Co-authors: Abida Bilal, Ananya Gulati, Kiranmai, Shree Devi

Abstract

This paper asks whether India’s post-2014 Make in India tariff increases gave real protection to domestic manufacturers or backfired into an inverted duty structure. Using Corden’s effective rate of protection (ERP) formula, it tracks four sectors, mobile phones, televisions, automobiles, and air conditioners, from 2005 to 2023, drawing tariff data from UNCTAD TRAINS/WITS and cost shares from the 2015-16 India Input-Output Transactions Table. Mobile phones show the clearest case of inversion: ERP stayed negative from 2005 to 2018 because finished handsets faced near-zero duty while components like camera modules and connectors were taxed at 6-15%. That reversed only in 2019, when the output tariff rose to 7.5% and ERP turned positive for the first time. Televisions and air conditioners held positive ERP throughout, and automobiles ran far above nominal protection on the strength of a pre-existing high-tariff regime, not any post-2014 policy shift. A cross-check against import values from the same HS codes finds that import declines in phones and ACs line up with the 2019 tariff jump, though the PLI scheme and the pandemic muddy any causal claim. The paper concludes that Make in India’s flagship electronics sector spent over a decade taxing the very domestic assembly it was meant to protect.

Keywords: Effective rate of protection (ERP), inverted duty structure, Make in India, manufacturing, tariff policy, input-output analysis, trade protection

1. Introduction

Presently finished capital equipment for handset lines can be imported at zero duty but the key parts that are required to build this key machinery attracts about 5-25% levies (Ministry of Electronics and Information Technology, 2022). This system of higher import tariffs on intermediaries and capital goods relative to final goods poses the problem of creating an Inverted Duty Structure. This issue is further exacerbated China curbed exports of high-end machinery and rare earth-linked equipment which has essentially disrupted supplies. This pushes Indian manufacturers to localise equipment which becomes doubly difficult under a tariff regime that penalises them for doing so.

The irony is that India’s own Economic Survey for 2025-26 has already diagnosed the problem, in its own words- tariffs skewed toward intermediates over final goods actively discourage the very domestic manufacturing they are meant to protect. The Union Budget that followed days later did little to fix it.

To note, Make in India’s declared objective is to raise domestic value addition and to reduce import dependence in manufacturing. The implicit assumption behind this objective is that protection should rise while moving downstream-this being the standard cascading structure (Pathania & Bhattacharjea, 2020). The tariff data in India in sectors like electronics show the reverse gradient, the “inverted duty structure”

GST 2.0 has been presented to simplify the tax system by bringing in a primary two-slab system. The tax reforms have corrected IDS in labour-intensive and agri-input sectors such as textiles and fertilizers. The Economic Survey claims that residual IDS issues will be addressed through fully automated refunds of unutilised Input Tax Credit (ITC). This fails to consider that the conditions required to claim ITC can trap working capital, posing a devastating blow to MSMEs. Having funding locked up completely impairs their ability to run production cycles, buy supplies and settle vendor payments because they rely so heavily on steady cash flow and have such few credit options.

To expand upon this argument, sectors where IDS can be observed are looked at here. The new policy aims to make medicine cheaper but has made production far more difficult for domestic producers. The tax on finished medicines was cut from 12% to 5% or even down to zero for life saving drugs, but the tax on key ingredients has stayed at 18%. This creates a serious working capital problem for producers. Another case of this is observed in the bicycles/tractors industry, they attract a tax of just 5%, but the steel used to make them is still at 18%.

The reason an inverted duty structure matters economically, not just as an accounting curiosity, is what it does to a manufacturer’s effective protection, as distinct from the tariff rate printed on the finished goods. A nominal tariff tells you how much tax is charged on the final product at the border; it says nothing about how much of that protection actually survives once the manufacturer’s own costs are accounted for (Corden, 1966). When the tariff on inputs is low relative to the output tariff, the manufacturer keeps most of that output-side protection as a cushion on their value added. But when the relationship reverses – when the raw materials, components, or capital equipment going into production are taxed more heavily than with the finished goods coming out that cushion shrinks. The manufacturer is paying a tariff-inflated price for their inputs while competing against an import that never carried that same cost burden. Pushed far enough, this can eliminate the protective effect entirely: the effective rate of protection on domestic value addition falls not just below the nominal output tariff, but below zero – meaning the tariff structure, in net terms, taxes domestic manufacturing rather than protecting it (Bhattacharjea, Pathania & Sinha, 2022). It effectively becomes a system that penalizes domestic production rather than aiding it.

This paper poses the question- Did India’s post-2014 tariff increases under Make in India provide meaningful effective protection to domestic manufacturers in targeted sectors of air conditioners, televisions, mobile phones and automobiles or has simultaneous increase in intermediate input tariffs create an inverted duty structure that eroded the nominal protection offered to output producers?

This paper attempts to contribute in the following manner-

1. It seeks to identify the sector/products where Inverted Duty Structure can be observed.
2. Compute ERP and present how the targeted sectors diverge and showcase an inversion in the basic cascading tariff structure.

2. Literature Review

2.1 The Case for Tariff Protection

The use of tariffs as an instrument of industrial policy has long been a central debate in international economics. The traditional justification stems from the Infant Industry Argument, advanced by Alexander Hamilton and Friedrich List, which argues that newly established industries in developing economies require temporary protection from foreign competition until they acquire economies of scale, accumulate technological capabilities, and become internationally competitive.

Building upon this framework, Melitz (2005) examines when and how infant industries should be protected. Melitz develops a dynamic model demonstrating that protection is welfare-enhancing only under specific conditions, particularly when industries exhibit learning-by-doing externalities and the potential for productivity growth. The study argues that the duration and form of protection should be tailored to industry characteristics, implying that tariffs can be justified as temporary instruments of industrial development rather than permanent barriers to trade.

This conditional perspective has informed many contemporary industrial policies, including India’s Make in India initiative, which sought to strengthen domestic manufacturing through a combination of tariff increases and complementary measures such as Production Linked Incentive (PLI) schemes.

2.2 The Case Against Tariff Protection

Despite its theoretical appeal, infant industry protection has faced substantial criticism. Baldwin (1969) argues that while market failures may justify government intervention in principle, tariffs are generally an inefficient policy instrument because they distort both production and consumption decisions. Governments often lack sufficient information to identify industries with genuine long-term comparative advantage, increasing the likelihood that protection is extended to inefficient firms. Prolonged tariff protection may therefore encourage rent-seeking behaviour, reduce competitive pressure, and weaken incentives for technological upgrading.

Empirical evidence similarly presents mixed conclusions. Irwin (2000) examines the historical development of the U.S. tinplate industry, frequently cited as a successful example of infant industry protection. Although tariffs contributed to the expansion of domestic production, Irwin concludes that the associated welfare gains were insufficient to outweigh the economic costs of protection. The findings suggest that while tariffs may stimulate industrial growth in certain circumstances, they cannot be regarded as universally efficient policy instruments.

Taken together, these competing perspectives suggest that the effectiveness of tariff protection depends not simply on higher tariff rates but on whether such protection translates into sustained improvements in domestic production, productivity, and value addition.

2.3 Effective Rates of Protection as a Measure of Trade Protection

The debate surrounding tariff protection also raises an important methodological question: how should protection be measured? While policy discussions often focus on nominal tariff rates, these do not necessarily reflect the actual incentives available to domestic producers because they ignore tariffs imposed on intermediate inputs.

The concept of Effective Rate of Protection (ERP) was formalised by Corden (1966), who demonstrated that nominal tariffs on finished goods systematically misrepresent the degree of protection actually received by domestic producers. By accounting for the tariff-induced burden on intermediate inputs through an input-output framework, Corden showed that value-added protection may diverge substantially from headline duty rates and can even become negative when input duties are sufficiently high relative to output tariffs. This insight forms the theoretical basis of the ERP methodology adopted in this study.

2.4 Empirical Evidence on Effective Protection

Early empirical application of the ERP framework to India was undertaken by Das (2003), who estimated Corden-style ERPs for 72 Indian manufacturing industries between 1980 and 2000. Despite successive rounds of tariff liberalisation during the 1990s, Das found that effective protection remained both high and uneven across industries, with electronics and capital goods

displaying substantial divergence between nominal and effective rates. This provides an important benchmark against which the post-2014 Make in India period can be evaluated.

The most directly relevant empirical contribution is Pathania and Bhattacharjea (2020), who document widespread inverted duty structures in Indian electronics, optical products, computer equipment, and pharmaceuticals between 2000 and 2014. Their analysis shows that domestic producers in these industries often experienced negative effective protection, despite apparently favourable nominal tariff rates, because tariffs on imported intermediate inputs exceeded protection on final goods. Their findings establish the structural conditions inherited by the Make in India programme and motivate the central question of this study: whether post-2014 tariff reforms corrected or reinforced these distortions.

Extending this work, Bhattacharjea, Pathania and Sinha (2022) develop a theoretical model demonstrating that negative ERP is not necessarily evidence of policy failure. Using a Cournot duopoly framework, they show that while inverted duty structures and negative ERP are closely related, they are conceptually distinct. Under particular market conditions—including economies with relatively small domestic markets and lower wages—negative ERP may represent a welfare-maximising policy outcome. These findings are especially relevant for India’s electronics sector, where production remains deeply integrated into regional supply chains dominated by China.

International evidence further reinforces the usefulness of ERP as a measure of trade protection. Laksono (2024) separately analyses output tariffs, input tariffs, and ERP to examine the effects of trade liberalisation on manufacturing markups in Indonesia. The study finds that reductions in output tariffs decrease producer markups, whereas lower input tariffs improve productive efficiency, highlighting the importance of distinguishing between nominal and effective protection. Similarly, Dinh et al. (2020) apply an input-output ERP framework to Vietnamese agriculture and demonstrate that tariffs on imported inputs substantially erode nominal protection across sectors.

Using firm-level historical data, Ostermeyer (2025) examines nineteenth-century Swedish manufacturing and similarly concludes that substantial divergence exists between nominal and effective protection across industries. The study demonstrates that policy evaluations based solely on headline tariff rates may significantly misrepresent the incentives facing producers.

Finally, Goldar (2022) links ERP directly to industrial performance in India by showing that changes in effective protection following the tariff liberalisation of the 1990s had measurable effects on Total Factor Productivity growth in manufacturing. This establishes ERP not merely as a descriptive indicator of tariff structures but as a variable with tangible implications for industrial performance. The findings reinforce the importance of evaluating Make in India through effective rather than nominal protection, particularly in industries such as mobile phones where complex supply chains make producers highly sensitive to tariffs on intermediate inputs.

2.5 Research Gap

Although the literature has established both the theoretical importance of effective protection and the prevalence of inverted duty structures in Indian manufacturing, relatively little research has examined how the tariff changes introduced under Make in India altered effective protection across industries. Existing studies primarily focus on the pre-2014 period or analyse individual sectors in isolation. This study addresses that gap by estimating Effective Rates of Protection for four strategically important manufacturing industries-mobile phones, televisions, automobiles, and air conditioners-between 2005 and 2023, thereby evaluating whether the tariff reforms associated with Make in India strengthened or weakened effective protection for domestic manufacturers.

3. Methodology

3.1 Analytical Framework

This study employs the Effective Rate of Protection (ERP) to assess whether India’s post-2014 tariff increases under Make in India generated meaningful net protection for domestic manufacturers or produced an inverted duty structure that penalised domestic value added. The ERP is calculated using the standard Corden (1966) formula:

ERP = (tᵢ − Σaᵢⱼtⱼ) / (1 − Σaᵢⱼ)

Where tᵢ is the MFN applied basic customs duty on the finished good, tⱼ is the tariff on input j, and aᵢⱼ is the cost share of that input in total production. The denominator represents free-trade value added as a share of the output price. When ERP exceeds the nominal tariff, input duties are sufficiently low that the producer receives amplified protection. When ERP falls below the nominal tariff, input duties are eroding that protection. When ERP turns negative, the cost burden of input tariffs exceeds the benefit of the output tariff entirely which is called the inverted duty structure condition, and the domestic producer is effectively penalised despite nominal protection being in place.

The analysis covers four sectors: mobile phones (HS 8517), televisions (HS 8528), automobiles (HS 8703), and air conditioners (HS 8415), each with between four and five key intermediate inputs identified at the HS-4 digit level. ERP is computed annually for each sector across the full study period.

3.2 Dataset

Tariff data for all output and input HS codes are sourced from the UNCTAD TRAINS database accessed via the World Integrated Trade Solution (WITS) platform, covering 2005 to 2023. For years where WITS data shows classification gaps, specifically the PCBA sub-heading under mobile phone and Television components, rates are edited using official Central Board of Indirect Taxes and Customs (CBIC) budget notifications.

Input cost shares (aᵢⱼ) are derived from the Input-Output Transactions Table for India 2015-16 published by Brookings India (Chadha et al., 2020), which draws on MoSPI’s official Supply and Use Tables. Since the I-O table’s sectors don’t line up one-to-one with HS codes, each input HS code was manually matched to the I-O row/column it fits best. To get the cost share for an input in a given sector: sum that input’s row value(s) within the target sector’s column, then divide by the column’s total output value. Where more than one HS code mapped to the same I-O row, those values were summed into a single combined input before calculating the share. This is consistent with standard practice in the ERP literature

3.3 Assumptions and limitations

Several assumptions underlie the ERP estimates in this study.

First, a small-country assumption holds throughout: world prices of inputs and outputs are treated as fixed regardless of India’s tariff changes, so tariffs are assumed to pass through fully to domestic prices without affecting global supply or demand.

Second, the formula relies on fixed input coefficients from a single Leontief-type input-output matrix (2015-16). Cost shares (aᵢⱼ) are held constant across the full 2005-2023 window. Technological change, input substitution, or shifts in sourcing patterns over nineteen years are not captured. This is a particular concern for mobile phones, where the production structure changed substantially as domestic assembly scaled up under the PLI scheme.

Third, no substitution effects are modelled. The formula assumes producers cannot substitute away from tariffed inputs in response to price changes, which likely overstates the cost burden in later years as domestic alternatives emerged for some components.

Fourth, non-traded inputs and labour are implicitly assumed to face zero tariff. This may overstate ERP where domestic inputs carry embedded upstream tariff costs.

Fifth, the HS-to-I-O mapping involved judgment at the matching stage. HS codes and I-O sector classifications do not align one-to-one, so a different mapping convention could shift aᵢⱼ values and therefore ERP estimates. Section 3.4 addresses this through sensitivity analysis, testing ERP under ±10 percentage point variations in cost shares.

4. Results

Table 1: Effective Rate of Protection (ERP) for the respective industries

ERP calculated using Corden’s (1966) method, based on WITS tariff data and Brookings India Input-Output Table (2015–16) cost shares.

This section presents Effective Rate of Protection estimates for mobile phones, televisions, automobiles, and air conditioners across 2005-2023, assessing whether Make in India’s tariff architecture generated genuine protection for domestic value addition or produced an inverted duty structure that left domestic manufacturers worse off than importers of the finished good.

Mobile phones stand out as the only sector with persistently negative ERP across the study period, running from -2.64% in 2005 to -1.14% by 2018. The mechanism is straightforward: throughout 2005-2016, the finished phone attracted zero basic customs duty while tariffs on camera modules ranged from 5.97% to 11.25% and connectors from 6.97% to 15.00%. Taxing the components while leaving the finished import untaxed compressed value-added protection below zero, penalising domestic assembly despite the absence of any nominal import barrier. The post-2014 Make in India policy initially did not resolve this, the output tariff remained near-zero through 2018 even as input duties held firm, sustaining the inversion. The reversal came from 2019 onwards, when the output tariff on finished phones rose to 7.50% and eventually 10.32% by 2022-23, pushing ERP into positive territory for the first time, reaching 9.98% by the end of the study period.

The television sector showed no inversion at any point. ERP remained above the nominal tariff throughout, declining from 15.77% in 2005 to a trough of around 6.30% during 2011-15 before recovering to 12.75% by 2022-23 as output tariffs rose. Input tariffs on display panels, PCBs, and TV parts stayed consistently below the output tariff, meaning the sector received amplified rather than eroded protection across all nineteen years.

Automobiles recorded the highest ERPs of any sector, ranging from 78.17% to 166.67% against a nominal output tariff of 60-125%. With input tariffs on engines, gearboxes, auto components, and windshields held in the 7-15% range, the dominant output tariff overwhelmed any cost burden from components. This does not, however, represent a Make in India achievement,the automobile sector entered 2014 already shielded by one of the most protective tariff regimes in Indian manufacturing, and the post-2014 period simply maintained that structure rather than transforming it.

Air conditioners sustained positive ERP throughout, rising from 15% in 2005 to approximately 25% by 2020-23. Before 2019, ERP held marginally above the 10% nominal tariff at around 11%, indicating that the compressor, the largest single cost component, was not heavily enough taxed to erode protection materially. The 2019 doubling of the output tariff to 20% widened this gap further. Unlike mobile phones, where low output tariff combined with high input duties drove inversion, the AC sector maintained a configuration in which the output tariff consistently outpaced the weighted input burden.

Taken together, the four sectors reveal an uneven picture. Mobile phones – the flagship Make in India sector – were the only case of genuine tariff inversion, with negative ERP persisting for over a decade before the 2019 corrections took hold. Televisions and air conditioners received consistent positive protection throughout, while automobiles benefited from a pre-existing legacy of very high output tariffs that no input tariff could realistically erode.

4.1 Sensitivity Analysis

Given the uncertainty inherent in HS-to-I-O cost share mapping, ERP estimates are recalculated under alternative cost-share assumptions: each sector’s largest-share input is scaled by ±10% and ±20% relative to its baseline value, and, as a more conservative joint test, all input cost shares are scaled by ±20% simultaneously. Table 2 presents results for the mobile phone sector, for the most relevant years, where the sign change is of most analytical interest; full results for all four sectors appear in the Appendix.

Table 2: ERP sensitivity analysis for mobile phones (most relevant years)

ERP is recalculated with each sector’s largest cost-share input (aij) scaled by ±10% and ±20% relative to its baseline value, and, as a more conservative joint test, with all input cost shares scaled by ±20% simultaneously

Across every scenario tested, including the joint ±20% shock, mobile phones record negative ERP in every year from 2005 to 2018 and positive ERP in every year from 2019 to 2023, confirming that the inverted duty finding, and its reversal point, is not sensitive to the specific aᵢⱼ (cost share) values used. Televisions, automobiles, and air conditioners show the same pattern: positive ERP throughout the study period under all scenarios, with no sign change in any year. The core finding is that there is inversion in mobile phones only, no inversion in the other three sectors, it is therefore robust to reasonable variation in cost-share assumptions.

5. Discussion

The results establish that mobile phones and air conditioners underwent a comparable shift in 2019: output tariffs rose sharply, and ERP crossed from marginal or negative territory into a materially higher band. Import data from the same HS codes (8517 and 8415) offers a way to check whether that shift left a footprint in trade flows, independent of the ERP calculation itself.

For mobile phones, finished-good imports rose almost every year from 2005 through a peak of $20.6 billion in 2017, then fell to $18.7 billion in 2018 and $13.5 billion in 2019, the same year the output tariff jumped to 7.5% and ERP turned positive for the first time in the series. Imports stayed near that lower level through 2020 and 2021 and had only partially recovered to $16.5 billion by 2023, still below the 2017 peak. Air conditioners show a milder version of the same pattern: imports peaked at $1.31 billion in 2018, then fell every year through 2023 ($0.73 billion), the period during which the output tariff doubled to 20% and ERP rose from roughly 11% to roughly 25%.

The 2019 ERP jump was larger for air conditioners (roughly 14 percentage points, from 11% to 25%) than for mobile phones (roughly 9 points, from -1.6% to 7.3%). This gap reflects the relative size of the output tariff increase, the AC output tariff doubled from 10% to 20%, a larger proportional shift than the phone tariff’s move from near-zero to 7.5%, along with a lower weighted input burden in ACs, where the compressor tariff hadn’t risen as aggressively as PCBA. The larger ERP jump in ACs may partly explain why the import decline in that sector was more sustained post-2019 than in phones, though the PLI scheme’s electronics-specific targeting makes a direct comparison difficult.

Automobiles complicate this reading. ERP in this sector was already the highest of the four before 2014 and changed little afterward, yet imports more than tripled between 2019 and 2023, from $0.22 billion to $0.73 billion, off a small base. Televisions show no clear break at all: the 2018-19 import peak collapsed in 2020, which lines up far better with the pandemic than with the ERP path, which had already started recovering before that drop.

The automobile and television sectors serve as an implicit falsification test. If ERP were mechanically driving import volumes, automobiles, which recorded the highest ERP of the four sectors throughout, should show the strongest import suppression. Instead, auto imports more than tripled between 2019 and 2023. This suggests ERP operates differently depending on the regime: in sectors where protection is already very high and stable, marginal changes in input tariffs have little additional effect on trade flows. The ERP-import relationship appears to activate mainly when a large regime shift moves a sector from negative or very low ERP into significantly positive territory, as happened with phones and ACs in 2019, rather than when protection is adjusted incrementally within an already-high band.

Set against the aggregate tariff-import correlation, based on WITS data, found no significant relationship between India’s average tariff rate and total import volume across 2010-2023 (r = – 0.02), these sector patterns are worth noting rather than dismissing. That null result held once every HS code and year was pooled together. The mobile phone and air conditioner cases suggest that where protection changed enough to move ERP into a new range, product-level import trends broke at roughly the same point, even though that relationship washes out once averaged across the whole economy.

A decade of negative ERP between -1% and -2.6% in mobile phones raises the question of whether the 2019 correction, bringing ERP to 7.3%, was large enough to plausibly change producer behaviour, or whether it was more of a symbolic policy correction. The answer depends on what threshold of effective protection actually triggers investment decisions in assembly-oriented electronics. Since the PLI scheme, launched in 2020, added production-linked incentives on top of the tariff correction, separating the ERP effect from the subsidy effect in the post-2019 period is difficult. What the data does establish is that a decade of negative ERP coincided with a decade of rising finished-good imports, which is the opposite of what import substitution policy intended, and that both trends reversed at roughly the same point.

This is not evidence that the 2019 tariff changes caused the import decline in phones and ACs. 2019 also marks the early ramp-up of the Production-Linked Incentive scheme for mobile manufacturing, which independently targeted import substitution in electronics assembly, and 2020 introduces a pandemic shock affecting every sector’s trade data regardless of tariff policy. The import figures used here are values, not physical quantities, so exchange-rate and price movements are folded in as well. What the alignment supports is a narrower claim: the sectors where this paper finds ERP moved most sharply are also the sectors where finished-good import trends broke most visibly from their prior path. That is worth flagging for follow-up work using quantity-based trade data and a formal before/after test rather than a visual comparison of two series.

6. Conclusion

This paper examined a central question underlying India’s Make in India strategy: did the tariff increases introduced after 2014 provide meaningful protection to domestic manufacturers, or were their intended benefits offset by higher tariffs on imported intermediate inputs through an inverted duty structure? This question is important because much of India’s industrial policy has implicitly assumed that raising tariffs on final goods automatically increases protection for domestic manufacturing. By estimating Effective Rates of Protection (ERP) across four strategically important industries, this study demonstrates that this assumption does not hold uniformly. Rather

than nominal tariff rates, ERP provides the more appropriate measure of whether domestic value addition is genuinely protected or inadvertently penalised (Corden, 1966).

The results reveal substantial heterogeneity across industries. Mobile phones represent the clearest case of tariff inversion. Despite being the flagship sector of the Make in India programme, ERP remained negative between 2005 and 2018 because several critical intermediate inputs attracted tariffs while finished mobile phones entered largely duty free. Consequently, the tariff structure reduced rather than enhanced protection for domestic value addition, despite appearing supportive when judged solely by nominal tariff schedules. Only after tariff revisions in 2019 did ERP become positive, reaching approximately 10 per cent by the end of the study period. This finding reinforces earlier evidence that inverted duty structures imposed a significant burden on India’s electronics manufacturing sector (Pathania & Bhattacharjea, 2020).

In contrast, televisions and air conditioners exhibited consistently positive Effective Rates of Protection throughout the study period, indicating that tariff structures generally favoured domestic value addition and did not experience inverted duty structures. The automobile industry presents a different trajectory. Although it recorded the highest ERP among the four sectors, this largely reflected a long-standing protective tariff regime that predated Make in India. Consequently, attributing the sector’s high level of effective protection solely to post-2014 policy reforms would overstate the programme’s contribution. Taken together, these findings suggest that tariff protection under Make in India was not a uniform policy outcome but depended largely on the tariff structures inherited by individual industries.

The broader empirical evidence further suggests that changes in ERP alone are insufficient to evaluate the effectiveness of industrial policy. Import trends reveal that mobile phones and air conditioners experienced noticeable changes around 2019, coinciding with significant shifts in Effective Rates of Protection, whereas televisions and automobiles did not display comparable relationships between ERP and imports. At the aggregate level, the correlation between India’s average tariff rate and total import values remained negligible, indicating that tariff policy alone cannot explain changes in trade patterns. These findings imply that while ERP appears to have influenced  outcomes  in  sectors  where  tariff  structures  changed  substantially,  industrial performance is also shaped by broader factors including global demand conditions, supply chain integration, production incentives, and macroeconomic shocks such as the COVID-19 pandemic.

This interpretation is consistent with the broader literature on industrial policy. While proponents of infant industry protection argue that temporary tariffs can promote industrial learning under conditions of market failure (Melitz, 2005), critics caution that prolonged protection may reduce competitive pressures and encourage inefficient allocation of resources (Baldwin, 1969). Empirical evidence likewise suggests that although tariffs may stimulate industrial expansion in specific contexts, they do not automatically generate sustained productivity growth or welfare improvements (Irwin, 2000). The findings of this study align most closely with this conditional perspective. Tariff protection appears to be most effective when accompanied by complementary industrial policies that strengthen domestic productive capacity rather than relying solely on higher duties on final goods.

The policy implications are therefore clear. Raising tariffs on final goods without simultaneously evaluating tariffs on imported intermediate inputs may produce the opposite of the intended outcome by reducing effective protection for domestic manufacturers. The experience of India’s mobile phone industry illustrates how an inverted duty structure can persist despite an industrial policy explicitly designed to encourage domestic production. More broadly, the findings support the concern raised in the Economic Survey 2025–26, which identifies inverted duty structures in sectors such as pharmaceuticals and bicycles as continuing obstacles to domestic manufacturing. Rather than focusing exclusively on nominal tariff increases, future tariff policy should incorporate systematic Effective Rate of Protection (ERP) assessments before tariff revisions are implemented. Such evaluations would allow policymakers to identify situations where input tariffs inadvertently offset protection provided to final goods and would contribute to more coherent industrial policy design.

Beyond tariff design, the results also suggest that sustained manufacturing competitiveness depends on complementary policies that deepen domestic value chains. Higher Effective Rates of Protection are unlikely to generate substantial gains in domestic value addition unless accompanied by investments in domestic component manufacturing, technology acquisition, logistics, research and development, and supplier development. In this sense, tariffs should be viewed as one instrument within a broader industrial strategy rather than as a standalone mechanism for promoting manufacturing competitiveness.

This study contributes to the literature in three ways. First, it extends the application of the Corden framework by estimating Effective Rates of Protection for four strategically important manufacturing industries over the period 2005–2023, thereby evaluating the evolution of protection before and after the introduction of Make in India. Second, it demonstrates that nominal tariff schedules may substantially misrepresent the incentives facing domestic producers, reinforcing the importance of ERP as the appropriate metric for evaluating industrial policy. Finally, by combining ERP estimates with sectoral import trends, the analysis provides a more comprehensive assessment of how tariff structures may influence manufacturing outcomes than would be possible through tariff schedules alone.

Several limitations should be acknowledged. The analysis relies on manually matched HS tariff lines and Input–Output coefficients, introducing a degree of judgement in the concordance process despite the consistent methodology applied throughout. ERP estimates are based on the 2015–16 Input–Output table and therefore assume fixed production technologies over the study period, limiting the ability to capture structural changes in production processes. Furthermore, import analysis is based on trade values rather than quantities, while the post-2019 period coincides with the introduction of the Production Linked Incentive Scheme and the COVID-19 pandemic, making it difficult to isolate the effects of tariff policy alone.

Future research could extend this analysis by incorporating updated Input–Output tables, quantity-based trade data, firm-level production data, and causal identification strategies to examine more precisely how changes in Effective Rates of Protection influence domestic value addition, import dependence, productivity, and long-run industrial competitiveness.

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Appendix

ERP Sensitivity Analysis (Full Series, 2005–2023)

1.Mobile Phones

Largest cost-share input perturbed one-at-a-time: Connectors (HS 8536). No scenario reverses the sign of ERP in any year, 2005–2023.

2.Televisions

Largest cost-share input perturbed one-at-a-time: TV Parts (HS 8529). No scenario reverses the sign of ERP in any year, 2005–2023. 

3. Automobiles

Largest cost-share input perturbed one-at-a-time: Auto Components (HS 8708). No scenario reverses the sign of ERP in any year, 2005–2023.

4. Air Conditioners

Largest cost-share input perturbed one-at-a-time: Copper Tubes (HS 7411). No scenario reverses the sign of ERP in any year, 2005–2023.