3.1. Proliferation of RTAs and the WTO debate

The proliferation of Regional Trade Agreements (RTAs) since the 1990s has sparked significant debate in international trade policy, particularly regarding their alignment with the World Trade Organization (WTO) system [1]. Initially, discussions focused on whether RTAs acted as “building blocks” or “stumbling blocks” for the WTO. Recently, however, the emphasis has shifted towards ensuring that RTAs complement the multilateral framework of the WTO, rather than undermining it. Three main areas of contention persist: (1) many RTAs significantly exclude agriculture from liberalization, despite its critical role in global trade; (2) intricate and restrictive rules of origin within RTAs often complicate trade processes and can distort trade flows; and (3) the varied treatment of trade remedy laws across RTAs leads to inconsistencies with WTO rules.

The ongoing debate regarding RTA compatibility with WTO principles has prompted various proposals for reforming GATT Article XXIV. The literature highlights a need to balance reforms with the specific needs of developing countries, while addressing potential impacts on ACP (African, Caribbean, and Pacific) countries [2,3]. Proposals such as the Kemp-Wan compatibility criterion are suggested as alternatives to the current “catch me if you can” approach, potentially fostering a more cohesive multilateral trade environment. This approach acknowledges the role of both multilateral and regional efforts—such as those by the US—in aligning RTAs with the WTO framework, viewing these efforts as complementary to, rather than replacements for, the WTO’s mandate [1]. This body of literature underscores the need for a multi-faceted approach involving both multilateral reforms and unilateral/regional initiatives to harmonize RTAs with WTO principles. Future research could explore the effectiveness of these proposed mechanisms to further strengthen the global trading system.

3.2. Impact of RTAs on trade flows, economic growth, and trade dynamics

[4] investigated the effects of RTAs on trade creation and trade diversion in the Asia-Pacific region using a gravity model. Their study examined whether RTAs within this region fostered trade among member countries at the expense of non-members. Specifically, they compared RTAs such as ASEAN, CER, APEC, MERCOSUR, and NAFTA, identifying ASEAN and CER as “trade-creating” agreements that promote trade inclusively with both members and non-members, enhancing welfare. In contrast, APEC, MERCOSUR, and NAFTA were characterized as “trade-diverting,” favoring intra-bloc trade over trade with external partners, potentially diminishing welfare. The study attributes these disparities to differences in the liberalization levels and trade concession structures among RTAs. ASEAN and CER’s broader reduction of trade barriers, including for non-members, exemplifies a more outward-oriented trade policy, while APEC, MERCOSUR, and NAFTA reflect a more inward-looking stance. By applying a gravity model and including comprehensive cost factors, the authors provide valuable insights into the trade and welfare effects of these RTAs, highlighting the significance of trade creation and trade diversion in regional trade agreements.

[5] examined trade complementarities between India and ASEAN countries using the Trade Intensity Index (TII) and Revealed Comparative Advantage (RCA) Index. The study identified substantial trade complementarities, suggesting strong potential for enhanced economic collaboration. India’s trade intensity with ASEAN countries exceeded unity, signalling robust trade ties, while RCA analysis highlighted India’s comparative advantage in sectors like agriculture, chemicals, and textiles. Meanwhile, ASEAN nations demonstrated strengths in machinery, transport equipment, and electronics. The study also revealed competitive overlaps in textiles, posing challenges under regional agreements. Chandran concluded that while tariff reductions under the India-ASEAN Free Trade Agreement (AIFTA) may initially impact India’s exports, medium-term productivity gains could strengthen economic ties further, emphasizing the need for targeted synergies to maximize mutual benefits.

3.3. India-ASEAN free trade agreement

Research on the India-ASEAN Free Trade Agreement (AIFTA) predominantly examines its impact on trade dynamics between India and ASEAN countries [6–19]. Studies, such as [8], have indicated that AIFTA led to a deterioration in India’s trade balance with ASEAN, with a declining trade surplus turning negative post-2012, influenced by the global economic slowdown. [20] used a gravity model to assess the AIFTA’s impact on agricultural trade, suggesting that the agreement has a pure trade-creating effect, benefiting the agricultural sector from further liberalization.

[21] evaluated trade creation and diversion effects under IAFTA, noting that while the agreement fosters bilateral trade, it generates more import than export creation, a potential target for policy intervention. [22] examined trade specialization, finding that India is gradually transitioning towards a competitive and specialized economy but still faces significant competition from ASEAN members. This study underscores the necessity of technological advancement and diversification to strengthen trade resilience under AIFTA.

[23,24] discussed AIFTA-related issues, noting inconclusive evidence on its overall benefits to India. They argue that while the agreement has potential, particularly in services, future assessments are necessary to determine its long-term value. Overall, the literature suggests that AIFTA has spurred trade creation but has also introduced challenges, including trade deficits and heightened competition. Policymakers are encouraged to address these issues to enhance the agreement’s benefits [25–27].

The current study builds on this body of work by offering a comprehensive analysis of the India-ASEAN Free Trade Agreement, using an extended time series of data and advanced econometric techniques. While prior studies have often overlooked issues of heteroskedasticity and cross-sectional dependency, this research applies Generalized Least Squares (GLS) methods to deliver more robust and unbiased results. In doing so, this study aims to provide a nuanced understanding of AIFTA’s effects on both exports and imports, contributing valuable insights to the literature on RTAs and trade policy development.

5.1. Assumptions of the augmented gravity model

The augmented gravity model of international trade builds on the original Tinbergen [28] specification and the theoretically grounded framework of Anderson and van Wincoop [34] by incorporating additional variables such as common language, colonial links, regional trade agreements, infrastructure, institutional quality, and cultural proximity. Despite these extensions, it retains several core assumptions. First, bilateral trade flows are positively proportional to the economic masses of the trading partners (usually proxied by GDP) and inversely proportional to bilateral trade costs, with geographic distance serving as the primary observable proxy for such costs. Second, economic size is treated as exogenous or weakly endogenous in the short to medium run. Third, trade costs are assumed to be log-linear in observable factors (distance, contiguity, language, RTAs, etc.) plus an error term. Fourth, the model accounts for multilateral resistance terms—either through country fixed effects, remoteness indices, or structural estimation of outward and inward price indices [34]—to avoid omitted-variable bias. Fifth, consumer preferences are homothetic with constant elasticity of substitution (CES) across varieties, and goods are differentiated by origin (Armington assumption). Sixth, markets operate under perfect or monopolistic competition with iceberg trade costs. Finally, the augmenting variables are typically assumed exogenous or appropriately instrumented when endogeneity arises [35–39]. These assumptions preserve theoretical consistency while enhancing empirical applicability

The conceptual framework for this study links the ASEAN–India Free Trade Agreement (AIFTA, implemented in 2010) to bilateral trade flows through a sequence of mechanisms. First, AIFTA reduces tariffs and harmonizes certain trade measures between India and ASEAN countries, lowering bilateral policy barriers. Second, tariff reductions and related facilitation measures lower bilateral trade costs — both direct (tariffs) and indirect (procedural delays, compliance costs) — and also reduce elements of Multilateral Trade Resistance (MTR) faced by exporters/importers. Third, lower trade costs increase the volume of bilateral trade, ceteris paribus. In the gravity model, these mechanisms are operationalized by including tariff measures and an FTA dummy (post-2010) as key explanatory variables, while controlling for standard gravity determinants (GDP of both partners, population, distance) and institutional variables (democracy, globalization). Country and time fixed effects are used to capture unobserved multilateral resistance and global shocks respectively.

Recent studies have further refined the gravity model by employing panel data techniques, which offer several advantages over traditional cross-sectional approaches. Panel data allows for the inclusion of time-series dimensions, enabling researchers to control for country-pair-specific factors that might otherwise bias results. For example, panel data could control for unobserved heterogeneity, capturing effects that vary over time and between trading partners. Additionally, recent developments in the gravity model of trade often incorporate variables such as a common language between trading partners, shared borders, and trade agreements. In this study, we have also included the levels of democracy in India and ASEAN trading partners to account for non-economic factors that influence trade flows. The present paper employed four different models, estimated with and without controls, for exports and imports. Hence, the equation 2 is modified as follows.

(3)

(4)

(5)

(6)

where i = India and j shows the ASEAN partners countries; t donates the years (1990–2022)Equations (1)–(4) represent the augmented gravity models used to estimate India’s bilateral exports (EX) and imports (IM) with ASEAN partner countries. In these models, denotes India, refers to the ASEAN partner country, and indicates the time period (1990–2022). The dependent variables and capture India’s export flows to, and import flows from, each ASEAN country in year .

The explanatory variables include the economic size of the trading partners—represented by their respective GDP levels —and demographic size, captured through population . Trade policy variables include the applied tariff rate (), trade openness (), and the FTA dummy (), which equals 1 for years after India–ASEAN FTA implementation and 0 otherwise.

Geographical and cultural proximity are measured using the bilateral distance between countries (), common currency (), common language (), and common border (). Institutional and globalization effects are incorporated through the level of democracy in India and partner countries and the degree of globalization (). The error term captures unobserved bilateral factors affecting trade.

Together, these variables allow the model to quantify how economic size, population, trade costs, institutional characteristics, and the India–ASEAN FTA influence bilateral trade flows over time.

The term represents the country-fixed effect, which accounts for factors such as infrastructure, Multilateral Trade Resistance (MRT), and other unique characteristics specific to each country [40,41]. Similarly, denotes the time-fixed effect, capturing variations in trade caused by fluctuations in economic cycles and global trade disruptions, including natural shocks [40–42]. Lastly, the idiosyncratic error term, which changes across different countries and over time. Traditional panel data estimations can lead to biased results if cross-sectional dependence, serial correlation, and heteroscedasticity are present [40]. To address these issues, this study uses Feasible Generalized Least Squares (FGLS) as the main estimation technique. FGLS is well-suited for handling panel data with cross-sectional dependence, serial correlation, and heteroscedasticity [43,44]. The FGLS estimation is based on the model specified in Equations (1–4).

6.1. Impact of India- ASEAN trade agreement on exports flows

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Table 5. Results of Pooled OLS and GLS: Exports.

https://doi.org/10.1371/journal.pone.0350036.t005

In the baseline model without control variables, India’s GDP exhibits a negative and statistically significant impact on export levels (coefficient: −0.93), suggesting that as India’s economy grows, its reliance on exports may diminish, potentially due to a stronger domestic market orientation. Conversely, the GDP of ASEAN partner countries exert a positive and highly significant influence on exports (coefficient: 1.17), indicating that economic expansion in partner countries correlates positively with India’s export volume, likely due to increased demand in these markets. India’s population shows a robust positive effect on exports (coefficient: 7.93), suggesting that a larger domestic market is positively associated with export capacity, potentially due to economies of scale or enhanced production capabilities. Similarly, the population of ASEAN partner countries has a positive and significant effect (coefficient: 0.37), highlighting the role of larger foreign markets in sustaining higher export levels. The tariff coefficient is negative but statistically insignificant (−0.01), indicating that tariff variations have not substantially affected export flows in this model. The coefficient for geographical distance between India and its trading partners is negative and significant (−2.20), affirming that greater physical separation tends to inhibit trade, possibly due to increased transportation costs and logistical barriers. Shared colonial history and a common language have significant positive effects, suggesting that historical and linguistic ties facilitate trade. Interestingly, the common border variable is negative, suggesting that geographical proximity without strong institutional trade frameworks may not necessarily enhance exports.

When control variables are introduced, the effects shift in key areas. India’s GDP effect loses significance (coefficient: 0.32), while the GDP of ASEAN countries remain a significant positive determinant (coefficient: 1.01), reinforcing the importance of partner economic size. India’s population and the populations of ASEAN partners maintain positive effects on exports, albeit with slightly reduced magnitudes. Tariffs now exhibit a small but statistically significant negative effect (−0.06), suggesting that tariff reductions marginally enhance trade. The effect of distance decreases notably (−0.55), indicating that some deterrence effects of distance are mitigated when institutional and policy variables are included.

The inclusion of controls shows that shared colonial history, common language, and common border variables all significantly enhance trade. Additionally, trade openness (coefficient: 0.82), India’s level of democracy (coefficient: 1.36), and globalization (coefficient: 1.06) exhibit positive and significant impacts on exports, suggesting that institutional and policy factors play critical roles in facilitating trade flows. Conversely, the democracy level in ASEAN partner countries shows a minor but negative effect (−0.18), which may reflect varying political dynamics across ASEAN economies that influence bilateral trade.

In the GLS model without control variables, India’s GDP exerts a statistically significant negative influence on export levels (coefficient: −0.71), suggesting that domestic economic growth may shift India’s focus towards internal markets, potentially reducing export dependence. Conversely, the GDP of ASEAN partner countries is positively associated with India’s exports (coefficient: 1.16), indicating that economic expansion in partner economies stimulates demand for Indian exports, consistent with established gravity model predictions. India’s population exhibits a substantial positive impact on exports (coefficient: 5.85), underscoring the relationship between a larger domestic market and enhanced export capabilities, possibly due to production scaling or increased market capacity. The population of ASEAN partner countries also positively affects exports (coefficient: 0.33), suggesting that larger ASEAN markets correlate with increased trade volumes. While the coefficient for tariffs is slightly negative (−0.05) and significant at the 10% level, suggesting a modest inhibitory effect on exports, geographical distance between India and ASEAN partners shows a significant negative impact (−2.00), reflecting the expected friction associated with physical separation. Historical and linguistic ties, such as shared colonial history (1.66) and common language (0.61), significantly enhance trade, indicating the importance of familiarity and mutual understanding in trade relations. Notably, the shared border variable exhibits a negative coefficient (−1.17), suggesting that geographical proximity may not directly facilitate exports without strong institutional support.

Upon incorporating control variables in the GLS model, the impact of India’s GDP on exports becomes statistically insignificant (coefficient: 0.32), while ASEAN partners’ GDP maintains its positive and significant effect on exports (coefficient: 1.01). The coefficients for population in both India (2.76) and ASEAN partners (0.41) decrease but remain positive and statistically significant, underscoring the role of demographic size as a persistent determinant of export levels. The negative impact of distance diminishes notably (coefficient: −0.55), indicating that additional variables, such as institutional factors, may partially offset distance-related trade barriers.

Key institutional factors emerge as significant determinants of trade performance. Trade openness (coefficient: 0.82), India’s democracy level (1.36), and globalization (1.06) display positive and statistically significant associations with exports, underscoring the relevance of liberalized trade policies, democratic stability, and integration with global systems in fostering trade. In contrast, the democracy level of partner countries has a slight negative effect on exports (−0.18), which may reflect diverse political-economic contexts within ASEAN that shape bilateral trade interactions differently.

The GLS findings, when interpreted alongside Pooled OLS results, reveal the critical roles of economic scale, demographic factors, and institutional variables in driving India’s exports to ASEAN nations. Both models highlight that factors like trade openness and globalization significantly bolster export volumes, while the complex interplay of economic, geographic, and institutional determinants reveals the nuanced dynamics governing India-ASEAN trade relations. In both econometric models, FTA dummy is showing positive impact on exports means FTA plays an important role in Export between India and ASEAN.

6.2. Impact of India- ASEAN free agreement on indian imports

The Pooled Ordinary Least Squares (OLS) regression results, summarized in Table 6, investigate the impact of the India-ASEAN Free Trade Agreement (AIFTA) on India’s import dynamics. In the baseline model without controls, India’s GDP shows a positive and statistically significant relationship with imports (coefficient: 1.07), indicating that as India’s economy expands, its demand for imports increases proportionately. Similarly, the GDP of ASEAN partner countries positively and significantly impact Indian imports (coefficient: 1.27), suggesting that economic growth in ASEAN countries fosters a conducive environment for import demand from India, likely due to increased consumption and production needs.

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Table 6. Results of Imports OLS Model and GLS Model.

https://doi.org/10.1371/journal.pone.0350036.t006

Population effects present a nuanced picture: while India’s population coefficient is negative (−0.72) and not statistically significant, ASEAN countries’ population has a positive and significant effect on Indian imports (coefficient: 0.66). This implies that a larger population in ASEAN nations is positively correlated with higher import levels from India, possibly due to expanded market demand within these economies.

The analysis reveals that tariff levels play a crucial restrictive role in trade, with negative and significant coefficients in both models (−0.30 and −0.31), signifying that higher tariffs directly reduce import volumes. The geographical distance between India and ASEAN partners, as expected, exerts a negative influence on imports, with the effect declining in magnitude and significance in the model with controls (coefficients: −0.97 and −0.33), indicating that proximity remains a relevant factor in facilitating trade but can be moderated by other factors when controls are introduced. Historical connections appear to enhance trade ties: a common colonial history is positively associated with imports in both models (coefficients: 3.12 and 2.65), underscoring the role of historical familiarity in sustaining trade relations. The influence of common border and language variables, however, is mixed. The common border variable shows an insignificant impact on imports, while common language surprisingly demonstrates a negative relationship with imports in the uncontrolled model (coefficient: −0.47). This suggests a complex interaction where linguistic commonalities may not uniformly translate into trade facilitation within the AIFTA context, possibly due to regional heterogeneities and divergent economic structures.

Upon incorporating control variables, further institutional factors emerge as significant determinants. India’s democracy level exhibits a robust positive effect on imports (coefficient: 2.16), implying that democratic governance in India supports greater openness to trade. Contrarily, the democracy level in ASEAN countries negatively impacts imports from India (coefficient: −0.77), indicating that lower democratic levels within partner countries may limit bilateral trade engagement. Meanwhile, other controls, including trade openness and globalization, do not display significant effects on imports, suggesting their relatively minor role in shaping import behavior within this specific bilateral trade framework.

3 presents the Generalized Least Squares (GLS) regression results that assess the impact of the India-ASEAN Free Trade Agreement (AIFTA) on India’s import flows. The log of India’s GDP shows a positive and statistically significant effect on imports (coefficient: 1.07) in the model without controls, indicating that increases in India’s economic size are associated with higher import levels. Similarly, the GDP of ASEAN partner countries also has a positive and significant effect (coefficient: 1.27), suggesting that economic expansion within ASEAN countries bolsters imports from India. When controls are added, the coefficients for both India’s GDP and ASEAN partners’ GDP decrease to 0.75 and 0.89, respectively, although the relationship remains significant, highlighting a stable relationship between economic size and import levels. The impact of population dynamics on imports presents a more complex picture. India’s population variable is insignificant in the uncontrolled model (coefficient: −0.72) but becomes positive and significant with controls (coefficient: 1.41), suggesting that other institutional or economic variables may mediate the role of India’s population in influencing import demand. Conversely, the population of ASEAN partner countries consistently shows a significant positive impact on imports in both models (coefficients: 0.66 and 0.68), reflecting that larger ASEAN populations correlate with increased import activity from India.

Tariffs demonstrate a strong negative association with imports in both models, though the effect diminishes slightly when controls are introduced (coefficients: −0.30 and −0.06). This negative relationship underscores the restrictive impact of tariffs on import flows, while the reduction in magnitude with controls suggests that other factors, such as trade liberalization or economic policies, may mitigate the tariff effect to some extent. Distance, as expected, has a negative effect on imports, although its significance decreases in the controlled model, indicating that physical distance remains a trade barrier but may be counterbalanced by other trade-enabling factors. Historical connections are shown to have a robust impact on trade: common colonial ties are positively and significantly associated with imports in both models (coefficients: 3.12 and 2.73), suggesting that colonial legacies foster sustained trade relationships. However, the effects of a common border and language are not significant, implying that while these factors can facilitate trade, they may not be primary drivers within the India-ASEAN trade framework. Institutional factors emerge as influential under the controlled model. India’s level of democracy has a positive and significant impact on imports (coefficient: 0.85), indicating that democratic governance in India is associated with greater openness to trade. Conversely, the democracy level in ASEAN countries does not show a significant impact on imports (coefficient: −0.06), suggesting that the governance structure of ASEAN partners does not strongly influence India’s import levels under AIFTA. In both econometric models, FTA dummy is showing positive impact on imports means FTA plays an important role in Import between India and ASEAN.