Evaluation of the Impact of Regional Trade Agreements on Indian Exports

Abstract

This article scrutinises the repercussion of the regional trade agreements on Indian exports. The gravity model has been used for the analysis of two decades of panel data from 30 countries, spanning from 2001 to 2019. The gravity equation is estimated by the Ordinary Least Square (OLS) and Poisson pseudo-maximum likelihood (PPML) techniques using panel data regression. The PPML approach addresses the heteroskedasticity bias that is typically present in the OLS method. The study has revealed that the PPML results are more encouraging than those obtained based on the traditional OLS approach. According to the empirical findings, India’s trading partners in South Asia benefit more from its economic integration than India does. However, the measurement of country-specific degrees of globalisation has a negligible effect on fostering trade across select nations.

JEL Codes: C1, F1, F2, F6

Keywords

Free trade agreement gravity model KOF globalisation index Asian integration process

1. Introduction

The promotion of economic growth and development is greatly facilitated by international trade and has grown significantly over the last few decades (World Bank, 2021). The coordinated endeavour of several nations pursuing regional economic integration is primarily responsible for this progress. The two catastrophes from history, viz., the Great Depression of 1930 and the Second World War, have made the world realise that only a globally coordinated economic recovery could bring the world economy in the right direction (Pal, 2014). Following these traumatic events, most nations opted to implement multilateralism through the General Agreement on Tariffs and Trade (GATT). However, the GATT had its own deficiencies as pertinent trade-related issues were not included in its ambits, such as services, textiles and clothing and agricultural trade. Member nations went through eight rounds of trade negotiations to resolve these issues. Thereafter, the World Trade Organization (WTO) was founded in January 1995, post the last round of GATT negotiation, viz., Uruguay round. Along with multilateralism, the WTO members were also allowed to create Regional Trade Agreements (RTAs) under certain conditions depending on the level of integration sought.

The RTA is a framework for fostering deeper integration on a broader basis including competition, investments, preferential market access, environment, economies of scale in production etc. India as a developing country has largely pursued economic integration through the WTO’s multilateral trading system. However, when the Doha Round discussions stalled and the trading system weakened, countries turned to preferential trade agreements as an alternative. India has signed many bilateral and multilateral trade agreements to diversify export markets and provide access to necessary intermediates, capital goods and raw materials. In this paper, we have taken three FTAs that India has signed with its neighbouring nation, viz., AIFTA, India–Sri Lanka FTA (ISFTA) and South Asian Free Trade Agreement (SAFTA).

After the economic liberalisation in 1991, India began to open up its economy to trade and investment and explored new sectors for trade and initiated RTAs with its neighbouring and developing nations. India launched the Look East Policy (LEP) in 1991, which emphasised India’s active engagements with Southeast Asia. The LEP marked a shift in India’s foreign policy from security to economic issues, particularly with its eastern neighbours. The LEP was upgraded to Act East Policy in 2014 in order to reach out to a larger Asia-Pacific region. In August 2009, the Association of Southeast Asian Nations (ASEAN) and India signed an FTA in goods (AIFTA).

Table 1.

Trend in Trade in Pre- and Post-AIFTA.

	Pre-AIFTA					Post-AIFTA
	2005–2006	2006–2007	2007–2008	2008–2009	2009–2010	2010–2011	2011–2012	2012–2013	2013–2014	2014–2015
Export	10.41	12.60	16.41	19.14	18.11	25.63	36.74	33.00	33.13	31.81
growth	23.56	21.09	30.19	16.62	−5.37	41.48	43.38	−10.1	0.38	−3.99
Import	10.88	18.11	22.67	26.20	25.79	30.60	42.15	42.86	41.27	44.71
growth	19.41	66.38	25.22	15.56	−1.55	18.64	37.74	1.68	−3.71	8.33
Total trade	21.29	30.71	39.08	45.34	43.90	56.23	78.89	75.86	74.4	76.52
Trade balance	−0.47	−5.51	−6.26	−7.06	−7.68	−4.97	−5.41	−9.86	−8.14	−12.9

Source: Export–Import Data Bank, Ministry of Commerce and Industry, Government of India, Ministry of Commerce and Industry (2022).

Note: Export, import and trade in US$ billion and growth in percentage.

Table 1 presents pre- and post-AIFTA trade data. It can be seen that the trade balance has worsened in the post-AIFTA phase. In 2021–2022, India’s trade deficit has raised to US$ 24.75 billion. Changes in import tariffs have little impact on imports and special product group, where tariff reductions are relatively minimal, has seen the greatest increase in India’s imports from ASEAN. When compared to ASEAN members, India’s tariff reduction is substantial under this FTA.

The AIFTA has elicited a variety of responses from various interest groups. The literature review reveals that relatively little research has been undertaken with post-agreement data. The studies of Sikdar and Nag (2011) and Veeramani and Saini (2011) are based on pre-agreement data before 2009. Several studies are piecemeal, focusing on specific goods such as plantation commodities (Jagdambe & Mouzam, 2019) and fisheries (Ratna & Kallummal, 2013).

ISFTA was enacted on 1 March 2000. Twenty-two years have passed since the ISFTA began its operation. Although it has numerous accomplishments to its credit, there are still challenges. Both sides have yet to fully realise the FTA’s full potential. Between 2000–2001 and 2018–2019, India and Sri Lanka’s bilateral trade increased by almost nine times. In 2018–2019, the value of bilateral trade was US$ 6.2 billion.

India ratified the SAFTA under the auspices of the South Asian Association for Regional Cooperation (SAARC) in 2006 together with Pakistan, Bhutan, Bangladesh, Maldives, Nepal, Sri Lanka and Afghanistan in 2007. However, the trade within the SAARC countries is not very impressive. About 5% of the region’s overall trade is from within the SAARC, and less than 1% of India’s total imports come from South Asia. The reason for the lack of integration between the member countries is that the region’s two main economies, namely, India and Pakistan, have tense ties, which has hindered the successful implementation of many trade facilitation initiatives (Taneja et al., 2013).

India has signed several RTAs which are crucial to improve its trade and investment. India currently has 42 trade agreements that are in force or are in the tendering stage. At a time when India is negotiating FTAs with several countries and groupings, it is critical to examine the growth of trade between India and its main FTA partners. India’s ongoing challenge is to strike a balance between advancing local markets’ ability to act as global exporters and maintaining the benefits of global economic integration. To understand the effect of FTAs and to determine how the various trade variables affect trade, the gravity model was applied in this study.

In addition to the conventional gravity variables, this study employs the KOF globalisation index and its proxies, such as the Economic Globalisation Index and International Trade Globalisation Index. Globalisation is a trend that lessens national borders and integrates global economies. According to our hypothesis, the level of globalisation in different countries may have a distinct effect on commerce. The KOF globalisation index is amply used in the academic literature as a reliable indicator. This index, developed by Dreher (2006), assesses the level of globalisation on a scale of 1 (least) to 100 (most globalised). The KOF globalisation index is notable because it combines various factors that measure various facets of globalisation into a single index. The index relies on 43 separate variables that are combined to create the various dimensions, with the overall index being generated annually for 203 nations and territories from 1970 to 2019, and users can select the level of quantification that is most suited to their particular purpose from a total of 27 different indices that are published.

2. Review of Literature

2.1 India’s RTAs

The international economic theory suggests that deeper economic integration offers long-term advantages in terms of effective resource allocation and welfare gains. In contrast, when domestic manufacturers do not face intense competition, adverse repercussions could happen in the short term (Plummer et al., 2010). The study conducted by Sikdar and Nag (2011) revealed that the world suffered significant losses of market share in the ASEAN and India in the post-AIFTA phase. China is particularly impacted by a decline in market share in Malaysia, Thailand, the Philippines, Vietnam, Cambodia and India. The developing nations of South Asia, particularly Bangladesh, are experiencing a similar effect from the FTA. Saraswat et al. (2018) evaluated the effects of FTAs on various industries in their study. According to the findings, the ASEAN–India FTA has decreased the quality of trade. In addition to the rise in the trade imbalance due to the reduction in tariffs, a closer examination of sector-specific trade flows also reveals a bleak picture. The Harmonised System (UN) of product classification divides items into 21 sections and 99 chapters, including categories such as chemicals, textiles, diamonds and jewellery. According to this paper, in 13 out of the 21 sectors, trade balances have gotten worse.

The ISFTA was India’s first bilateral FTA. Reflecting on the ISFTA, Varma and Abhayaratne (2016) revealed that trade creation was more positively affected by the FTA between India and Sri Lanka than trade diversion. Consequently, welfare increases are visible. The study concluded that the ISFTA might be advantageous to its participating nations as well. A paper on the ISFTA by Kelegama (2014) focused on the growth of vanaspati and copper, as well as other implications of ISFTA, with particular attention paid to the third wave of Indian investment in Sri Lanka after 2000.

In 2006, the SAFTA was established, although it has had limited success. A quantitative evaluation of the SAFTA’s desirability was conducted by Bandara and Yu (2003). According to the findings of the study, unilateral trade liberalisation increased the efficiency in all nations in the area, but India got the most advantage because of its initially high tariff levels and sizeable manufacturing sector. Another study by Kaur and Nanda (2010) assessed India’s export potential with other SAARC countries. According to the study, India has export potential to SAARC nations including Bhutan, Pakistan, Maldives and Nepal. India is a key market for goods for the other SAARC members and is a source of prospective investment and technological advancement. Therefore, supporting SAARC is fundamentally in India’s best interests.

2.2 Gravity Model

The gravity model is commonly known as a workhorse in international trade analysis and is widely used by researchers to describe the linkage between bilateral trade and foreign direct investments. Tinbergen (1962) used the gravity framework for trade analysis, which was derived from Newton’s gravitational theory and applied in their study to determine the scope of bilateral trade flows between any two nations. Nevertheless, there are several empirical works of literature that provide methodological advances to illuminate policy implications on trade flows. The gravity approach is particularly effective for understanding and projecting the effects of regional economic integration on bilateral trade.

Typically, the conventional gravity framework has been employed to assess the influence of GDP, geographical distance between countries and many dummy factors, such as common borders, common official language, colonial link and FTA. Since the conventional theory of gravity cannot handle multilateral resistance terms (MRT), the model has come under criticism for yielding biased assessments of the variables in trade flows. The exporter and importer fixed effects in the gravity equation is a typical method for solving these unobservable MRTs (Olivero & Yotov, 2012). Numerous approaches have been implemented by researchers to overcome this problem. For instance, Alvarez et al. (2018), developed a new variable that combines the values of variables on both the importer and exporter sides, allowing them to be identified even when country-specific fixed effects are present. The drawback of this technique is that it does not offer the direct identification of country-specific factors on bilateral trade, making interpretation of the elasticities difficult. Other research has directly analysed the effects of country-specific factors without explicitly considering MRT. According to Bergstrand et al. (2015), intra-national trade, combined with international trade in the gravity model, encompasses globalisation influences, such as innovation and technology, and so aids in getting an unbiased evaluation of the impact of RTAs on trade flows. In the gravity model, another issue is how to manage a year where there is no trade (zero trade) between two countries. This impacts all gravitational exercises and is equally a measuring issue as well as an estimating one. The levels of trade may be estimated directly from the non-linear form of the gravity framework using a Poisson pseudo-maximum likelihood (PPML) estimator. Despite the prevalence of heteroskedasticity which is a common issue in trade data, the PPML is a reliable method (Silva & Tenreyro, 2006).

3. Data and Methodology

3.1 Data Description and Sources

The International Trade Centre provided the bilateral trade data for the period 2001 to 2019. The World Economic Outlook database of the International Monetary Fund (2022) has been used to obtain GDP. Distance (great circle distance formula), border, language and common colony data are provided by the Centre for Prospective Studies and International Information (CEPII) (Conte et al., 2022) (Head & Mayer, 2014). Data on KOF globalisation index were drawn from Gygli et al. (2019).

3.2 Econometric Specifications

The basic step in the estimation process for the gravity equation (1) is to take the natural logarithms of all the variables and create a log-linear equation which will be estimated using Ordinary Least Squares (OLS) regression.

\ln X_{i j, t} = β 0 + β \ln Y_{i} + β 2 \ln Y_{j} + ϕ_{i j},

(1)

where, at time t, Xij is the monetary value of exports from I to j. β0 is a constant. Yi stands for exporter-specific factors (i.e., GDP) which describe the total amount of supply that exporters are ready to offer, and Yj represents importer-specific factors (i.e., GDP) which describe the total demand of importers. At the last, φ shows how simple it is for exporter i to reach market j.

Trade costs are often captured using several variables. Empirical studies commonly use bilateral geographical distance as a proxy for trade costs. However, we have added more factors to our analysis. The dummies for shared borders, common languages, common coloniser, landlocked nations and the FTA variable are among them.

\begin{matrix} \ln X_{i j, t} = β 0 + β 1 \ln {GDP}_{i t} + β 2 \ln {GDP}_{j t} + β 3 \ln {Distance}_{i j} + β 4 Common {Border}_{i j} \\ + β 5Common Language + β {6Colonial  Link}_{i j} + β 7 Landlocked \\ + β 8 AIFTA + β 9 SAFTA + β 10 India Sri Lanka FTA + ε_{i j t} \end{matrix}

(2)

These variables illustrate the hypothesis that when the GDP of the reporting nation rises, more production occurs and raising the number of goods accessible for trade. Therefore, we can anticipate that the reported country’s GDP will have a positive coefficient sign. The partner country has more purchasing power because of its high GDP. As a result, there is a positive correlation between trading partners’ GDP.

Given that the distance factor is a measure of potential trade friction, the predicted coefficient sign of distance will be negative. The cost of transportation rises with distance and is greater for islands and landlocked nations while being lower for nearby nations. The geographical closeness between the two countries will favourably affect trade flows. The common border dummy variable is used to represent physical closeness. Trade is more desirable between two economies that are close to one another than between those that are far apart. This will lower the cost of trading with one another. Therefore, the common border coefficient ought to be positive.

Information costs are recorded using dummies for a common language or other pertinent cultural traits, including colonial history. Trade between nations whose business methods, competitiveness and dependability of delivery are well known to each other probably involves less search cost. Companies operating in more comparable contexts than those in neighbouring nations, nations with a shared language, or those with other significant cultural traits are likely to know more about one another and have a better understanding of one anothe’’s business practices. As a result, businesses are more inclined to look for suppliers or clients in nations where their current business environment is secure. The projected coefficient for the colonial relationship and shared language component is expected to be positively correlated. Multilateral trade agreements promote trade more than bilateral trade agreements, given the positive and significant regional dummy factors. The research study has included dummy variables for India’s three FTA groups, and these are AIFTA, SAFTA and India’s FTA with Sri Lanka.

Along with these variables we have included nation-specific and country-pair fixed effects, drawing from the recent advancements in the gravity study. With the availability of a complete collection of country-specific and country-pair fixed effects, we may get a more thorough and nuanced estimate of the impact of the KOF indexes on trade between India and its trade partners. The variables $E K O F G I i$ , $E K O F E c G I i$ and $E K O F T r G I i$ represent the exporter’s overall globalisation, economic and trade KOF indices, respectively. Similarly, $I K O F G I j$ , $I K O F E c G I j$ and $I K O F T r G I j$ represent the importer’s overall globalisation, economic and trade KOF indices, respectively.

\begin{matrix} \ln X_{i j, t} = β 0 + β 1 \ln {GDP}_{i t} + β 2 \ln {GDP}_{j t} + β 3 \ln {Distance}_{i j} + β 4 Common {Border}_{i j} \\ + β 5 Common Language + β {6Colonial Link}_{i j} + β 7Landlocked \\ + β 8 AIFTA + β 9 SAFTA + β 10 India Sri Lanka FTA + β 11 {EKOFGI}_{i, t} \\ + β 12 {EKOFEcGI}_{i, t} + β 13 {EKOFTr}_{i, t} + β 14 {IKOFGI}_{j, t} \\ + β 15 IKOFE c {GI}_{j, t} + β 16 {IKOFTrII}_{j, t} + +_{i j t} . \end{matrix}

(3)

4. Results and Discussion

The study has used the PPML estimate to evaluate the result to deal with zero trade observations. The monetary value of export from i to j serves as the dependent variable in this study, designated as Xij. The estimated results are presented in Table 2. The GDP coefficient for the reporting country is positive at 0.23 which means that a one-unit rise in GDP would typically increase exports by 0.23 units, demonstrating that economic size significantly affects bilateral commerce. Similarly to this, the GDP coefficient for the partner nation is positive and significant. The GDP coefficient of the partner nation is 0.36, which means that a change of one unit in the partner nation’s GDP would typically result in a change in the export of 0.36 units. This suggests that bilateral trade flows and national income have a favourable connection. The studies of Josic and Zmuk (2020) supported a similar hypothesis.

Table 2.

Estimation Results of the Gravity Model.

IndependentVariables	(1)Pooled OLS	(2)Fixed Effect	(3)GLS Random Effect	(4)PPML
Log GDP_it	0.237(0.657)	0.255(0.210)	0.248(0.211)	0.229(0.826)
Log GDP_jt	−0.160**(0.0558)	0.277***(0.0733)	0.244***(0.0695)	0.358***(0.0667)
Log DISTANCE_ij	0.0700(0.123)		−0.0264(0.504)	0.477***(0.127)
Common Border_ij	1.539***(0.213)		2.210**(0.843)	2.062***(0.170)
Common Language_ij	−0.215(0.149)		0.259(0.648)	−0.0385(0.145)
Colonial Link_ij	−0.346**(0.123)		−0.739(0.558)	0.468***(0.129)
Landlocked Country	−0.868***(0.192)		−1.416(0.882)	0.124(0.268)
AIFTA_ij	−0.306*(0.151)	0.202***(0.0607)	0.202***(0.0606)	−0.571**(0.180)
SAFTA_ij	0.548**(0.208)		0.198(0.928)	−0.0136(0.199)
ISFTA	0.732*(0.312)		1.924(1.520)	1.103***(0.168)
KOF Globalisation Index overall (Exporter)	0.0589(0.0896)	0.0572*(0.0285)	0.0569*(0.0286)	0.0893(0.113)
KOF Economic Globalisation Index (Exporter)	0.0312(0.0418)	0.0291*(0.0132)	0.0297*(0.0133)	0.0204(0.0619)
KOF Trade Globalisation Index (Exporter)	−0.0155(0.0208)	−0.0156*(0.00658)	−0.0153*(0.00662)	−0.0146(0.0322)
KOF Globalisation Index overall (Importer)	0.167***(0.00750)	0.0699***(0.00939)	0.0754***(0.00917)	0.0674***(0.00731)
KOF Economic Globalisation Index (Importer)	−0.127***(0.0143)	−0.00892(0.00781)	−0.0125(0.00775)	−0.0694***(0.0164)
KOF Trade Globalisation Index (Importer)	0.0855***(0.0109)	0.00501(0.00582)	0.00597(0.00583)	0.0623***(0.0144)
Constant	−5.739***(1.539)	−5.233***(0.484)	−4.837(4.297)	−10.83***(2.241)
R-squared	0.724	0.8561	0.8559	0.37010849

Source: Authors’ calculations.

Note: Standard errors in parentheses; * P < 0.05, ** P < 0.01, *** P < 0.001.

The coefficient of distance is statistically significant and positive. The distance coefficient is 0.47, which means that a change in distance of one unit between the reporter and partners would tend to boost export by around 0.47 units. This positive value contradicts the gravitation principle according to which closing the distance between the reporter and partner country, lowers the transportation cost. Several studies have shown that trade levels are unaffected by the distance between the two nations. As technology advances, costs have decreased, and efficiency has increased for commodities transportation in the long run. Another factor is that cutting-edge technology has been made more affordable by significant advancements in vessel intake and operational expenses, leading to lower transportation costs (Hummels, 2007).

According to the statistical significance and positive common border coefficient of 2.06 trading partners who share borders trade 684.59% more than partners who are far apart. This supports the hypothesis that if two nations shared a border, they would trade more than those situated far apart. The common official language has a negative coefficient, which means that the common official language between the reporter and partner nations does not affect commerce (Melitz & Toubal, 2014).

The colonial link coefficient, however, is statistically significant and positive. Landlocked has a small influence on commerce because of its positive and statistically insignificant coefficients. In the data, landlocked trading partners include Lao PDR, Afghanistan, Nepal and Bhutan. These nations’ negotiating leverage is severely hampered due to their weak domestic industry. Nepal and Bhutan are both landlocked nations that depend on India for trade-related access to the ocean. Appendix 1 presents the list of countries considered in this study.

The ASEAN–India FTA coefficient is negative at 0.57 and statistically significant. Findings indicate that except for AIFTA countries with whom the trade imbalance has substantially widened concerning other members and imports outweigh, India has been effectively boosting exports to other countries. There is concern that the accord may favour ASEAN more than India because India has committed to considerable tariff reductions as part of the agreement. The window for further trade expansion will get smaller after commerce between the two nations has stabilised. Policies to enhance trade through tariff reduction schemes lose their effectiveness when there is little scope for growth (Glorius et al., 2021). Exports from ASEAN to India may rise by 1.5% for every percentage point increase in GDP among the ASEAN nations. A rise in India’s GDP may lead to an increase in exports to ASEAN as well, but with a lower magnitude. A reduction in tariffs will result in a less than 1% increase in India’s exports to ASEAN. Similarly, SAFTA has a negative and statistically insignificant coefficient. The FTA between India and Sri Lanka has a positive coefficient of 1.10, which is statistically significant.

Overall, the KOF globalisation of exporters is positive and statistically insignificant, while the coefficient of the importer is positive and statistically significant. The KOF economic globalisation of the exporter is positive and statistically insignificant and for the importer, it is negative and significant. The coefficient of KOF trade globalisation for the exporter is negative and statistically insignificant, whereas the partner country’s trade KOF globalisation index is positive and significant. This suggests that globalisation is a decisive factor for export for all countries included in the study. This result also supports the study of Stavytskyy et al. (2019).

5. Conclusion

This article has made an attempt to develop a theory-driven, statistically viable and comprehensive estimation of trade flows between India and her trade partners. The gravity model has been used to evaluate India’s export to Asian countries. To deal with heteroskedasticity and zero trade data, the study has interpreted the gravity model result by the PPML estimation approaches. The GDP coefficient is positive for both the reporter and the partner countries. It shows a favourable association between the income of the nation and the volume of bilateral trade. The distance coefficient, which was found to be positive and statistically significant, defies the gravity hypothesis. The influence of the common language on trade is minimal, and the landlocked variable and the colonial connection coefficient are positive and statistically significant. Both the AIFTA and SAFTA have negative coefficients, thus indicating that they have a negative effect on trade, whereas the FTA between India and Sri Lanka has a statistically significant positive coefficient. The KOF globalisation index estimate has consistently shown relatively small effects on trade when used as an indicator of country-specific globalisation.

There are several areas where robust research is needed such as how developing nations may optimise the benefits of FTA, multilateralism, regionalism and preferentialism for their development. Prospective FTAs should be the subject of thorough cost-benefit studies. How would the nexus of multilateralism and regionalism be used as a development tool? For socio-economic development, developing nations must assure coherence and establish a constructive liaison between regionalism and multilateralism. The findings are important for understanding India’s unrealised export potential and evaluating the efficacy of programmes facilitating economic integration. Environmental issues, especially those related to the climate, are thorny issues in trade talks. Environmental rules and challenges are being actively included in FTA negotiations by developed nations like the US and the EU.

Appendix 1.

Countries Included in the Sample.

AIFTA	SAFTA	Rest of the Trade Partners
India	Afghanistan	United States of America
Cambodia	Bangladesh	Canada
Indonesia	Bhutan	Mexico
Malaysia	Maldives	China
Philippines	Nepal	Japan
Singapore	Pakistan	Korea Republic
Thailand	Sri Lanka	Saudi Arabia
Vietnam		Mauritius
Myanmar		Bahrain
Brunei		Oman
Laos		Qatar
		Kuwait
		United Arab Emirates

Source: Authors’ selection.

Footnotes

Acknowledgement

The authors are grateful to referee’s comments, which helped to improve the paper. Views are authors own. Usual disclaimers apply.

Declaration of Conflicting Interests

The authors declare that there is no conflict of interest.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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