Economic Asymmetry and Trade Openness: An Intra-Industry Trade Analysis of India–Myanmar Plastic Products using the Grubel-Lloyd Index
Tennyson Pangambam1
ORCID: 0009-0004-5314-8778
JEL: F10, F14, F15, C33, L67
Email: tennykuman987@gmail.com
Abstract
This study examines the determinants of intra-industry trade (IIT) in plastic goods between India and Myanmar over the time period of 2013 to 2023. It focuses on key product groups under HS (Harmonized Commodity Description and Coding System) Chap. 39, including packaging and plastic articles (HS 3923 and 3926). Using the Grubel-Lloyd index to measure the intensity of trade symmetry, both pooled OLS and logistic regression with interaction models are employed to investigate how economic asymmetry (GDP gap), trade openness, and external shocks, particularly the COVID-19 pandemic, influence the structure of Indo-Myanmar bilateral trade. The pooled regression model finds that both trade openness and GDP gap play important roles, with a significant interaction term indicating that greater trade openness facilitates intra-industry trade even when the two economies are structurally different. The logistic regression with interaction further reinforces this result by showing that the probability of intra-industry trade increases sharply when trade openness coincides with high GDP asymmetry. This suggests the presence of vertical product differentiation and structural complementarities, especially in low- to medium-tech plastic sectors. Interestingly, the direct impact of COVID-19 on intra-industry trade is statistically insignificant. This indicates resilience in trade flows of plastic products during the pandemic years. However, the interaction of economic asymmetry and trade openness appears to have become more critical in that period. These results challenge conventional “North-South trade” assumptions and highlight the role of policy-driven integration in supporting trade patterns that transcend simple comparative advantage. The study underscores the importance of nuanced policy design that leverages economic diversity alongside market liberalization to foster symmetric trade relationships.
Keywords:
intra-industry trade
plastic goods
HS
Indo-Myanmar trade
Grubel-Lloyd index
trade openness
GDP gap
pooled OLS
Logistic regression with interaction
Department of Economics, Manipur University.
A
Introduction
Intra-industry trade (IIT) has become a vital facet of modern international trade theory, particularly in understanding trade flows among economies with differing factor endowments. Krugman (1979) highlighted how economies engage in trade of differentiated products and benefit from economies of scale, enabling even structurally dissimilar countries to trade similar goods. While much of the literature focuses on trade among developed economies (Salvatore, 2019), the dynamics of intra-industry trade in South–South trade (i.e., among developing countries) remain under-investigated.
In recent decades, international trade has undergone considerable transformation with the proliferation of global value chains, trade liberalisation and deeper regional integration. These changes have created new opportunities for trade flows not just between developed economies but increasingly among developing countries themselves. Indeed, as observed by Standard Chartered, South-South trade corridors have emerged as a “key growth engine,” with trade among emerging markets and developing economies (EMDEs) making up about 61% of EMDE trade in 2023. Within this context, the notion of intra-industry trade (IIT) takes on heightened relevance for understanding the evolving structure of trade among developing economies.
Traditionally, models of trade such as the Paul A. Samuelson-type Ricardian framework or the Heckscher-Ohlin (H-O) model emphasised inter-industry trade driven by comparative advantage. But the emergence of IIT, especially in manufactured sectors characterised by product differentiation and economies of scale, challenged those classical assumptions. The foundation for IIT among structurally similar economies was laid by Paul Krugman (1979) in his new trade theory, and by Staffan Linder (1961) who argued that demand similarity supports IIT. Yet most empirical work has centred on trade among advanced economies.
Fewer studies examine trade between a more advanced developing economy and a less advanced one where asymmetries might prevail in factor endowments, industrial capacity and institutional structure. In the context of this study, India and Myanmar differ significantly in size, per-capita income and industrial infrastructure. Yet, these two economies display meaningful bilateral trade flows in plastics under HS Chap. 39 categories (e.g., HS 3923 and HS 3926). For example, Myanmar’s exports of “plastics and articles thereof” (HS 39) show distinct product shares in 2023: 25% for HS 3923, 22% for HS 3926. The study posits that even in such asymmetric relationships, intra-industry trade may arise, especially when trade openness is high and product differentiation (vertical or horizontal) plays a role. The central research question thus becomes: how do economic asymmetry (GDP gap), trade openness and external shocks (such as the COVID-19 pandemic) affect the intensity and occurrence of intra-industry trade in the plastic goods sector between India and Myanmar?
This study examines intra-industry trade in the plastic goods sector between India and Myanmar during the time period 2013–2023. Despite their economic asymmetry, these two countries exhibit meaningful two-way trade in plastic articles (HS codes 3923, 3926), motivated partly by India’s export of packaging materials and Myanmar’s emerging consumption demand. I focus on the Grubel–Lloyd Index (Grubel & Lloyd, 1975) to measure trade symmetry and explore how economic disparity, trade policy, and external shocks such as COVID-19 influence intra-industry trade.
I frame the analysis around three key hypotheses: (1) larger GDP per capita gaps reduce intra-industry trade intensity, as per classical comparative advantage logic (Balassa, 1966; Helpman & Krugman, 1985); (2) greater trade openness stimulates intra-industry trade through reduction of trade costs and facilitation of product variety (Feenstra, 1998; Baldwin, 2005); and (3) the effect of trade openness may be contingent on economic asymmetry, implying an interaction effect. COVID-19 presented a unique economic shock in this context and while many studies point to disruptions in trade (Baldwin & Freeman, 2020), plastic goods tied to essential supply chains may have demonstrated resilience (UNCTAD, 2021).
In addition, the shock of the COVID-19 pandemic offers a natural interruption. While many sectors faced severe disruption, packaging/plastics (essential goods) might have exhibited resilience, which suggests the possibility of an insignificant direct shock effect but a stronger moderating interaction with structural variables. The present study thus adds value by focusing on a low- to medium-tech manufacturing segment (plastics) in a South–South context, where vertical differentiation in terms of quality differences and regional supply-chain complementarities may be operative.
Beyond empirical value, the study carries important policy implications. If trade openness facilitates IIT even among structurally asymmetric economies, then regional trade agreements, liberalisation policies, and trade-facilitation measures gain added importance not just for trade volume but for structural integration and upgrading of manufacturing sectors. For practitioners and policymakers in India and Myanmar (and broadly across South Asia–ASEAN linkages), the findings may point to areas for targeted industrial cooperation, value-chain integration and policy design that harness structural complementarity rather than simply similarity.
Review of Literature
Intra-Industry Trade: Concepts and Classification
Intra-industry trade (IIT) initially emerged as a theoretical challenge to classical models such as Heckscher–Ohlin (H-O) and Ricardo, which posited trade largely based on comparative advantage and inter-industry flows (Samuelson, 1949). Krugman (1979) advanced the notion of symmetric intra-industry trade among similar economies by incorporating product differentiation and economies of scale. Linder (1961) also argued that countries with similar income levels have overlapping demand structures, promoting intra-industry trade. Empirically, many studies have demonstrated high intra-industry trade levels among developed economies (Hummels & Klenow, 2005).
IIT can take two broad forms: horizontal and vertical. Horizontal IIT occurs when the traded goods are of similar quality but different variety (e.g., in terms of colour or brand). Vertical IIT occurs when goods differ in quality (or price/quality ratio). That is, a more advanced economy exports higher-quality variant while the less advanced economy exports a lower-quality variant, yet both within the same industry (Dutta, 2022). Empirical guidelines for distinguishing these include comparing unit values of exports vs imports. Substantial difference signals vertical differentiation. For developing–developing country pairings, vertical IIT may dominate because of differences in factor endowments, technology and productivity.
Determinants of Intra-industry Trade
Several strands of literature identify key determinants of IIT. Balassa (1986) found smaller GDP per capita gaps foster IIT. The underlying logic is that similar income levels imply similar demand, consumption patterns and thus overlapping production of varieties. Brülhart (2009) emphasised that economic similarity promotes IIT. However, these conclusions are mostly based on developed economy pairings. Lower trade costs, tariffs, border frictions, and increased openness facilitate IIT because firms can engage in differentiated product trade, exploit economies of scale and access broader markets (Feenstra, 1998; Baldwin, 2005). For South–South trade corridors, integration efforts and regional trade agreements (RTAs) build the logistical and institutional environment for IIT.
More recent research including the one conducted by Pahl and Timmer (2019) emphasises supply-chain linkages, vertical specialisation and outsourcing of production stages across countries. Their study on vertical specialisation across 91 countries found waves of vertical specialisation since the 1970s, reflecting increased fragmenting of production. In such scenarios, IIT may emerge even among asymmetric economies if their production processes are vertically differentiated, that is one country produces inputs or lower-quality variants while another produces final or higher-quality variants. The differentiation dimension means that having the capacity for differentiated production matters. The strategic trade-policy literature (Kunin & Zigic, 2024) shows how firms in developed and developing countries compete on quality and price in vertically differentiated industries. This implies that structural asymmetry need not preclude IIT if one country specialises in lower-tech variants and the other in higher-tech variants within the same industry.
Research on the determinants of intra-industry trade consistently underscores the role of market similarity (GDP per capita gap) and trade openness (Balassa, 1986; Brülhart, 2009; Kim, 2007). Balassa (1986) found that smaller GDP gaps correlate with higher intra-industry trade levels, reinforcing Linder’s hypothesis. Conversely, Brülhart (2009) emphasized that trade liberalization and openness promote intra-industry trade by lowering border costs and increasing product variety. Further, some emerging evidence suggests that even asymmetric economies can trade within the same industry when industrial complementarities exist (Staiger, 2020; Goldberg & Pavcnik, 2004).
Intra-industry Trade in the South-South Context
While much of the literature on IIT concerns North–North or North–South trade, a gap remains in South–South trade or trade among developing countries. Yet this is increasingly relevant. Some studies argue that South–South IIT may be facilitated by regional integration, shared commodity profiles or emerging value chains (Shakeel & Gallanti, 2018). In the context of the ASEAN-India Free Trade Area, regional liberalisation fosters product variety, supply-chain linkages and differentiated trade flows, which are conducive to IIT.
South–South intra-industry trade has received limited attention, especially in sectors such as plastics. Studies focusing on South Asian trade highlight that “product complementarities” and “trade facilitation” through regional agreements enhance horizontal and vertical intra-industry trade. Similarly, Shakeel and Gallanti (2018) observed intra-industry trade structures emerging among China, India, and ASEAN countries, with differentiated goods playing a larger role. In the context of ASEAN–India FTA, Baldwin (2011) discusses how trade liberalization fosters intra-industry trade through integrated supply chains. Many developing economies still face structural constraints (infrastructure, productivity, institutions) which theoretically favour inter-industry rather than intra-industry specialisation. But as industries mature, and trade costs fall, developing countries can move beyond pure comparative-advantage trade to source more varieties, engage in processing-trade and specialise in product variants, thereby creating IIT possibilities.
Plastics Industry and Indo-Myanmar Trade
Though the plastics sector may not have received the same level of empirical attention as automotives or electronics, it is a relevant medium-tech manufacturing sector with differentiated products (packaging, household goods, films, etc.). For example, India’s plastics exports declined by 3.5% in FY24 (to US$11.55 billion) amid global headwinds, yet packaging items saw a 20.2% increase in exports, signalling differentiated demand and export variety (Kumar, 2024). For Myanmar, exports in HS 39 “Plastics and articles thereof” in 2023 highlight key sub-headings such as HS 3923 (packing articles) and HS 3926 (other articles of plastics) representing 25% and 22% of total HS 39 exports respectively.
Moreover, border trade between India (North-East region) and Myanmar (via Moreh LCS) shows that plastic articles such as school items, gloves, aprons, handles (HS 3926) and packaging items (HS 3923) are key export items from India to Myanmar (Basu, 2013). These facts suggest that the Indo-Myanmar plastics trade may incorporate elements of differentiated goods, vertical specialisation (packaging vs final articles), and evolving value chains between uneven economies.
COVID-19 introduced significant global trade disruptions, as outlined by Baldwin & Freeman (2020) and UNCTAD (2021). While these studies reveal broad trends of reduced trade flows, less is known about its sectoral impact, particularly in resilient sectors such as essential packaged goods. This study contributes to this gap by analysing intra-industry trade in plastic goods, an industry crucial for packaging, medical supply, within a South–South context.
In summary, while classical theory predicts intra-industry trade among similar economies, recent developments in South–South trade, product differentiation and regional integration suggest that intra-industry trade can thrive despite structural disparities. This study adds to the discourse by applying such an approach to the India–Myanmar plastic goods trade, thus enriching both the theoretical debate and policy relevance.
Research Gap and Contribution
The existent literature underscores two structural forces: economic similarity (income gap) and trade openness as fundamental drivers of IIT. Yet the interaction between trade openness and economic asymmetry (the GDP gap) has been relatively under-explored, particularly in South-South contexts and in manufacturing sectors beyond high-tech industries. Additionally, the plastics sector, while important from a trade policy and manufacturing-development lens, is under-studied in IIT empirics. This paper thereby contributes by: (1) empirically assessing the IIT in the plastic goods sector between a major developing economy (India) and a smaller partner (Myanmar) characterised by structural asymmetry, (2) using both pooled OLS and logistic regression to examine not only the intensity but also the probability of IIT events, and (3) introducing the interaction of trade openness and economic asymmetry as a key explanatory dimension, including the context of a shock (COVID-19).
Methodology
This study investigates the determinants of intra-industry trade in India–Myanmar plastic goods for the time period of 2013 to 2023. Data is collected on key product groups under HS (Harmonized Commodity Description and Coding System) Chap. 39, including packaging and plastic articles (HS 3923 and 3926). I quantify intra-industry trade using the Grubel–Lloyd Index (GLit) as:
GLit = 1 − ∣ Xit − Mit ∣/ Xit + Mit,
where Xit and Mit are India’s exports to and imports from Myanmar for product i in year t. The index ranges from 0 (pure inter-industry trade) to 1 (pure intra-industry trade) (Grubel & Lloyd, 1975).
Variables and Hypotheses
The key explanatory variables are:
1.
Economic Asymmetry (GDP gap):
GDPgapt = ∣ ln(GDP pcIndia,t) − ln(GDP pcMyanmar,t) ∣
This captures relative per capita income differences (Balassa, 1986). I hypothesize that larger GDP differences reduce intra-industry trade intensity (Hypothesis 1).
2.
Trade Openness (TOt):
Tot = Xt + Mt / GDPIndia,t
where Xt + Mt is the total bilateral trade value. Trade openness (TOt) is expected to positively influence intra-industry trade (Hypothesis 2).
3. Interaction Term (GDPgap × TO):
4.
I include the interaction term to test whether trade openness facilitates intra-industry trade especially when economies are structurally asymmetric (Hypothesis 3).
5. COVID Dummy:
6.
A binary variable for pandemic years (2020–2022) is included to capture potential COVID disruptions (tested in the pooled regression).
Empirical Modelling Framework
The modelling framework includes the pooled regression model and the logistic regression.
The Pooled Regression Model
I estimate:
GLit = α + β1(Log GDPgapt) + β2(TOt) + β3(Log GDPgapt × TOt) + β4COVIDt + β5(TOt× COVIDt) + β6(Log GDPgapt × COVIDt) + εit
This allows evaluation of direct and interaction effects on intra-industry trade intensity. Robust standard errors are used for inference.
Logistic Regression with Interaction
To further assess the probability of intra-industry trade (IIT) occurring, I convert GLit into a binary dependent variable (IITbinary):
IITbinary = { 1, GLit ≥ threshold
0, otherwise.
Here, the threshold value of IITbinary is the median value of the GLit variable from the data. This value = 0.006061
After defining the binary variable, a logit model with interaction is estimated:
Logit (P (IITbinary = 1)) = γ0 + γ1(Log GDPgapt) + γ2(TOt) + γ3(GDPgapt × TOt)
This model captures how the combined effects of income disparity and trade openness shift the likelihood of observing substantive intra-industry trade.
Analysis and Results
Firstly, the time series component is checked for stationarity using the Panel Levin-Lin-Chu test.
Variable | Test Statistic at I(0) | p-value |
|---|---|---|
GL Index | -3.22*** | 0.000 |
Log GDP Gap | -2.84** | 0.002 |
Trade Openness | -4.86*** | 0.000 |
Source
Author’s estimation.
Table 1 confirms the variables to be stationary at levels or at I(0).
Pooled Regression Model
The empirical analysis of intra-industry trade (IIT) in plastic products between India and Myanmar provides important insights into the interplay between economic asymmetry, trade openness, and external shocks. The pooled regression and logistic regression models reveal both expected and surprising dynamics in bilateral trade patterns. The result of the pooled regression model is specified in Table 1.
Variables | Coefficients | Standard Error | t Stat | P-value |
|---|---|---|---|---|
Intercept | -0.907* | 0.416 | -2.179 | 0.040 |
Log GDP gap | 5.9543** | 2.137 | 2.785 | 0.011 |
Trade openness | 1096.48** | 449.129 | 2.441 | 0.023 |
Covid | -0.876 | 4.650 | -0.188 | 0.852 |
Log GDPgap*TO | -7285.57* | 3896.34 | -1.869 | 0.075 |
TO*covid | 2665.50 | 8860.384 | 0.300 | 0.766 |
Log GDPGap*covid | -2.793* | 1.371 | -2.03 | 0.054 |
Source
Author’s estimation.
The Variance Inflation Factors for the two main independent variables are calculated to ensure that these two variables are not multicollinear.
Variable | VIF |
|---|---|
Log GDP Gap | 1.34 |
Trade Openness | 1.22 |
Source
Author’s estimation.
Table 3 reports no multicollinearity as the VIF values are below the threshold level of VIF = 10.
In the pooled regression (Table 2), log GDP gap emerges as a statistically significant determinant of intra-industry trade. The positive and the significant coefficient suggests that as the logarithmic difference in GDP between India and Myanmar increases, intra-industry trade intensity also rises. This finding contrasts with the traditional expectation that larger economic asymmetries reduce the scope for intra-industry trade (Helpman & Krugman, 1985). However, in the case of India–Myanmar trade in plastics, this positive association may reflect vertical intra-industry trade, where structural differences in production capabilities foster complementary exchanges. India’s relatively advanced plastic manufacturing base aligns with Myanmar’s demand for intermediate and finished goods, generating an asymmetry-driven trade complementarity.
Similarly, trade openness shows a significant and positive effect. This indicates that greater openness, measured through trade-to-GDP ratios, expands bilateral intra-industry trade. This result aligns with empirical literature emphasizing the role of liberalization and regional integration in fostering intra-industry linkages (Greenaway, Hine, & Milner, 1995; Kimura, 2006). Myanmar’s progressive liberalization under ASEAN frameworks and India’s “Act East Policy” have likely reinforced this effect.
The interaction term “GDP gap × trade openness”, though only marginally significant at 10% significance level, is negative in the pooled regression. This suggests that while GDP gap and openness individually promote intra-industry trade, their combined effect may attenuate trade symmetry when openness disproportionately benefits one side. However, this outcome becomes more nuanced in the logistic regression. Furthermore, the direct impact of the COVID-19 dummy on intra-industry trade is statistically insignificant. This indicates resilience in trade flows of plastic products during the pandemic years.
Logistic Regression Model
The result of the logistic regression with interaction model is specified in Table 4.
Variable | Coefficient | p-value |
|---|---|---|
Intercept | 2.636 | 0.57 |
Log GDP gap | -1.122** | 0.044 |
Trade Openness | -1.470** | 0.035 |
GDP gap * Trade Openness | 2.611** | 0.03 |
Source
Author’s estimation.
In the logit specification, the results indicate a more complex relationship. Log GDP gap exerts a negative and significant effect. This is consistent with classical intra-industry trade theory that predicts dissimilar economies are less likely to engage in symmetric trade. Similarly, trade openness alone shows a significant negative coefficient, suggesting that trade openness without structural alignment reduces the probability of intra-industry trade. At first glance, this appears contradictory to the pooled regression findings. However, the interaction term resolves the puzzle. GDP gap × trade openness is strongly positive and significant. This indicates that when trade openness coincides with large economic asymmetry, the probability of intra-industry trade rises sharply.
This contrast between the two models highlights an essential distinction: pooled regression captures intensity of trade flows, while the logistic model identifies the probability of crossing a certain threshold of intra-industry trade (given by the Grubel-Llyod Index). In other words, trade openness and GDP asymmetry do not guarantee consistently higher intra-industry trade intensity, but when combined, they sharply increase the likelihood of trade being classified as intra-industry.
The COVID-19 dummy and its interactions are largely insignificant across models, suggesting that intra-industry trade in plastics proved resilient during external shocks. Taken together, the findings support the hypothesis that India–Myanmar plastic trade is characterized by structural complementarity and vertical intra-industry trade, where economic asymmetries, rather than hindering trade, facilitate mutually beneficial exchanges under conditions of greater openness.
Graphical Representation of the Conditional Logit Relationship
The impact of Log GDP Gap on IIT binary conditional on Trade Openness is given by:
Fig. 1
Impact of Log GDP Gap on intra industry trade conditional on Trade Openness.
Source
Author’s estimation.
As seen in Fig. 1, with a lower trade openness level (orange line), larger Log GDP gap hinders intra-industry trade; while with a higher level of trade openness(blue line), larger Log GDP gap promotes intra-industry trade. So, trade integration between India and Myanmar would mean going from the downward sloping “Low TO” line to the upward sloping “High TO” line.
In a similar vein, the impact of Trade Openness on IIT binary conditional on Log GDP Gap is given by:
Fig. 2
Impact of Trade Openness on intra industry trade conditional on Log GDP Gap.
Source
Author’s estimation.
As seen in Fig. 2, with a lower Log GDP Gap (orange line), larger Trade Openness hinders intra-industry trade and vice-versa. So, given that the GDP gap between India and Myanmar is large, trade openness would mean being on the upward sloping “High Gap” line. Thus, both these graphs support more trade openness between the two countries to induce greater intra-industry trade in the plastic goods sector.
Findings and Discussion
The empirical results of this study shed new light on the complex interaction between economic asymmetry and trade openness in determining intra-industry trade (IIT) between India and Myanmar in plastic products. The results from both pooled OLS and logistic regression models reveal that the interaction term between trade openness and GDP gap is statistically significant and positive, indicating that structural differences between the two economies can, under certain conditions, become a source of strength for bilateral trade. Rather than supporting the traditional Linder or Krugman view that income similarity is the key driver of IIT, the evidence here aligns with the evolving South–South trade literature which emphasizes complementarity over similarity.
When examined separately, however, the individual coefficients for trade openness and GDP asymmetry are negative, suggesting that each factor alone is insufficient to promote sustained intra-industry exchange. High trade openness without a complementary industrial base may simply increase import dependency, while large economic gaps without sufficient integration mechanisms may discourage two-way trade. Yet, when both occur together, the interaction amplifies the probability of IIT. This pattern reflects a vertical form of intra-industry trade, wherein India exports intermediate or high-quality plastic products such as industrial packaging and technical components, while Myanmar’s demand is concentrated in low- to medium-tech downstream segments like household and consumer goods.
Furthermore, the logistic regression results reveal that the COVID-19 dummy variable is statistically insignificant, implying that the pandemic did not substantially disrupt the IIT dynamics in the plastic sector. This resilience may be attributed to the essential nature of plastic-based products in packaging, healthcare and logistics during the pandemic years. The results collectively suggest that trade openness acts as an enabling condition. It magnifies the potential of IIT when structural complementarities exist but cannot independently generate sustained symmetry in trade flows.
Overall, the findings challenge the conventional notion that structural similarity is a precondition for intra-industry trade. Instead, they support the argument that in South–South trade settings, differential capabilities and endowments can coexist with trade symmetry when openness policies and industrial linkages are properly aligned. The India–Myanmar plastic goods trade thus represents a case of evolving vertical integration where production stages and product qualities are distributed according to comparative strengths, not parity in economic structure.
Policy Implications
The above findings carry significant implications for trade strategy and industrial policy design in both India and Myanmar. First, the evidence that GDP asymmetry, when combined with higher trade openness, fosters IIT suggests that policymakers should treat structural differences as complementary opportunities rather than developmental gaps. India’s advanced processing and manufacturing capacity can supply intermediate and semi-finished plastic goods, while Myanmar’s expanding domestic demand and assembly-based sectors can absorb and transform these imports into finished consumer products. Such an arrangement promotes vertical specialization, enhances efficiency, and deepens regional value chains.
Second, the results reaffirm that trade openness remains a critical enabler of IIT provided it is coupled with institutional and logistical harmonization. The alignment of India’s Act East Policy with Myanmar’s ASEAN commitments offers a practical policy pathway. Both countries could work toward reducing tariff and non-tariff barriers, simplifying customs procedures and standardizing product certification within the plastics sector. These steps would not only facilitate smoother cross-border movement of goods but also stimulate joint investment in warehousing, packaging and recycling infrastructure, thereby strengthening the foundations of intra-industry trade.
Third, the negative standalone coefficients for openness and GDP gap caution against liberalisation policies that are detached from industrial cooperation. Trade liberalisation without coordinated sectoral development can lead to one-sided dependency or hollowing out of domestic industries. To counter this, joint ventures, technology transfer arrangements and skill development programs should be prioritized in the plastics value chain. Collaborative research in biodegradable plastics, polymer recycling and green production technologies can further expand bilateral complementarities while aligning with sustainable trade goals.
Finally, recognizing that the GDP gap remains large, sustained intra-industry trade growth will require deeper integration through policy synchronization and institutional cooperation. Enhancing trade openness via regional infrastructure corridors and improving logistics connectivity across India’s Northeast and Myanmar’s border regions can make IIT both resilient and inclusive. By turning structural asymmetry into an engine for specialization, both economies can strengthen industrial interdependence and build a more balanced and sustainable trade partnership in the long run.
Conclusion
This study investigates intra-industry trade (IIT) in plastic products between India and Myanmar with a focus on the roles of economic asymmetry, trade openness and external shocks. Using both pooled regression and logistic regression models, the analysis finds that while GDP gap and trade openness individually influence intra-industry trade in complex ways, their interaction emerges as the critical determinant of trade symmetry. Specifically, intra-industry trade intensity increases with GDP asymmetry and trade openness in pooled regressions, while the logit model highlights that the probability of intra-industry trade rises sharply when both conditions coincide.
The findings suggest that India–Myanmar plastic trade is shaped by vertical intra-industry trade, reflecting complementarities rather than similarities in production structures. Importantly, the COVID-19 pandemic appears to have had little direct effect on intra-industry trade flows, underscoring the resilience of these trade relationships in the plastic goods sector. The persistence of trade flows during global disruption indicates the essential and embedded nature of plastic-related trade, linked to packaging, healthcare and basic consumer needs.
From a policy perspective, the results emphasize the need to leverage economic diversity alongside openness. Rather than seeking convergence, India and Myanmar can benefit from their structural differences through targeted cooperation in the plastic sector. Regional integration, trade facilitation and industrial linkages will be essential in sustaining and expanding intra-industry trade. Moreover, the findings imply that IIT can serve as a developmental mechanism for less industrialized partners, enabling technology diffusion, skill enhancement, and gradual upgrading along value chains. For India, the results highlight opportunities to consolidate its industrial leadership within South–South value chains, while for Myanmar, deepening integration with India’s industrial ecosystem can promote diversification and capacity building. These insights challenge conventional North–South trade theories and contribute to the broader debate on trade asymmetry, industrial integration and sustainable regional development.
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