Quantifying Non-tariff Measures: The Ad-valorem Equivalents Approach

The shift from tariff-based to NTM-based protection has generated a substantial empirical literature. The foundational contribution is Kee et al. (2008), who used a structural import demand system across 78 countries and over 4,000 product lines to estimate AVEs of both tariffs and NTMs. Their subsequent article (Kee et al., 2009) constructs the Overall Trade Restrictiveness Index (OTRI) and the Market Access OTRI using these AVEs, showing that NTMs account for a large share of effective protection, particularly in agriculture. The present study adopts the KNO elasticities and quantity-to-price conversion as its core identification strategy.

Cadot and Gourdon (2016) extend the KNO framework to develop country-specific AVE estimates using UNCTAD’s TRAINS database and find that agricultural NTM AVEs in developing-country markets often exceed 20%, with substantial cross-country and product-level heterogeneity. Their work demonstrates that the distributional spread of AVEs is wide: while median AVEs cluster around 15%-20%, upper-tail estimates for SPS-intensive categories frequently reach 80%-100%—precisely the range documented in this study for cereals, animal products and live plants. Ederington and Ruta (2016) survey the broader NTM literature and argue that the interaction between trade agreements and domestic regulations is central to understanding why effective protection may persist or rise even after tariff liberalisation—the mechanism this article tests empirically for India and ASEAN.

Two main econometric strategies dominate the AVE estimation literature. Price-gap methods (Bureau & Salvatici, 2004; Ferrantino, 2006) compare domestic and border prices to compute implicit tariff equivalents. Quantity-based methods, exemplified by KNO and by Fontagné et al. (2015), estimate how NTM presence or intensity affects trade volumes and then convert the volume effect into a price equivalent using demand elasticities. The quantity-based approach is adopted here because it is directly applicable to the bilateral ASEAN-India panel, which lacks the product-level price data required for price-gap estimation. Importantly, Fontagné et al. (2015) demonstrate using firm-level data that NTM effects on trade are systematically larger at the extensive margin—that is, they reduce the number of firms able to enter a market—than at the intensive margin. The Heckman correction in this study directly captures this extensive-margin suppression, providing more conservative and credible AVE estimates than ordinary least squares (OLS) on positive-trade observations alone.

Non-tariff Measures in the Association of Southeast Asian Nations and Regional Trade Agreements

The ASEAN literature documents a persistent NTM paradox: regional integration has reduced intra-ASEAN tariffs substantially, yet NTM coverage ratios have risen in parallel (Ing, 2015; Ing et al., 2016). Ing (2015) shows that NTM frequency indices for food and agricultural products in ASEAN exceed those for manufactured goods, with SPS and TBT measures accounting for the bulk of the count. The data in this study are consistent with this finding: across the 11 restrictive chapters, NTM stocks grew between 17.7 and 38.5 times over 2010-2024, with beverages (HS 22) registering 36-fold growth from 2.0 to 72.0 measures per importer-chapter pair. Cadot and Gourdon (2018) confirm that SPS-related AVEs in Asian food markets frequently range between 15% and 50%, consistent with the Tier 3 results found in this article for dairy, vegetables and cocoa.

For the specific India-ASEAN context, gravity-based studies of AIFTA generally find positive trade creation but incomplete preference utilisation. Pant (2011) and Batra (2012) identify agricultural chapters as exhibiting the weakest utilisation of AIFTA preferences, attributing this to SPS and documentation barriers rather than tariff levels. This is not incidental: the chapters flagged by Pant and Batra—cereals, animal products and feed residues—are precisely the three Tier 1 chapters in this study, which exhibit AVEs above 90% in 2024. More recently, Kumar and Sharma (2020) estimate a gravity model for India’s food exports to ASEAN and find that NTM-proxied variables reduce bilateral trade flows even after controlling for tariff preferences, corroborating the negative coefficients found in the Heckman estimation here. These studies, taken together, constitute a consistent body of evidence that the tariff schedule does not adequately represent the market access environment for Indian agricultural exporters in ASEAN.

The economic significance of the 11 restrictive chapters extends beyond their share of HS-2 lines. These chapters collectively account for a disproportionately large share of India’s agricultural export potential to ASEAN. Cereals alone—principally rice and wheat—represent India’s largest single agricultural export category by volume; India is the world’s largest rice exporter and consistently ranks among the top three wheat exporters globally (Food and Agriculture Organization of the United Nations, 2023). Animal products, feed residues and beverages each correspond to product categories with high value-added and strong ASEAN import demand, driven by the rapid expansion of ASEAN’s middle-income consumer base and its industrialised livestock sector. The failure of AIFTA to translate tariff elimination into actual market access in these chapters, therefore, represents not a marginal statistical finding but a commercially material constraint on India’s agricultural trade trajectory.

Instrument Substitution and Effective Protection

The hypothesis that governments substitute NTMs for tariffs as trade agreements constrain tariff policy has been tested in several contexts. Bown and Tovar (2011) find evidence of instrument substitution in India’s use of anti-dumping and safeguard measures following tariff bindings under the World Trade Organization (WTO). Limão and Tovar (2011) develop a theoretical model of tariff-NTM substitution and provide cross-country empirical support. In the ASEAN context, Ing et al. (2016) document rising NTM counts coinciding with ASEAN tariff phase-downs under the ASEAN Free Trade Area and the ASEAN-China Free Trade Agreement, a pattern directly analogous to that observed here for AIFTA.

On the gravity modelling side, Santos Silva and Tenreyro (2006) demonstrate that Poisson Pseudo-Maximum Likelihood (PPML) is superior to log-OLS for gravity estimation because it handles heteroskedasticity and zero trade flows correctly. Helpman et al. (2008) show that ignoring the extensive margin—that is, selection into trading—biases intensive-margin estimates, motivating the Heckman two-step approach used in this study. The combination of Heckman selection correction with the KNO AVE conversion, therefore, directly addresses the two dominant methodological critiques in this literature.

A further strand of literature documents how the structure of preferential trade agreements (PTAs) shapes NTM outcomes. Disdier et al. (2008) find that SPS measures imposed by high-income countries on developing-country agricultural exports reduce trade flows by 63% on average for affected products. Baldwin (2011) argues that the ‘juggernaut’ logic of tariff liberalisation in PTAs does not automatically extend to NTMs, because NTMs serve domestic regulatory functions that governments are reluctant to constrain through international commitments. This political economy logic explains why AIFTA’s SPS and TBT chapters remain weak: while the agreement mandates adherence to WTO disciplines, it contains no bilateral transparency mechanisms, no accelerated scientific justification requirements, and no joint committee empowered to review accumulating NTM stocks. The result, as the data in this study show, is an unchecked regulatory ratchet in the 11 restrictive chapters.

Data and Panel

Bilateral export data at the HS-6 level are sourced from UN Comtrade (UNCTAD, 2017) and aggregated to 24 HS-2 chapters. NTM data are drawn from UNCTAD’s TRAINS database; for each importer-chapter pair, the cumulative stock of implemented measures is counted annually from 2010 to 2024 to produce a non-decreasing NTM stock variable Nc,h,t. AIFTA preferential tariff rates τh,t are coded from official AIFTA schedules at the HS-2 level. Import demand elasticities εh are taken directly from Kee et al. (2008), who estimate these using a structural demand system across multiple countries and HS product lines.

Estimation

For each HS-2 chapter h, a Heckman two-step model is estimated pooling across all 10 ASEAN importers and all 15 years, following the approach recommended by Helpman et al. (2008) for handling selection into trade. The selection equation (probit) models the probability of positive trade as a function of NTM stock, AIFTA tariff, year and importer fixed effects. The outcome equation (OLS on positive-trade observations) adds the inverse Mills ratio and recovers the intensive-margin NTM coefficient βh. The preference for Heckman over PPML (Santos Silva & Tenreyro, 2006) here reflects the chapter-level panel’s small cross-sectional dimension, which makes PPML convergence unreliable.

The AVE conversion follows KNO (2008, 2009). The volume effect of NTMs in year t is expressed as: ∇ln(M) = βh ´ ln(1 + N̄h,t). This is divided by the elasticity to yield an implied price change: ∇ln(P) = ∇ln(M)/εh. A raw AVE is then exp(∇ln(P)) - 1, expressed in per cent. Following UNCTAD (2018) guidance, a hyperbolic tangent bounding transformation—AVEh,t = max{tanh(∇ln(P)) ´ 100, 0}—keeps AVEs in the [0, 100] range, consistent with Cadot and Gourdon (2016). TEP is TEPh,t = τh,t + AVEh,t.

Chapter Classification and the Commercial Weight of Restrictive Chapters

The Heckman-KNO estimation yields negative NTM coefficients in 11 of the 24 agricultural HS-2 chapters, implying that higher NTM stock significantly depresses India’s export volumes to ASEAN, conditional on selection, tariffs and fixed effects. The remaining 13 chapters have non-negative coefficients and are treated as non-restrictive. This 11/24 split—that is, 46% of agricultural HS-2 chapters—is broadly consistent with Cadot and Gourdon (2016), who find that roughly half of HS-2 agricultural chapters exhibit significant NTM trade-restricting effects in Asian markets. Table 2 presents the 2024 snapshot for all 24 chapters.

The classification of 11 chapters as restrictive is not merely a statistical artefact. These chapters correspond to some of India’s most commercially significant agricultural export categories in the ASEAN market. Cereals (HS 10)—principally non-basmati rice and wheat—account for a substantial portion of India’s total agricultural export value; India supplied over 22 million tonnes of rice to global markets in 2022-2023, with ASEAN member states, particularly Indonesia, the Philippines, Malaysia and Vietnam, constituting a primary destination (Food and Agriculture Organization of the United Nations, 2023). Animal products (HS 05) encompass a range of processed biological materials—hair, bones, ivory and natural sponges—for which India holds a comparative advantage and ASEAN import demand is driven by processing industries. Beverages and spirits (HS 22), food residues and feed (HS 23), and milling products (HS 11) represent higher value-added categories where India’s agri-processing sector is competitive. Taken together, the 11 restrictive chapters represent a broad cross-section of India’s agricultural export potential in ASEAN, spanning primary commodities, processed foods and industrial agricultural inputs.

The aggregate commercial weight of these chapters reinforces the analytical priority placed on the restrictive group. Based on UN Comtrade trade flow data, the 11 restrictive chapters collectively account for approximately 60% of India’s total agricultural export value to ASEAN-10. This estimate is consistent with the composition of India’s agricultural export basket to ASEAN documented by Kumar and Sharma (2020), who identify cereals, animal-origin products, edible fats and beverages as the dominant product categories by value. The implication is direct: the NTM AVEs documented in this study do not describe a peripheral set of niche products—they describe the central mass of India-ASEAN agricultural trade, and their steady rise from 2010 to 2024 constitutes a systematic erosion of AIFTA’s market access value in the segment of bilateral trade that matters most.

The Non-tariff Measure Accumulation Pattern: A Regulatory Ratchet

Before turning to chapter-level trajectories, it is important to establish the structural pattern of NTM accumulation that underlies all the AVE estimates in this study. Across the 11 restrictive chapters, NTM stocks in 2010 were uniformly low—ranging from 0.8 measures per importer-chapter pair for vegetable plaiting materials (HS 14) to 3.7 for animal and vegetable fats (HS 15). By 2024, these stocks had grown to between 18.4 (HS 14) and 74.2 (HS 04, dairy products) measures. The growth multipliers are striking: dairy NTM stocks grew 28.5 times, beverages grew 36.0 times, and food residues grew 38.5 times over the 15-year period. For comparison, the non-restrictive chapters—where AVEs are zero or negligible—exhibit similarly large NTM stock growth, which confirms that the restrictive outcome is driven not by NTM accumulation alone but by the sign and magnitude of the underlying Heckman coefficient. In other words, where βh is sufficiently negative (as in HS 05, 06, and 10), even a modest NTM stock generates high AVEs; where βh is positive (as in HS 08, 12, and 17), NTM accumulation does not suppress trade volumes and generates no AVE.

This pattern has an important policy implication. The NTM accumulation trajectory is essentially a one-way ratchet: once measures are enacted by ASEAN importers, they are rarely withdrawn. The non-decreasing construction of the NTM stock variable in this study reflects this institutional reality, documented by Ing et al. (2016) for the ASEAN regulatory context. The cumulative weight of the NTM stack, therefore, grows each year mechanically, and in chapters with sufficiently trade-suppressing regulatory content, the AVE rises monotonically even when the tariff is falling. This is the structural mechanism that produces the TEP stability—or in some chapters, outright increase—documented in Table 1.

When Non-tariff Measures First Surpassed Tariffs: A Diagnostic Threshold

One of the most analytically revealing features of the data is how early NTM AVEs exceeded AIFTA tariffs in the restrictive chapters. In nine of the 11 restrictive chapters, the NTM AVE exceeded the prevailing AIFTA tariff rate already in 2010—the first year of the agreement. For HS 05 (animal products), the AVE in 2010 was 90.9% against a tariff of 5.0%; for HS 10 (cereals), it was 60.0% against 10.0%; and for HS 22 (beverages), 45.7% against 6.0%. Only vegetables (HS 07) and cocoa (HS 18) crossed the threshold in 2011. This finding has profound implications for the evaluation of AIFTA: the NTM burden was already the dominant form of protection in these chapters at the moment AIFTA entered into force. Tariff liberalisation was, from the outset, removing the smaller and less binding of the two protection instruments in these markets. The agreement’s agricultural gains were therefore overstated from the beginning in any assessment that focused solely on tariff schedules.

To quantify the degree to which NTMs dominate TEP in 2024, one can compute the NTM share of TEP for each restrictive chapter. For HS 05 (animal products) and HS 06 (live plants), NTMs account for 100% of TEP—the tariff has reached zero and all protection is regulatory. For HS 10 (cereals), the NTM share is 100%; for HS 22 (beverages), 100%; for HS 23 (food residues), 100%; and for HS 14 (vegetable plaiting materials), 100%. In HS 11 (milling products), the 0.5% residual tariff means NTMs account for 99.4% of TEP. In HS 15 (fats), HS 04 (dairy) and HS 18 (cocoa), the NTM share of TEP ranges from 96.1% to 99.3%. Only in HS 07 (vegetables), where a small residual tariff interacts with a moderate AVE, does the NTM share fall slightly below 95%. The message is unambiguous: across all 11 restrictive chapters, the effective market access barrier in 2024 is almost exclusively a regulatory one.

Ad-valorem Equivalent Trajectories in High-burden Chapters (2010–2024)

Among the 11 restrictive chapters, three distinct tiers of NTM intensity are evident, consistent with the heterogeneity documented by Fontagné et al. (2015) across agricultural product categories.

The Total Effective Protection–Tariff Inversion: Formal Liberalisation Without Market Access

A critical diagnostic for the effectiveness of AIFTA’s agricultural commitments is what may be termed the TEP-tariff inversion: the point at which NTM AVEs grow to dominate TEP so completely that tariff concessions become commercially insignificant. In the five Tier 1 chapters, this inversion was already in place at AIFTA’s inception in 2010—NTM AVEs then ranged from 32.8% (HS 23) to 90.9% (HS 05), while AIFTA tariffs ranged from 5% to 10%. By 2024, with tariffs at zero, the inversion is absolute in these chapters: 100% of the residual protection barrier is regulatory.

Across all 11 restrictive chapters, the weighted average NTM share of TEP in 2024 is approximately 97%. This figure underscores a fundamental analytical point: evaluating AIFTA’s agricultural performance through the lens of tariff schedules—as most FTA assessments do—is equivalent to measuring the depth of an ocean by the height of the waves on its surface. The tariff is the visible, treaty-commitments-bound, easily monitored component of the protection regime. The NTM regime is the structural, regulatory and largely unconstrained component. In the 11 restrictive chapters, the latter has grown to dwarf the former.

A concrete illustration of the TEP tariff inversion is provided by cereals (HS 10). In 2010, India’s rice exporters to ASEAN faced a 10% preferential tariff under AIFTA and an NTM AVE of 60%—a combined TEP of 70%. By 2020, the tariff had been eliminated under AIFTA’s normal track and was zero, but the NTM AVE had risen to 96.7%: TEP increased to 96.7%. By 2024, the TEP is 97.4%—27.4 percentage points higher than in 2010, despite full tariff liberalisation. In effect, ASEAN governments’ regulatory accumulation more than offset the entire value of AIFTA’s tariff concessions in cereals, leaving Indian rice and wheat exporters facing a higher effective barrier in 2024 than they did when the agreement entered into force.

Aggregate Effective Protection Trends

Table 1 presents aggregate AVEs and effective protection at three levels of aggregation for selected years. Two patterns stand out. First, simple-mean NTM AVEs (column 5) rise monotonically from 17.1% in 2010 to 32.5% in 2024, while simple-mean tariffs collapse from 7.1% to 0.3%. As a result, simple-mean TEP remains remarkably stable, ranging from 24.1% in 2010 to 32.8% in 2024—consistent with the Limão and Tovar (2011) prediction that tariff and NTM instruments are substitutes in governments’ protection portfolios. Second, focusing on the 11 restrictive chapters, average NTM AVEs roughly double over the period, rising from 37.2% in 2010 to 71.0% in 2024.

The stability of aggregate TEP warrants emphasis. Over the 15 years, AIFTA eliminated approximately 6.75 percentage points of simple-mean tariff protection. Over the same period, simple-mean NTM AVEs increased by 15.4 percentage points. The net change in aggregate TEP is therefore positive: Indian agricultural exporters to ASEAN face, on average, 8.7 percentage points more total protection in 2024 than in 2010, despite the complete formal tariff liberalisation delivered by AIFTA. This is the quantitative expression of the instrument substitution hypothesis applied to the India-ASEAN agricultural trade relationship over a 15-year horizon.

Non-restrictive Chapters: Genuine Liberalisation

Thirteen chapters—live animals (HS 01), meat and offal (HS 02), fish and seafood (HS 03), fruits and nuts (HS 08), coffee, tea and spices (HS 09), oil seeds (HS 12), lac and gums (HS 13), meat and fish preparations (HS 16), sugars (HS 17), cereal preparations (HS 19), vegetable and fruit preparations (HS 20), miscellaneous food preparations (HS 21), and tobacco (HS 24)—have non-negative NTM coefficients. In these chapters, AIFTA tariff reductions translate directly into lower TEP, validating AIFTA as a genuine market-opening instrument in these segments. By 2024, TEP ranges from 0% (HS 01, 02, 03, 08, 09 and 12) to 3% (HS 24). This is consistent with Kumar and Sharma’s (2020) finding that spices, oil seeds and processed foods show the strongest price-preference transmission under AIFTA.

It bears noting that the non-restrictive classification does not imply an absence of NTMs in these chapters. NTM stocks in HS 12 (oil seeds), HS 21 (miscellaneous food preparations), and HS 19 (cereal preparations) are among the highest in the data set—110.5, 86.0 and 67.0 measures, respectively, by 2024. The non-restrictive outcome arises because the Heckman coefficients for these chapters are positive, implying that NTMs as applied to these categories do not suppress India’s export volumes on net—possibly because they serve as quality signals that facilitate trade rather than impede it, consistent with the ‘standards-as-catalysts’ argument advanced by Disdier et al. (2008) for certain food safety standards in high-income markets. The contrast between these chapters and the restrictive group, therefore, does not represent a difference in NTM quantity but a difference in NTM type and trade impact: what restricts trade in cereals and animal products facilitates it in spices and oil seeds.

Tariff Illusion and Instrument Substitution

The aggregate data reveal a textbook case of instrument substitution as theorised by Limão and Tovar (2011) and empirically documented for India by Bown and Tovar (2011). As AIFTA tariffs are phased down on a predetermined schedule, NTM stocks in ASEAN accumulate continuously. In the 11 restrictive chapters, the NTM stock roughly doubled or tripled between 2010 and 2024. Because the KNO elasticities are fixed (reflecting long-run demand responses), the rising NTM stock mechanically translates into rising AVEs—and thus into stable or rising TEP. For cereals, TEP increased by 27 percentage points over a period of full tariff liberalisation. For food residues, it rose by nearly 57 percentage points.

The AIFTA text contains limited disciplines on SPS and TBT measures beyond general WTO-consistency obligations, creating space for this substitution to occur. Ederington and Ruta (2016) highlight that the design of NTM disciplines in PTAs is the decisive variable determining whether tariff concessions translate into actual market access—a finding that directly motivates the policy agenda in the next section. The empirical record here suggests that AIFTA’s SPS chapter, which relies on best-endeavour language and defers to the WTO SPS Agreement without bilateral specificity, has provided no meaningful constraint on the regulatory accumulation documented in Tables 1 and 2.

Scale of the Problem: 46% of Chapters, ~60% of Trade Value

The 11 restrictive chapters include India’s most commercially significant agricultural export categories to ASEAN. The ~60% trade-value share of the restrictive group means that the NTM barrier problem is not confined to niche product lines but is embedded in the mainstream of the bilateral agricultural trade relationship. Cereals alone—principally rice—account for India’s largest single agricultural export by volume, and ASEAN’s rice import demand is structurally driven by population growth and urbanisation in Indonesia, the Philippines and Vietnam. Animal and vegetable fats (HS 15) and food residues (HS 23) feed directly into ASEAN’s expanding livestock and aquaculture processing sectors. Milling products (HS 11) and beverages (HS 22) represent value-added categories where Indian exporters are competitive, and ASEAN consumer demand is rising with income growth.

Batra (2012) estimates that India’s unrealised agricultural export potential to ASEAN—accounting for tariff preferences not being utilised—is largest precisely in these chapters. The fact that these face AVEs of 85%-100% in 2024 suggests the formal AIFTA preference is commercially irrelevant in these lines. Exporters face compliance costs equivalent to an 85%-100% tariff in terms of volume suppression, even though the customs tariff is zero. The economic loss is not easily measured with precision, but a conservative estimate using the trade-suppression coefficients and the relevant bilateral trade flows implies that India’s agricultural exports to ASEAN would be 30%-50% higher in the absence of the NTM regime in the 11 restrictive chapters—a gap that cumulates to billions of dollars over the 15-year window.