Abstract
Our purpose is to undertake a comparative analysis of the likely impact of tariff reduction under the Trans-Pacific Partnership (TPP) on various macro and trade variables of the Indian economy under different scenarios. The TPP was concluded in October 2015, but it is yet to be ratified by the partner countries, and while Asian giants like India, China and Korea have not joined the TPP, there are some talks about their joining the partnership in future. Ours is a unique study that evaluates India’s perspective on joining the TPP, in terms of tariff reduction, and not in terms of the removal of non-tariff barriers. We employ the widely used standard Global Trade Analysis Project (GTAP) model for this exercise. This is a unique framework with a global economy-wide approach, in a Computable General Equilibrium (CGE) setting. Five different scenarios of complete integration in terms of tariff reduction between different regions are simulated using the GTAP model. Under each scenario, the tariff among members of a group of regions is eliminated, but is unchanged for other regions. Higher welfare arising from allocative efficiency comes with the cost of a relatively lower consumption of domestic products and investment, resulting in a loss in terms of GDP. Therefore, we conclude that there are mixed prospects and no strong reason for India to pursue being part of the TPP in future, from a perspective of tariff reductions.
INTRODUCTION
The Trans-Pacific Partnership (TPP) negotiations have emerged amidst a lot of uncertainty about the global trading system as well as concern due to the slow progress of the multilateral system under WTO (Petri et al., 2011). To promote economic growth and trade through regional integration, Brunei, Chile, New Zealand and Singapore signed the Trans-Pacific Strategic Economic Partnership Agreement (TPSEP or P4). Negotiations for the TPP, which began in 2010, culminated in the conclusion of a deal in October 2015, to expand the scope of the TPSEP in terms of membership as well as content, by including various issues related to trade and investment. Twelve countries, namely, Brunei, Chile, New Zealand, Singapore, United States, Australia, Peru, Vietnam, Malaysia, Mexico, Canada and Japan, negotiated the TPP to arrive at this deal, but it is yet to be ratified by the parliaments of the partner/member countries. Since 2013, Taiwan and South Korea have also shown interest in joining the TPP, although they have not been part of the deal in 2015. However, emerging economies like India and China have not been part of the TPP negotiations since the beginning until now that the deal has been arrived at. The TPP agreement is believed to have 29 chapters dealing with issues like IPR (Intellectual Property Rights), rules related to SPS (Sanitary and Phytosanitary Standards) & TBT (Technical Barriers to Trade), market access, investment, labour, environment, etc.
Since the TPP deal was achieved during the final revisions of this article, most of the current literature focuses on TPP negotiations and the possibility of their success as well as potential impact if they were concluded. Seshadri (2013) mentioned that with the vast coverage of issues like trade and investment, the TPP is bound to have an influence on other free trade initiatives underway, as also on the Doha multilateral trade negotiations. TPP members include both large and small economies drawn from either side of the Pacific. This study also pointed out that the US has taken a leadership role in the negotiations due to its unwillingness to make concession on market access and agriculture subsidies under the Doha Round and has been looking for other trade liberalisation initiatives in which an asymmetric strategy will be successful where its contribution will be minimal and its gains optimal.
Using Computable General Equilibrium (CGE) modelling, many studies like Lee and Itakura (2014), Cheong (2013), Arif et al. (2014), Xin (2014) and Petri et al. (2011) try quantifying the impact of the TPP on different regions. The study by Lee and Itakura (2014) used the GTAP dynamic model to examine the welfare impact of the Regional Comprehensive Economic Partnership (RCEP) and TPP on various regions. Their conclusion is that India will experience a welfare gain in the case of the RCEP by 0.5 to 1.3 percentage points in comparison to the baseline projection. As India is not member of the TPP, trade liberalisation under the TPP track will have a negative impact in comparison to the baseline.
Cheong (2013) analyses progress on major issues regarding the TPP negotiations which were being led by the US, and draws implications for East Asian economic integration. His paper argues that the TPP should be promoted for its economic value, not for geopolitical purposes. It should be open to all Asian and Pacific countries, including the People’s Republic of China. The impact of forming the TPP under three scenarios was estimated using the GDyn, a recursive dynamic CGE model developed by the Global Trade Analysis Project (GTAP). The three scenarios are TPP9 (nine TPP members), TPP12 (12 members) and TPP12+ China (13 members). As India is not a member of the TPP in these three scenarios, its GDP declines by 0.01 to 0.38 percentage points in comparison to the baseline projection.
Arif et al. (2014) examine the impacts of the TPP on the Turkish economy. By using the GTAP database and a general equilibrium model, the effects are studied of various scenarios on the GDP and exports. The results show that Turkey could face losses of up to 1 per cent in its GDP if the TPP covers only the current 12 countries. However, if the agreement is widened to include other countries, Turkey’s losses could reach 2.4 per cent of its GDP. Exports may decline by 0.65 per cent in the first scenario and by up to 1.79 per cent in the second scenario.
Xin (2014) shows that most of the macroeconomic indicators (like GDP, consumption, real export, imports and employment) are positive for China, US and Japan but negative for Vietnam, Singapore, Australia and New Zealand if China becomes a member of the TPP.
Petri et al. (2011) carry out a quantitative assessment of the TPP and Asia-Pacific integration by using the GTAP database. According to this study, the TPP and an Asian Track could consolidate the ‘noodle bowl’ of current smaller agreements and provide pathways to a Free Trade Area of the Asia-Pacific (FTAAP). The effects on the world economy would be small initially, but by 2025 annual welfare gain would rise to $104 billion on the TPP track, $303 billion on both tracks, and $862 billion with an FTAAP. The study also mentioned that strong economic incentives would emerge for the US and China to consolidate the tracks into a region-wide agreement.
The above-mentioned studies analyse and quantify various aspects of the TPP and its impact on different regions. However, very little research has been done to quantify the impact of the TPP on the Indian economy under different scenarios. Although the TPP deal has been achieved without India, China and Korea as partners currently, it is possible that they may join the Partnership in future, as additional members; particularly in the case of Korea, the possibilities are very high. Therefore, it would be interesting to see the impact of the TPP on the Indian economy in two cases: (i) if India is a member of the TPP, and (ii) if India is not a member of the TPP. It will also be important to see the impact of the TPP on various macroeconomic and trade indicators of the Indian economy if China joins the TPP.
On the issue of India joining the TPP, Seshadri (2013) pointed out that there is no immediate prospect of India joining such an agreement due to commitments such as supply-chain management, regulatory coherence and TRIPS-plus issues. If the recently concluded TPP deal is fully ratified and implemented by the member countries, India may lose some market share in TPP markets as a result of trade diversion. Generally speaking, however, the negative fallout may not be very significant as India already has FTAs with some TPP participants. India’s main loss on market access would, therefore, come from the US market, where Vietnam and Malaysia could be particular beneficiaries in products such as textiles, apparel, leather goods, etc., where US’s MFN tariffs are relatively high, compared to other sectors.
CGE modelling is the chosen approach for the study because it provides an economy-wide insight into the ‘what-if’ question we pose herein. Another major alternative that may have been considered is the gravity model; we do not pursue this since the focus of such models is more on past trends rather than on a hypothetical future-linked possibility such as the TPP. Andreosso-O’Callaghan (2009) offers another interesting approach to deal with this question: economic structural complementarity analysis. While this approach provides a framework for structural comparisons, we do not pursue this for our current question, since it is difficult to quantify the potential changes in GDP, trade and sectoral output in such a framework. The CGE approach has been adopted for several CGE studies focusing on India’s role in trade agreements, such as the SAFTA (South Asian Free Trade Agreement), in a study by Kumar and Saini (2009).
With this background, the objective of this study is to make a comparative analysis of the likely impact of tariff reduction under the TPP on various macro and trade variables of the Indian economy under different scenarios by using the GTAP static model. While most of the other studies on TPP have included non-tariff barriers in the analysis, we ignore them and focus solely on tariff measures. The unique contribution of this article lies in the evaluation of scenarios wherein India may be involved in the TPP and its focus on the impact on India from different TPP scenarios. This has the potential to provide deep insights to the currently active policy debate on the TPP in Asia.
METHODOLOGY
Before delving into the methodology, we look at the total bilateral trade flows between the regions involved in this article (see Appendix for details). The top sources of India’s imports are the EU27, China, US, Japan and Australia, of which the last three are current TPP members. China mainly imports from the EU27, Japan, US, Korea and Australia. India’s top export destinations include the EU27, US, China, Japan and Korea, while China exports chiefly to the US, EU27, Japan, Korea and India. Therefore, Korea, China and India are closely related to the TPP members and it is important to consider their involvement in this partnership.
This study is conducted with a multi-country, multi-sector general equilibrium model. WTO (2012) states that the purpose of CGE simulations is to determine the effects of a change in trade policy on the endogenous variables of the model—prices, production, consumption, exports, imports and welfare. The simulation represents what the economy would look like if a policy change or shock occurred. The difference in the values of the endogenous variables in the baseline and the simulation represents the effect of the policy change. All the policy simulations as well as the results reported in the paper, as in other major models of this type, may be thought of as occurring in one shot over a time period that is needed for equilibrium to be achieved. This time period is akin to what is widely thought of by economists as the ‘medium run’, possibly 3–5 years at a go. So the model should be able to foretell the effect on trade and production patterns if the trade policy was changed. Furthermore, based on the change in welfare, the policy-maker would be able to judge whether the country benefited from the change in policy or not. Similarly, Gilbert (2013) mentions that the idea behind the CGE is to programme a large-scale mathematical system representing the global economy and combine that theoretical system with a benchmark set of real-world data representing the status quo. Shocks are then introduced to perturb the equilibrium to generate insights into the direction and magnitude of the economic effects of policy intervention and/or other changes in the economic system.
The impact of regional integration on different regions is estimated by using the GTAP static model. The model assumes perfect competition, constant returns to scale and profit and utility-maximising behaviour of firms and household, respectively. Hertel (1997) provides detailed information about the structure and overview of the GTAP model. The data used in this study is the version 8.2 (the most recent version available during the period of analysis, documented in Narayanan et al., 2012) of the GTAP database. The reference year for this database is 2007. We employed a non-publicly available 1 version of the GTAP 8.2 Data Base (140 regions), which is better suited for this analysis than the GTAP 8 Data Base, since the IO tables for China and a few other countries were improved in this version and the tariff data issues were also addressed in it; even more importantly, Brunei was included as one of the countries in this version.
Regional Aggregation
Regional Aggregation
The GTAP database is compiled for 140 countries/regions across the world and for 57 tradable commodities. In this study, the 140 countries/regions given in the GTAP data base are mapped to 17 regions (Table 1). The analysis is done for 18 sectors given in the GTAP database. The 57 sectors of the GTAP data base are mapped to 18 sectors (Table 2).
Experiment Design
Given the unstable economic environment, unemployment is a general phenomenon around the world. Therefore, to make this study more realistic, the standard closure of the GTAP is altered by changing the assumption of full-employment for skilled and unskilled labour. This study begins with the GTAP 8.2 Data Base (Narayanan et al., 2012) with a base year of 2007, aggregated to the set of regions and sectors specified in this article. We collected data on GDP, bilateral merchandise trade and tariffs for the year 2011 from the World Bank dataset, UN COMTRADES and ITC MacMAP, respectively, and then aggregated it to these sectors and regions. The ITC MacMAP dataset accounts for all the tariff preferences, FTAs and PTAs that were in effect in the year 2011, all over the world.
We then updated the data components in our dataset to the 2011 levels, by using Altertax closure and parameters (Malcolm, 1998). GDP is targeted by letting the sectoral outputs get updated; trade is targeted without affecting the tariffs, while tariffs are updated separately. The GTAP model has ‘technological change’ variables, which absorb these changes in the data during the Altertax simulation. These variables are exogenous typically for the policy simulations and act as the endogenous switch variables in the data-updating simulations. These simulations are and have to be different from policy simulations, since their only purpose is to update the relevant components of the dataset and not evaluate any policy impact. These assumptions ensure that the targeted components of the data base are updated, but other components of the data remain as undisturbed as possible.
Sector Aggregation
Sector Aggregation
The implications of reducing tariffs across different sectors would vary between regions, as various regions have a comparative advantage in different commodities. Similarly, the effects of regional integration on welfare and macroeconomic indicators would be varied due to different socio-economic conditions prevailing in these regions. Five different scenarios of complete integration in terms of tariff reduction between different regions are simulated using the GTAP model. Under each scenario, tariffs among members of regional integration (each scenario of Table 3) are removed but maintained for other regions. The tariffs faced by India in different regions across all sectors are given in Table 4; barring a few specific sectors in specific countries, India faces reasonably low tariffs across its partners.
Experiment Design
In this section, we discuss the results of our analysis. First, we look into the macroeconomic and more aggregate sectoral results in sub-section 3.1 and then focus on India’s bilateral exports, imports and trade balance in specific important sectors in sub-section 3.2.
Aggregate Global Results
In the GTAP model, tariff elimination leads to a reduction in the domestic market prices of imports. This results in price-induced rise in demand for imports by firms for intermediate inputs, private households as well as government. Cheaper imported intermediate inputs for firms may also reduce the cost of production across the spectrum of commodities. Further, reduced demand for domestic production may result in an excess supply situation, which can be rectified by the reduction of market prices to reach equilibrium. In bilateral terms, when an importer reduces tariffs on many or all of its partners, the degree of increase or decrease of imports from each of them would depend on two opposite effects—trade creation enabled by overall expansion in demand for cheaper imports, and trade diversion created by the expansion of exports by partners facing higher tariff reduction at the cost of others, accomplished in terms of a response to price differentials. This is similar to the income and substitution effects in standard microeconomic theory. This is the major mechanism that affects bilateral trade, which adds up to sectoral consumption, which, in total, equals output.
All these sector-specific results add up to the macroeconomic results. Table 8 shows the GDP and welfare results of several countries. In the GTAP model, welfare changes are measured in equivalent variations (EV). This is the amount of money consumers in any region would pay rather than face the changes in prices and quantities resulting from the simulations.
Tariff Faced by India in Different Regions
Tariff Faced by India in Different Regions
Table 5 shows that India loses in terms of GDP in some scenarios, including those in which it reduces tariffs, but gains in welfare when it reduces its tariffs. When China reduces its tariffs, it enjoys an increase in both GDP and welfare. Welfare gains may be traced back largely to the increased ability to allocate resources across sectors, thereby raising the efficiency effects. Canada, US, Chile, Japan, Malaysia, New Zealand, Singapore and Vietnam gain in terms of welfare and GDP in all scenarios; Korea and Australia have similar results, but they are exceptions in that the former loses in terms of GDP and welfare and the latter loses in terms of GDP alone, in the first scenario. The world as a whole gains in terms of both GDP and welfare in all scenarios. Japan and USA emerge as the biggest winners in terms of both GDP and welfare in all scenarios (Table 5).
Table 6 delves deeper into the welfare results for India. The first component is allocative efficiency, which is the measured change in the ability to efficiently allocate resources across sectors in the economy. Mathematically, this is just a collection of changes in the tax revenue of a regional household, which represents the government of a country in the real world. Given that India’s imports fall in the first three scenarios, these revenues also fall, as tariffs are unchanged, implying negative allocative efficiency effects. The endowment effect, the second component in this table, measures the increase in the wage bill caused by changes in employment. Given a fall in employment in the scenarios that involve no tariff reduction by India, the numbers are negative in the first three scenarios, while they are positive in the last two. The terms-of-trade effects show that India loses a little in the first three scenarios, but a lot in the last two, owing to cheaper import prices (than export prices) in India arising from tariff elimination. The difference between investment and savings in a country, adjusts to equate the real trade balance. This explains the last component, namely, the investment-savings effect, which is negative in all scenarios, more so in the last two scenarios, moving in line with the trade balance. In summary, despite the negative effects from a loss in terms of trade, India gains in welfare due to tariff elimination, because of increased allocative efficiency.
Investigating the reasons for the decline in India’s GDP in all scenarios, we learn from Table 7 that it is predominantly due to a decline in the consumption of domestic commodities and, to an extent, investment, although there is an expansion of the trade balance in all scenarios. The major driver for a decline in local consumption of domestically produced commodities is the increase in both imports and exports in the scenarios where India cuts tariffs. Thus, there is a potential for change in the production and consumption structures; more of production is exported than in the base case and more of consumption is imported. In the scenarios that involve no tariff change by India, owing to relatively reduced global prices, output in India goes down slightly in many sectors (as seen in Table 12). This results in reduced consumption and investment as well.
Changes in Gross Domestic Product and Welfare Effects (US$ million)
Welfare Decomposition for India (in US$ million)
Changes in GDP Components in India (in US$ million)
We focus on these exports, imports and trade balance in Tables 8 and 9. As expected, all countries including India witness a flooding of imports when they eliminate tariffs. Due to competitive prices from imports, production becomes cheaper in these countries, resulting in increased exports as well. India is no exception to both these effects in the last two scenarios that involve its tariff elimination. Further, while the aggregate trade balance for India has been positive in all scenarios, the situation is different for different sectors depending on the extent of tariff changes and economic structure (as we will discuss in sub-section 3.2).
Aggregate Exports and Imports: Changes (US$ million)
In value terms, changes in aggregate exports and imports are very similar, when all tariffs are eliminated, for a few, but not all, countries (e.g., Australia, Canada, Malaysia and Mexico). The explanation for this is as follows: cheaper imports mean an increase in import demand and also cheaper imported inputs for production, reducing prices in the importing country. This, in turn, enhances the competitiveness of exports, which also increase at an aggregate level. Given the steep fall in tariffs, the rise in exports and imports is high. The extent of the rise in both exports and imports depends on relative changes in prices in different sectors driven by tariff reduction. This is why aggregate exports and imports are similar for a few countries and different for others.
Aggregate Trade Balance: Changes (US$ million)
Until now, we have analysed the overall and global results in the macroeconomic and specific sectors. Now, we turn our attention to India’s results, focusing on a few sectors. Further, so far, we have looked at changes in the value of various variables, which include the total price and quantity effects. Some of the next few tables show the percentage changes in quantities and prices. While tariff changes affect directly the bilateral trade of specific sectors, the overall effects on aggregate trade in all sectors of the economy is of interest to policy makers.
Table 10 provides the results in this regard, in percentage change terms for quantities and not in values, which means that the changes in prices are not taken into account. This also explains why these numbers suggest a story that is different from the other results in value terms. An overarching trend here is that the exports grow but not as much as imports, across the board. Notable exceptions to this trend include wheat, sugar, fisheries, and the extraction and auto industries. Table 11 provides a good reason for this trend; export prices do not fall, if at all they do, to the extent that import prices fall, again if they do. Therefore, exports are relatively more expensive and hence increase less than imports. For the exceptions, the price equation is reversed; export prices fall more than import prices, implying that exports are more competitive and hence increase more than imports. The reason why such a possibility may occur is that intermediate imports have become quite cheap after tariff reductions (e.g., fertilisers within the light manufactures are cheaper by 3–5 per cent, and are needed to produce wheat), reducing production costs and hence the export price, despite no reduction in import tariffs (on wheat in this example—under 0.5 per cent).
Aggregate Exports and Imports for India
Aggregate Exports and Imports for India
Aggregate Export and Import Prices for India
For many agricultural products, India’s trade balance improves with a tariff reduction, except in a few sectors, where it deteriorates steeply (Table 12). Overall, India does gain in total trade balance, but less so when she reduces her own tariffs. The inclusion of Korea and China in the TPP does raise India’s trade balance, since global tariff reductions are much higher as a result, thereby reducing the import prices of many intermediate inputs leading to cheaper and, consequently, expanding exports.
Mixed prospects in terms of output in many sectors are seen in Table 12, when India eliminates its tariff. In a few sectors such as wheat, sugar, vegetables and processed food, output declines across all scenarios; the decline is steeper when India cuts tariffs. In contrast, in certain sectors such as dairy, fish, meat/livestock, textile products, leather, and light and heavy manufacturing, the decline (or small increase) in output if India is excluded from the TPP, is replaced with a significant increase when India joins the TPP.
A word of caution is needed while interpreting these results. India may face challenges in terms of sanitary and phytosanitary standards and other non-tariff barriers, which can curb the expansion of exports and the output of dairy, fish and meat/livestock shown in this study, as we focus only on tariff barriers.
In this section, we have a closer look at sector-specific results. Tables 13 and 14 summarise the effects of tariff elimination on India’s imports of a few commodities from various countries. We chose these products for this analysis, for a couple of reasons. First, among all the commodities considered in this study, these products experience substantial changes; second, sectors such as processed food products and textile products are vital in the Indian economy on account of employment.
Overall Trade Balance and Output for India
Changes in India’s Imports of Food and Textiles (US$ million)
For all these products, India imports less if it does not join TPP and imports more if it does. In the first three scenarios, India’s imports from all countries except Korea and Malaysia, fall slightly, except those from Japan. In contrast, in the last two scenarios, which involve India’s participation in the TPP, the import changes are largely positive, except in regions like the rest of the world, which neither reduce tariffs nor face tariff reduction by India, due to the diversion of trade away from them to the TPP partners. In the following paragraph, we shall first attempt to explain this overarching result and then move on to these exceptions.
Changes in India’s Imports of Other Manufactured Products (US$ million)
The general trend of small negative changes in India’s imports when India does not participate in the TPP, can be explained by the slightly negative changes to aggregate import demand in India; in other words trade creation (captured by the first square-bracketed term in equation 1) in India is negative, albeit small. Since India does not reduce tariffs in these scenarios, the prices of imports in India’s domestic market hardly change, resulting in a small reduction in import demand. Trade diversion (captured in the second square-bracketed term in equation 1), has not much of a role in these scenarios since all tariffs are unchanged. When India eliminates her tariffs on imports from other TPP partners, however, there is a huge reduction in prices, resulting in the expansion of import demand from all partners.
For the first three scenarios for food products, India’s imports from Malaysia and Japan increase despite the overall negative trend. These include vegetable oils, which is a major commodity exported from Malaysia to India. This is because the tariff reduction in these countries is so high that their domestic market prices fall a lot, 2 resulting in a reduction in their export prices as well. Thus, in spite of not reducing tariffs, India faces a reduction in the prices of these imports, whose pre-tariff prices decline due to a fall in export prices. Imports from Korea follow the same trend in the third suite which includes Korea in the TPP. There is a trade diversion effect in favour of these countries (Malaysia, Japan and Korea), which also partly explains the small reduction in imports from other countries in these scenarios.
The initial trade and tariff structure can explain most of these results. Japan’s processed food exports is about 6 per cent of global processed food exports, which makes it a significant global player in this sector. Among India’s total imports of food products, however, Japanese contribute 0.3 per cent, while Malaysia contributes 16 per cent. Japan, Malaysia and Korea have high tariffs on agricultural sector; therefore, tariff elimination across the board means a much steeper tariff reduction in the food sector than in others, including textiles and other manufacturing, implying a higher reduction in prices in the food sector, resulting in higher favourable trade diversion in the food sector. This explains why we see a reduced increase or even a reduction in imports by India in most non-food sectors; exceptions to this rule are sectors wherein the initial tariffs are higher than in the food sector, such as Malaysia’s heavy manufacturing sector. Comparing the last two scenarios, we can infer that the inclusion of China alongside India in the TPP may result in higher imports in India, since China would also grow more competitive due to tariff elimination.
Tables 15 and 16 summarise the results in terms of changes in exports from India. For all products, it is clear that India’s joining the TPP can help raise India’s exports to the world, while for food and textile products, India may even lose if it does not join the TPP. The reason for the poorer export performance in the scenarios in which India is not part of TPP is that the trade is diverted away from India owing to its higher relative export prices as a result of the higher relative import prices for all commodities. In other words, no tariff reduction in India means that import prices and hence market prices do not fall, resulting in the same or higher export prices; while for the TPP partners prices fall due to tariff elimination and hence relatively the price reduction is much higher in these countries. Thus, all importers shift away from India and towards these the TPP partners.
The inclusion of both India and China in the TPP enhances India’s exports further, as seen in the last scenario, in all sectors shown here, except textile products. Possibly, higher initial tariffs in India than in China may lead to a higher reduction of prices due to tariff elimination, and hence a favourable trade diversion against China. In terms of the trade balance, which is the result of changes in both exports and imports, India may gain by joining the TPP in textile products and other light manufacturing, while losses in the trade balance are much higher in food products and heavy manufacturing (Tables 17 and 18).
Tables 19 and 20 illustrate the analysis for changes in exports and imports in selected scenarios and partner countries, as a result of the tariff changes modelled. Textile exports of India to the US decrease when India does not cut its tariffs (TPP3), while it increases when India cuts tariffs (TPP4). In both cases, there is trade created (term 1 in equation 1) in the US import market, although less so in TPP4; however, since the US cuts tariffs on India’s exports in TPP4, there is a huge diversion of trade from other countries in favour of India. This phenomenon is shown in the equation below and illustrated in columns 4–8 of Table 19:
where, qxs(i,r,s) (column 4) and pms(i,r,s) (column 8) are percentage changes in quantities and prices of bilateral imports of commodity ‘i’ from region r to region s and qim(i,s) (column 5) and pim(i,s) (column 7) are those in total quantities and prices of aggregate imports of commodity ‘i’ by region s, respectively; ESUBM(i) is the (Armington) elasticity of substitution among imports from different sources for commodity ‘i’.
Changes in India’s Exports of Food and Textiles (US$ million)
Changes in India’s Exports of Other Manufactured Products (US$ million)
Changes in India’s Trade Balance of Food and Textiles (US$ million)
Changes in India’s Trade Balance of Manufactured Products (US$ million)
Analysis of Changes in India’s Exports (percentage)
Analysis of Changes in India’s Imports (percentage)
The first term, showing the change in imports in the destination (column 5) shows the extent of trade created overall due to a given tariff reduction, while the second term captures the substitution between different sources, in terms of the price differential between the exporter concerned and total imports; in other words this is the extent of trade diverted from other sources to the one of interest: India in our example. Another instance of the trade diversion effect, away from India, overwhelming the trade creation effect is India’s processed food exports to Korea, despite getting a bit subdued when India cuts tariffs. For the exports of light and heavy manufactures, from the US and Japan, respectively, trade creation is complemented by favourable trade diversion for India when it cuts tariffs; when it does not, trade diversion acts slightly against trade creation but still the latter wins. The trade diversion effect is driven by the differential between aggregate import prices in the destination and bilateral import prices of exports from India to the corresponding destination (columns 7 and 8).
Changes in bilateral import prices are driven by changes in tariffs as well as those in the CIF prices of imports from the source country India (column 9), which are in turn derived largely from changes in FOB prices (column 10) therein, given that transportation prices do not change so much. This price linkage aspect is shown in equation 2, where tms(i,r,s) and pcif(i,r,s) are percentage changes in tariffs and CIF prices of bilateral imports of commodity ‘i’ from region ‘r’ to region ‘s’:
FOB prices are largely determined by market prices, which are mostly the result of an adjustment between output supply and demand to clear the market for all commodities. When India does not cut its tariffs, output goes down or remains constant in the illustrations in Table 19 (column 12). When it cuts tariffs, output goes up in all examples except processed food. On the demand side, domestic demand decreases or does not change in all cases except in the case of textiles and apparel (column 13), wherein firms demand more for catering to increased exports when India cuts tariffs (column 16); on the hand, exports of textiles and processed food decline a lot while they remain stagnant in light and heavy manufactures, when India remains out of the TPP (column 16). In all cases, market prices in India fall, but more so in the scenarios involving India’s tariff reduction. Every scenario involves tariff reduction in some of India’s trading partners and hence there is a situation of excess supply or reduced demand, resulting in a reduction of market prices to equilibrate.
Table 20 traces the story pertaining to imports by India. As expected, for all sectors, India’s imports flood in when it cuts tariffs on processed food, textile products, and light and heavy manufacturing from Malaysia, China, Japan and the US, respectively, facilitated by both trade creation in India and trade diversion, stemming from a reduction in prices as a result of tariff elimination. Import prices in India fall in all scenarios and sectors shown in the table, more so in the ones in which India cuts tariffs. Market prices also go down in all scenarios, while output in India increases in all sectors except processed food. Most of the increase in output comes from export expansion; in the case of processed food, the reduction comes from domestic demand contraction.
This study used the GTAP model on 18 tradable commodities and 16 regions of the world to understand the likely impact of the recently concluded TPP agreement on the Indian economy. This study updates the GTAP database to the 2011 levels and analyses the likely impact on welfare, macroeconomic variables and output, employment and trade indicators. Five different scenarios of complete integration in terms of tariff reduction between different regions are simulated using the GTAP model. Under each scenario, tariffs among members of regions are removed but maintained for other regions. Although it is unlikely that an agreement would result in the complete removal of tariffs on all products listed in national tariff lines, this experiment provides the maximalist situation of tariff liberalisation. However, eliminating tariffs on all products in each scenario cannot be a real situation as in almost all FTAs, each partner has a sensitive or exclusion list covering products on which tariffs are not liberalised.
This study does not adequately capture service trade reforms and thus the results may underestimate the potential effect of liberalisation, where the services sector is to be included. It is to be noted that the GTAP model has both static and dynamic versions. However, in this paper, the static version of the standard GTAP model is used. Gilbert (2013) mentioned that the static model has disadvantages relative to the dynamic techniques, of not describing the time path, that is, attention in the analysis is concentrated on the end-outcome rather than the transition. Data aggregation is an issue, since the results may be different if one does a detailed sectoral and country-level analysis. For the model in general: market structure (perfect competition, uniformity of functions across sectors and regions, etc.) is too simplistic in the standard GTAP model. Studies that do incorporate imperfect competition tend to generate welfare estimates that are roughly double those of competitive models (Gilbert, 2013). This study presents a largely conservative outcome as it only considers merchandise trade liberalisation and ignores non-tariff barriers.
In this analysis, we have outlined the overall winners and losers of the various possible and hypothetical combinations of the TPP; the actual agreement did not comprise India, China and Korea, but we included them in some of our scenarios. Countries like Japan, Korea and Malaysia have a win-win situation in all scenarios that include their tariff reductions. However, we also find that India has mixed fortunes at stake here. Tariff elimination by India results in lower GDP due to a decline in consumption and, to an extent, investment. In crucial sectors such as food products, wheat and sugar, India loses whether or not it joins the TPP given an opportunity in future, despite the recent conclusion of a deal, due to strong trade diversion effects arising from global price reduction facilitated by widespread tariff elimination. However, in certain sectors such as textiles and leather, the decline of output and a negative trade balance if India does not join the TPP gets reversed under scenarios of it joining the TPP. Adverse effects on agricultural sectors seen in this article are likely to be more negative in reality if non-tariff measures are taken into consideration. Therefore, there is no strong reason for India to pursue being part of the TPP in the future, even if there is an opportunity to enlarge the partnership despite the current deal.
Total Bilateral Trade Patterns (US$ million)
