC countries); Y it = Country i s GDP in year t (measured in US $ millions); Y jt = Country j s GDP in year t (measured in US $ millions); Pop it = Country i s population in year t (measured in thousands); Pop jt = Country j s population in year t (measured in thousands); Rer it= Real exchange rate between the exporter s currency and the US $ in period t; Rer j = Real exchange rate between the importer s currency and the US $ in period t; Dis ij = Distance between country i and j (Kms); Bor ij = Border is a dummy variable for pair of countries sharing common border; and Lan ij = This is also a dummy variable for a pair of countries sharing common language. L n = Natural logs To assess the impact of the spaghetti bowl, the above mentioned gravity model is estimated. Two dummy variables capturing the potential trade effects of preferential integration and overlapping trade agreements are incorporated ( Fergin,201 ).The first picks up the trade effect of a Bilateral PTA. It takes the value 1 if the two member countries have signed bilateral PTA with each other and 0 otherwise: L ne ijt= + 1L ny it+ 2L ny jt+ 3L npop it+ 4L npop jt+ 5Rer it+ 6Rer jt+ 7L ndis ij+ 8 or ij+ 9Lan ij+ + 10Bil ijt+e ijt (2) Bil ijt= This is a dummy variable representing both countries have framed bilateral PTA in time tor not in time t. If one of the countries have signed an agreement with nonmember country at a point of time,the second dummy variable takes the value 1, and 0 otherwise. The dummy variable can be called the spaghetti bowl variable and shows whether bilateral trade is affected by a country entering additional agreements with a third country. This bowl may reduce the PTA s creation effect and so it often works negatively. To assess this bowl effect, interaction of value of this dummy variable with the PTAdummy is entered into the following equation : LnEijt= + 1LnYit+ 2LnYjt+ 3LnPopit+ 4LnPopjt+ 5Rerit+ 6Rerjt + 7LnDisij + 8 orij + 9Lanij + β10 Bilijt Spgijt)+ eijt (3) +β11(bilijt S pgijt= This is a dummy variable representing whether one member country has framed any FTA with non member country in period t. The coefficients for the PTAdummy and the interacted variable should be interpreted as: β10 = the trade creating effect of a PTA

10 β11= the effect of the spaghetti bowl variable on the PTA effect (for observations where both the PTAdummy and the spaghetti bowl dummy equals 1). It is hypothesized that 1, 2, 5, 8, 9, 10>0 10>0 ; 3, 4<0 or >0 and 6, 7, 11< 0. The study has found that fixed effect model s results are far better than other estimation s methods. Therefore, estimated country pair effects of fixed effects method s is regressed on on the time invariant variables in order to find out the importance of these variables in the fixed effects. The following regression is followed: Where: ij is country individual effects a 2, a 3, a 4 are coefficients. It is expected that a 2<0 but a 3 and a 4 >0 ij = a 1 + a 2 L ndis ij + a 3Bor ij + a 4 Lan ij + e ij The simple distance calculated following the great circle formula which uses latitudes and longitudes of the most important city (in term of population) or of its official capital ( These distances were expressed as the distance (in kms) between the capital cities. The language dummy value is zero for all SAARC nations except Pakistan shows that Indian and Pakistan language is well understood by both countries. Border s dummy value is zero for Maldives and Sri Lanka showing that their borders are not shared by India. Gravity Variables The study has taken different variables to estimate the gravity model of SAFTA s intraexports. These are: (a) Gross Domestic Product of Exporter Country: A high level of income in the exporting country indicates a high level of production which increases the availability of goods for exports. Therefore it is expected that coefficient for this variable to be positive (Harris and Matyas (1998), Matyas et al (2000), Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). (b) Gross Domestic Product of Importer Country: Since a high level of income in the importing country suggests higher imports, therefore it is also expected that coefficient for this variable to be positive (Harris and Matyas (1998), Matyas et al (2000), Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). (c) Population of Exporter Country: The coefficient estimate for population of the exporter may be positive or negative depending on whether the country exports less when it is big (absorption effect) or whether a big country export more than a small country (economies of scale) (Harris and Matyas (1998), Matyas et al (2000), Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). (d) Population of Importer Country: The coefficient of the importing country s population has also an ambiguous sign due to above reasons (Harris and Matyas (1998), Matyas et al (2000), Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). (e) Real Exchange Rate of Exporter Country: Real exchange rate is defined as local currency value of 1 unit in US $ multiplied by India s deflator and divided by US s deflator. It is expected to be positive as India s exchange rate depricates with respect to other and will have a positive impact on India s exports (Harris and Matyas (1998), Matyas et al (2000), Zorzoso and Lahman (2000) and Abraham and Hove (2005)).

11 (f) Real Exchange Rate of Importer Country: This rate is obtained by local currency value of 1 unit in US $ multiplied by importer country s deflator and divided by US s deflator. The coefficient for this variable is expected to be negative as import country s currency depreciates with respect to other countries, other country s exports will fall (Harris and Matyas (1998), Matyas et al (2000), Zorzoso and Lahman (2000) and Abraham and Hove (2005)). (g) Bilateral Dummy : The sign of this dummy is expected to be positive as bilateral trade agreement between two member nations has a positive effect on trade. (h) Spaghwetti dummy: The sign of this coefficient is expected to be negative as if member countries of a FTA are members of other FTA have a negative impact on their trade. (i) Distance: Distance between two countries is expressed as the distance (in km) between the capital cities. Distance has a negative impact on volume of exports. As the distance between the exporting and importing country becomes longer, exports will fall. The distance is a factor which is used as a proxy to consider the impact of transport cost and other transaction costs. Therefore the coefficient for this variables is expected to be negative (Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). (j) Border: This is the dummy variable for a pair of countries sharing common border. As the sharing border increases the export between any given pair of countries, therefore it is expected that coefficient for this variable to be positive (Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). (k) Language: This is also dummy variable for a pair of counters sharing common language. It is also expected that coefficient for this variable to be positive as the exports (trade) will be more between two countries if language is well understood in both trading partners (Zorzoso and Lahman (2000), Abraham and Hove (2005) and Rahman (2005)). Estimation Results of Gravity Model The estimation results of bilateral exports of SAFTA (Bangladesh, India, Nepal, Pakistan and Sri Lanka) have been reported in Table 4. The gravity model of exports has been estimated by restricted (pooled) model, fixed effect model and random effect model. The restricted model is the pooled model with the restrictive assumptions of single intercept and with the same parameter over time and across trading partners. The unrestricted model (fixed effect model), however is the same behavioral equation but allows the intercept to vary across trading partners. Formally, Ftest has been carried out to test for the null hypothesis that the country specific effects are jointly zero. In Table1, the value of F test was which was far larger than tabulated value and supported the alternate hypothesis indicating SAARC countries had different propensities to export. The pooled estimation gives biased results due to omitted variables. Next, the Hausman test has also been performed to compare the fixed and random effect estimators. The statistic result had a value of which was also far larger than the critical value. This suggested that the fixed effect is a better choice than the random effect. Therefore, the direction of the study focuses on the fixed effects estimation. Exports equation has run through above mentioned three estimation methods. Estimated coefficients had nearly all the expected signs except for GDP of importer country and bowl variable. However, the magnitudes of the coefficients in pooled and random effect estimation were notably different from those in the fixed effect method suggesting that there might be biased results due to ignoring country individual effects in pooled estimation and inconsistent estimates because of correlation between the individual effects and other regressors in random effect method. Even Ftest and Hausman test had also supported the same argument for the present data. Gravity model results given in Table 4 shows that economic size (GDP) of exporter country came out to be significantly and positively affecting SAARC s intraexports showing that it tends to exports with larger economies. An increase by one percent of GDP of exporter country will go in increasing exports by an average index of 1.11 per cent while coefficient of GDP of importer country is negative but not significant. But market size (population) of exporter country had positively impact on Indian exports, Market size (population) of importer country had also negatively affected the exports showing absorption effect i.e. country exports less when it is big. Surprisingly, real

12 exchange rate of exporter country had significant and positive effect on exports. One percent increase in country s value of exchange rate (depreciation of the exporter currency) will increase the exports by 0.86 percent on average only. While real exchange rate of importer country had significant and negative effect on exports. One per cent increase in currency of importer countries will decrease in exports up to 0.91 per cent only. Bilateral dummy s sign is positive and insignificant showing that bilateral agreements among themselves have a positive impact on intratrade but they are not significantly playing a major role. But the spaghwetti bowl s problem has not been seen in case of SAFTA as coefficient of this variable is positive but insignificant showing that it can be a source of diversion of trade. when the fixed effects from model are regressed on the distance variables and dummies which are fixed over time (Border and Language). According to the results, all the nontime variant variables (Distance, Border and Languages) were found to be nonsignificant and had expected sign. A very low R 2 coefficient (0.28) means that there were other determinants of the trading pair effects (which have not keen included in the analysis). Table 4 Results of Gravity Model Variable Restricted/ Pooled Estimation Coefficient Z statistics Fixed Effects Estimation Random Effects Estimation Coefficient Zstatistics Coefficient Zstatistics Constant 16.93** * ** Gross Domestic Product of Exporter Country Gross Domestic Product of Importer Country Population of Exporter Country Population of Importer Country Real Exchange Rate of Exporter Country Real Exchange Rate of Importer country 0.24** ** ** ** ** ** ** ** * ** ** ** Bilateral 0.66** Bowl R Restricted Ftest 36.05** Hausman Test Source: Based on data given in Direction of Trade Statistics Year Book &. World Development Indicators.**Significant at one per cent level.*significant at five per cent level. Figures in parentheses are degrees of freedom.

13 Export Potential:The gravity model is not only useful to find out determinants of bilateral exports flows. It can also be used to predict future trade flows or export flows. In particular, it is used to calculate export potentials i.e. difference between the predicted and the actual bilateral export flows. Predictions are based on the gravity model estimates. The study had estimated the export potentials by fixed effect model only since fixed effect model had been proved as better model than random effect model. Table 5 reports the export potential by calculating the difference between the potential (P) and actual level of exports (A) i.e. value of PA. A positive value indicates future possibilities of export expansion while a negative value shows exporter has exceeded its export potential with the particular SAARC countries (Batra, 2004). The average of export potential had been calculated to find out the export potentials of SAARC members with India over a period of time. The average of PA was THE highest for Pakistan with Bangladesh (37.98) followed by Pakistan with Sri Lanka (21.19) during showing that for Bangladesh and Sri Lanka, Pakistan had export potential with these nations where for other SAARC members especially India, it was negative with all selected countries showing that India has exceeded its export potential with these nations. Table 5 Export Potential between SAARC Members using (PA)* Approach (US $ Millions) Exporter Bangladesh India Nepal Pakistan Sri Lanka Importer Bangladesh India Nepal Pakistan Srilanka Source: Based on the results of gravity model. *P=predicted by gravity model, A=actual values. Table 6: Export Potential between SAARC Members using (P/A)* Approach (US $ Millions) Exporter Bangladesh India Nepal Pakistan Sri Lanka Importer Bangladesh India Nepal Pakistan Srilanka Soure: Based on the results of gravity model *P=predicted by gravity model, A=actual value. Export potential had also been calculated with the help of ratio method. The ratio of export potential (P) as predicted by the model and actual trade (A) was also used to analyze the future direction of export for

14 India. If the value of P/A exceeds one, there is a potential expansion of exports with the respective country (Batra, 2004). The results of this ratio are given in Table 6. The average of this ratio was maximum for Nepal with Bangladesh (5.89) followed by Nepal with Sri Lanka(5.54), depicting that scope of trade with them. Conclusions: The South Asian block is different from other regional blocks in the world, since it is dominated by a few large countries (India, Pakistan, Bangladesh, Si Lanka) which have had bilateral political conflicts and their trade largely with the rest of the world rather than their neighbors. India which is largest trading country in South Asia, but virtually all of its formal trade is with the rest of the world. South Asia has only eight countries, compared to more than 20 in East Asia and Latin America, coupled with the dominance of a large country namely India, which tends to trade less as a share of GDP (New farmer 2004) While the North American Free trade agreement (NAFTA) has even a lower number of members than SAFTA, and is dominated by its large member country US, the difference between SAFTA and NAFTA lies in the fact that the US aggressively moving towards regional integration as compare to India. Thus the study reveals that among SAARC countries, export potential has been exhausted with most of SAARC Nations. SAFTA member nations should move further for reduction in sensitive list and also,the intra trade in various services can generate complementarites in merchandise trade. The implementation of SAFTA has faced political hurdles. For example, the denial of most favored nation status by Pakistan to India when both the WTO members is the result of the political divide between India and Pakistan.

15 Appendix A List of FTAs enforced in Asia Intraregional SouthSouth FTAs [Bilateral] Republic of KoreaSingapore, FTA,2006 MalaysiaPakistan, FTA,2006 PakistanSri Lanka FTA,2005 IndiaSri Lanka FTA,2001 IndiaBhutan FTA, 2006 IndiaSingapore FTA, 2005 ChinaThailand, FTA, 2006 ChinaPakistan,FTA, 2007 ChinaHong Kong SAR, FTA,2004 ChinaMacao SAR, FTA,2004 IndiaNepal, FTA, 2009 IndiaAfghanistan, FTA, 2003 IndiaJapan, FTA, 2011 IndiaMalaysia, FTA, 2011 PakistanSri Lanka, FTA, 2005 PakistanMalaysia, FTA, 2008 [Plurilateral] AFTA (ASEAN Free Trade Agreement) Goods, 1993 Services, 1996 SAFTA (South Asia Free Trade Agreement : Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka), FTA, 2006 SAPTA (South Asian Preferential Trade Arrangement (Bangladesh; Bhutan; India; Maldives; Nepal; Pakistan; Sri Lanka)FTA, 1995 [CountryRTAs] ACFTA: ChinaASEAN FTA,2005 Republic of KoreaASEAN,2007 ASEANRepublic of Korea, (Brunei Darussalam; Myanmar; Cambodia; Indonesia; Lao People's Democratic Republic; Malaysia; Philippines; Singapore; Viet Nam; Thailand; Korea, Republic of), FTA, Goods(2010), Services(2009) Interregional SouthSouth FTAs [Bilateral] ChinaCHILE,FTA,2006 Republic of KoreaChile,FTA,2004 SingaporeJordan, FTA,2005 SingaporePanama, FTA, 2006 Republic of KoreaSingapore, FTA, 2006 Republic of Korea India, FTA, 2010 Republic of Korea Chile, FTA, 2004 ChinaMacao, China,FTA, 2003 ChileMalaysia, FTA, 2004 JapanIndonesia, FTA, 2008 PanamaSingapore,FTA,2006