The two commodities that explain almost everything

Look past the aggregate numbers and the deficit resolves into something much simpler than “structural imbalance” implies: it is, to a striking degree, an oil story with a gold problem attached.

Crude petroleum alone accounts for USD 559.1 billion of cumulative imports over the eight-year window — the single largest import line by a wide margin — with refined petroleum products (USD 411.0 billion) close behind. Add gold (USD 285.9 billion) and rough diamonds (USD 112.1 billion), and four commodity lines account for a meaningful share of India’s entire eight-year import bill.

On the export side, the same commodity — petroleum — shows up again, but reversed. Refined diesel (USD 131.5 billion), petrol (USD 62.0 billion), aviation turbine fuel (USD 56.7 billion), and other refined products together make petroleum India’s largest export category too, because Jamnagar and India’s other mega-refineries buy crude, process it, and re-export the output at a margin. India imports the raw material for its top export.

The rule of thumb buried in this table: a USD 10 per barrel move in crude prices shifts India’s annual trade balance by roughly USD 15–18 billion, in either direction, more or less automatically — before a single policy decision gets made.

What “concentration” actually looks like, numerically

Trade economists measure how spread-out (or bunched-up) a country’s trade basket is using the Herfindahl-Hirschman Index — the same tool used to measure market power in antitrust cases, repurposed here to measure commodity concentration. A score under 1,000 signals a diversified basket; above 2,500 signals dangerous concentration in a handful of goods.

India’s export HHI sits at 569 — genuinely diversified, helped by the fact that petroleum’s dominance is offset by sizeable pharmaceutical, engineering, and gems exports sitting underneath it. India’s import HHI sits at 1,390 — noticeably more concentrated, driven by the same three culprits repeatedly: crude oil, electronics, and gold. India’s import basket, in other words, has fewer places to hide from a shock than its export basket does — consistent with bilateral-trade research showing India runs deficits with nine of its ten largest trading partners simultaneously, a pattern that recurring studies attribute less to any single bilateral relationship than to exactly this kind of concentrated import dependence (bilateral trade performance research, 2026).

The three-phase story hiding inside “eight years of deficit”

Zoom into the year-by-year numbers and the deficit isn’t a flat, unchanging feature — it moves through three distinct regimes.

Phase one, the contraction: 2019–20, when both flows collapsed roughly 22% as COVID lockdowns shut down global demand and supply chains simultaneously — and, paradoxically, this is the year with the smallest deficit in the dataset, at USD 96.8 billion, purely because imports fell faster than exports did.

Phase two, the expansion: 2021–22, a V-shaped recovery so sharp that exports hit an all-time high of USD 453.2 billion in 2022, driven by a global commodity super-cycle and pent-up post-pandemic demand. The deficit widened sharply in the same window, because imports rebounded even faster.

Phase three, the plateau-with-a-widening-gap: 2023 onward, where exports have essentially flatlined in the USD 430–450 billion range for three consecutive years while imports kept climbing, pushing the deficit to a dataset record of USD 310.2 billion in 2025 — nearly triple the 2020 trough.

That third phase is the one that should worry policymakers more than the first two. A deficit that widens because a shock temporarily distorts trade flows is self-correcting. A deficit that widens because exports have stopped growing while imports keep growing is not.

Diversification is real — but it’s a rounding error next to petroleum

It would be unfair to the data to say nothing is changing on the export side. Pharmaceuticals, engineering goods, electronic components, and agricultural products have all genuinely expanded their share of the export basket over the eight-year window, and India’s export HHI has drifted marginally lower as a result. Basmati rice alone brought in USD 34.5 billion cumulatively — a top-ten export commodity in its own right, and a reminder that agricultural trade is not a footnote in this dataset.

But scale it against the numbers above: the top five HS chapters still account for over 60% of total exports. Diversification is happening at the margin, not at the core. The core is still petroleum, gems, and machinery, exactly as it was in 2018.

The import line that’s actually new

If there’s a genuinely fresh structural pressure in this dataset — something that wasn’t as prominent in earlier decades of India’s trade history — it’s electronics. Monolithic integrated circuits alone account for USD 75.7 billion in cumulative imports, and the electronics import bill overall has grown faster than any other major category across the eight years, driven simultaneously by consumer demand (smartphones, laptops) and by the assembly requirements of India’s own Production-Linked Incentive manufacturers, who import components to build finished electronics domestically. It’s an uncomfortable irony: a policy designed to reduce import dependence over the long run is, in the short run, adding to the import bill it’s meant to eventually shrink.

What the ledger says about the next decade

Three forces will determine whether India’s trade deficit narrows or keeps widening through the next cycle. The energy transition should, mechanically, reduce both crude imports and refined-product exports over time — shrinking the two biggest lines on both sides of the ledger simultaneously, a wash on the trade balance but a genuine reduction in exposure to oil-price shocks. The success (or failure) of import-substitution manufacturing in electronics and capital goods will determine whether the fastest-growing import category keeps growing. And market diversification — away from a trading system still centred on the US and China, toward partners in Africa, the Middle East, and Southeast Asia — will determine how much of any future export growth actually shows up as diversification, rather than just more of the same commodities sold to more of the same three buyers.

None of that changes the headline fact sitting at the bottom of eight years of tariff-line data: USD 1,755.4 billion of cumulative deficit, in a straight, unbroken line, through a pandemic, a war, and a manufacturing push that hasn’t yet turned the trend around.

Literature Review

The empirical literature on India’s trade deficit has a long-running methodological debate at its core: is the deficit a sustainable feature of a fast-growing, capital-importing economy, or a structural imbalance that eventually forces a reckoning? An early and still-cited contribution applying structural-break cointegration techniques to India’s exports and imports found no stable long-run equilibrium relationship between the two series across six decades — evidence, the authors argued, that India’s trade position has not historically been self-correcting through the channels standard trade theory would predict, and that policy intervention rather than automatic adjustment has done most of the work in any period of narrowing. That finding, now over a decade old, sits comfortably alongside this analysis’s own three-phase reading of the 2018–2025 data: each phase break (2020 contraction, 2021–22 expansion, 2023 plateau) tracks an external shock or policy response, not a gradual equilibrium adjustment.

A second, more policy-oriented strand of literature — including recurring bilateral-trade-performance studies — has focused on the fact that India runs deficits with nine of its ten largest trading partners simultaneously, attributing this less to any single bilateral relationship than to the commodity concentration this piece quantifies via the HHI framework: energy, gold, and increasingly electronics import dependence that no single trade agreement or partner-diversification strategy can fully offset. Where this literature remains comparatively thin is on the electronics-import paradox highlighted above — the tension between PLI-driven component imports in the short run and import-substitution goals in the long run — an area that would benefit from more granular, HS-8-digit-level research as India’s electronics manufacturing base matures.

References

• The Sustainability of Trade Deficits in the Presence of Endogenous Structural Breaks: Evidence from the Indian Economy. North American Journal of Economics and Finance (ScienceDirect).
• India’s Bilateral Trade Performance with Major Trading Partners. (2026). The Academic (peer-reviewed working paper).
• India’s Trade Deficit: Increasing Fast but Still Manageable. Indian Council for Research on International Economic Relations (ICRIER)-affiliated commentary.
• Directorate General of Commercial Intelligence and Statistics (DGCI&S), Ministry of Commerce & Industry — monthly 8-digit HS code export/import series.
• Directorate General of Foreign Trade (DGFT) — trade policy and licensing data.

Data: DGCI&S / DGFT monthly 8-digit HS code series, April 2018–March 2026 (11,993 export codes, 11,551 import codes). Analysis by Pawan. A companion interactive dashboard is available here.

The number that took a quarter-century to matter

In FY2000-01, India registered 323 electric vehicles. In FY2025-26, registrations crossed roughly 25 lakh — a compound annual growth rate above 40% sustained across a quarter-century, through two recessions, a pandemic, and five changes of government. EV market share, essentially flat for the entire 2000s, crossed the 5% threshold that analysts treat as the marker of mainstream adoption in FY2022-23, and reached approximately 8.5% by the close of FY2025-26, up from 7.7% a year earlier.

That headline number understates how lopsided the Indian story is compared with the rest of the world. In Europe and the US, the EV conversation is dominated by passenger cars. In India, electric two-wheelers alone account for roughly 58% of all EV sales, and India commands more than half of global electric three-wheeler sales. These are not aspirational purchases by early adopters with driveways and home chargers. They are cost calculations made by gig-economy delivery riders, e-rickshaw operators, and small logistics fleets who do the fuel-savings math in their heads before they walk into a showroom — which is exactly why a fuel-price shock moves this market faster than it moves wealthier ones.

Six forces that had to line up

No single scheme produced a 40%-plus CAGR sustained over decades. It took several forces compounding in the same direction.

Policy layering. India’s EV incentive architecture has been built in stages: FAME-I from 2015, FAME-II from 2019 to 2024 (₹11,500 crore, supporting roughly 16.29 lakh EVs), a Production Linked Incentive scheme worth ₹26,058 crore for component manufacturing, and the current PM E-Drive scheme, which targets 2.5 million subsidised electric two-wheelers and 320,000 three-wheelers, backed by roughly $315 million in vehicle and charging-infrastructure subsidies. Choice-model research evaluating FAME’s actual counterfactual impact — rather than simply its uptake numbers — found the scheme genuinely induced incremental EV sales that would not otherwise have happened, rather than merely subsidising purchases already in the pipeline (Sheldon et al., 2019). It’s the stacking of demand-side subsidies on top of supply-side manufacturing incentives, sustained over a decade, that bent the curve — not any one of them alone.

A battery-cost collapse that happened to arrive at the right moment. Global lithium-ion battery prices have fallen more than 90% since 2010. That’s not an India story specifically, but its consequences are outsized in India because it pushes electric two- and three-wheelers past cost-parity with petrol equivalents inside a payback period an ordinary buyer can actually do the arithmetic on.

Charging infrastructure finally showing up where it’s needed. Public charging stations crossed roughly 27,000–29,000 by early-to-mid 2026, from near zero in 2019, with the PM E-Drive scheme explicitly targeting Tier-2 and Tier-3 city expansion — the geography where the next wave of buyers actually lives, and where range anxiety remains the sharpest barrier.

Legacy manufacturers finally showing up too. In electric two-wheelers, established players TVS, Bajaj, and Hero MotoCorp now control roughly 61% of the market, leaning on dealer networks and after-sales infrastructure that pure-EV startups can’t match overnight. In electric cars, the picture is actually getting more competitive rather than less: Tata Motors’ share of the electric passenger vehicle market fell from around 57% in FY2024-25 to roughly 39% in FY2025-26 as JSW MG Motor, Mahindra, and Hyundai expanded their own line-ups — a healthy sign for a market that was, until recently, close to a one-brand story.

The freight and logistics pivot. Electric goods vehicles — light, medium, and heavy trucks — grew 172% year-on-year in FY2025-26, the fastest-growing segment in the market, as e-commerce fleets converted for cost and ESG reasons and PM E-Drive incentives extended to trucks for the first time.

And then, the oil shock. A 2023 study in Energy Policy by Garg, Mishra and Sinha found that India’s crude-price elasticity is low in the short run — price signals alone don’t move behaviour fast. The 2026 Strait of Hormuz crisis changed the political economy of that finding. When a Prime Minister publicly urges citizens toward EVs and public transport, the oil shock has crossed into territory no subsidy programme alone had reached: direct political urgency, applied at the top.

The number that makes this a macro story, not a green one

India spent an estimated USD 134.7 billion on crude oil imports in FY2025-26. The International Energy Agency projects EV adoption and efficiency gains will avoid roughly 480,000 barrels a day of additional oil demand by 2030 (IEA, 2025–2026) — at current prices, an annual saving in the range of USD 18 billion, more than a full year’s worth of the entire cumulative FAME programme spend, recovered in avoided imports alone.

That reframes the EV transition for anyone still filing it under “environmental policy.” At the scale India now operates, it is an import-substitution and current-account story first, and a climate story second — which may be precisely why it has survived five changes of government intact.

Three gaps that could still stall it

The trajectory looks close to inevitable from a distance, but three specific weaknesses remain exposed at close range.

Geographic mismatch. Charging infrastructure is still concentrated in metros, while the buyers with the strongest cost-of-ownership case increasingly come from smaller cities. The infrastructure needs to arrive before that cohort reaches the showroom, not after — a sequencing problem, not a capacity one.

Shallow supply chains. India’s EV growth still rests substantially on imported battery cells, with China dominating global cell manufacturing. A demand shock originating in China would expose a vulnerability structurally similar to the one that produced the Secunderabad appeal in the first place — just routed through a different chokepoint.

A financing gap that isn’t really about interest rates. Electric two- and three-wheelers are disproportionately bought by micro-entrepreneurs and gig workers whose credit profiles don’t clear conventional auto-loan underwriting. Consumer-behaviour research on Indian EV adoption consistently ranks purchase price and financing access alongside range anxiety as the dominant barriers to uptake (Rathore et al., 2024; Singh et al., 2023), even as the same literature finds environmental concern playing a comparatively minor role in actual buying decisions for this segment. Until a targeted guarantee scheme closes the financing gap, a segment of buyers who are economically rational — the fuel savings genuinely pencil out — is locked out by the banking system rather than by preference.

What the data actually settles

Twenty-six years of registration data and a sustained 40%-plus CAGR have already answered the question of whether India’s EV transition happens. What remains open, entering FY2026-27 with EV penetration near 8.5% against a 2030 government target of 30%, is whether the supporting ecosystem — the charging grid, the battery supply chain, the loan desk for a three-wheeler operator in a Tier-3 town — can move at the same speed as a market that has already made up its mind. The Strait of Hormuz crisis didn’t create that market’s momentum. It just made the cost of ignoring the remaining gaps considerably more visible, to a Prime Minister and everyone else, all at once.

Literature Review

Academic work on Indian EV adoption splits along the same supply/demand line the article traces above. On the demand side, a substantial body of consumer-behaviour research — much of it applying the Technology Acceptance Model or the Theory of Planned Behaviour to Indian survey samples — consistently identifies purchase price, charging-infrastructure availability, and range anxiety as the dominant psychological barriers, with financial incentives and perceived environmental benefit as the leading pull factors. What this literature tends to under-weight, and what the two- and three-wheeler registration data foregrounds instead, is that for the majority of India’s actual EV buyers, adoption isn’t primarily an attitudinal decision at all — it’s a fleet-economics calculation made by gig workers and small operators, closer to a total-cost-of-ownership problem than a green-consumption one.

On the supply and policy side, Sheldon, Cui and colleagues’ choice-model analysis of the FAME scheme found the subsidy programme to be genuinely effective at inducing incremental EV sales rather than merely subsidising purchases that would have happened anyway — an important empirical anchor, since counterfactual effectiveness is notoriously hard to establish for vehicle subsidies internationally. More recent 2024–2025 studies applying Fuzzy AHP and structural equation modelling to expert and consumer panels have begun disaggregating barriers by segment (two-wheeler vs. four-wheeler, Tier-1 vs. Tier-2/3 city), a useful methodological shift given how differently those segments behave in the registration data. The literature has not yet, as of this writing, produced a rigorous causal estimate of how acute fuel-price shocks — of the kind India experienced in 2026 — shift adoption in the short run; the IEA’s demand-avoidance projections remain the best available approximation, and are treated as such in this piece.

References

• Sheldon, T. L., DeShazo, J. R., et al. (2019). Studies on FAME scheme effectiveness and counterfactual EV sales simulation, Energy Economics / related working papers.
• Rathore, A., et al. (2024). Barriers to EV Adoption in India: A Fuzzy Analytical Hierarchy Process Study. ScienceDirect.
• Singh, V., et al. (2023). Factors Affecting Adoption Intention of Electric Vehicles: A Cross-Cultural Study. Environment, Development and Sustainability (Springer).
• Enablers and Disablers to Plug-in Electric Vehicle Adoption in India: Insights from a Survey of Experts. (2021). ScienceDirect.
• The Dynamics of Electrified Vehicle Adoption: An Indian Market Analysis. (2026). ScienceDirect.
• An Empirical Study on Electric Vehicle Adoption in India: A Step Towards a Greener Environment. (2024). ScienceDirect.
• International Energy Agency. (2025–2026). Global EV Outlook — India country projections, oil-demand avoidance estimates.
• Ministry of Road Transport and Highways (MoRTH) / NITI Aayog — Vahan and ICED registration databases.
• JMK Research & Analytics — segment-wise EV sales tracking reports, FY2024-25 and FY2025-26.

Data: MoRTH / NITI Aayog ICED database (FY2000-01–FY2025-26); JMK Research & Analytics; International Energy Agency. Analysis by Pawan. A companion interactive dashboard is available here.

The basket, explained without the jargon

Every day, the Petroleum Planning & Analysis Cell publishes a number called the Indian Crude Oil Basket — a weighted average of Oman/Dubai sour crude (the heavier, cheaper-to-trade, harder-to-refine grade that dominates India’s Gulf imports) and Brent sweet crude (the lighter international benchmark). It typically trades 2–5% below Brent itself, and since 2022 that discount has widened further because of one specific development: India began buying large volumes of sanctioned-but-discounted Russian Urals crude, at times $20–35 a barrel cheaper than the prevailing international price.

Twenty-five years of this number, laid end to end, does not describe a market finding equilibrium. It describes a market lurching from shock to shock, with brief plateaus in between.

India now imports over 85% of the crude it consumes — the world’s third-largest crude importer by volume — and that dependence has essentially tripled since 1998-99, from roughly 75,000 thousand metric tonnes a year to over 230,000. Every $10 move in the basket price adds or subtracts approximately ₹1.25 lakh crore from India’s annual import bill. There is no other single commodity price that moves India’s macroeconomy this directly, this fast.

Why cheap oil didn’t make petrol cheap in 2020

Here is the detail that most retellings of the COVID oil crash leave out. When the basket price collapsed to near $20 a barrel in April 2020 — so low that some US contracts briefly traded negative, sellers paying buyers to take oil off their hands — petrol prices at Indian pumps barely moved. The reason wasn’t refining costs or currency effects. It was policy: the government raised excise duty on petrol by ₹10 a litre and on diesel by ₹13 a litre during exactly this window, capturing the windfall from cheap global oil as tax revenue rather than passing it to consumers.

Then the mirror image happened two years later. When the basket surged past $120 a barrel after Russia’s invasion of Ukraine, the government cut excise duty by ₹8 a litre on petrol and ₹6 a litre on diesel in May 2022, absorbing some of the shock rather than letting it flow through to the pump.

The pattern is consistent enough to be treated as a rule rather than an anomaly: the government is the actual price-smoothing mechanism in India’s fuel market, not the international crude cycle. Dynamic daily pricing, introduced in June 2017, made the pump price mechanically follow the international benchmark day by day — and academic research on India’s pass-through mechanism had already anticipated exactly this shift, finding that transmission from global to domestic prices strengthens sharply once administered pricing gives way to market-linked formulas (Bhattacharya & Sen, 2012). What that research could not fully anticipate was how deliberately the tax lever would then be used to work against that same transmission, in both directions, whenever it suited the exchequer.

Where every rupee of your ₹103 actually goes

Break down a litre of Delhi petrol at current prices and the picture is stark:

Roughly ₹45 of every ₹103 you pay for petrol in Delhi is tax. The crude oil itself — the thing everyone blames when prices rise — accounts for only about ₹25–30. Policy-institute research on India’s petroleum tax revenue has documented this asymmetry in detail: had the 2015–2020 fall in global crude prices been fully passed through, retail prices would have been meaningfully lower than they were; instead, much of that windfall was captured as excise revenue (ORF, 2024). This is also why petrol prices across Indian metros diverge so much despite drawing on the same refineries and the same crude: Maharashtra’s higher VAT rate keeps Mumbai consistently the most expensive of the four major metros, while Delhi’s comparatively moderate VAT has kept it the cheapest.

Kolkata’s numbers are the most interesting in that table: the highest 2022 peak of any metro, but the smallest cumulative rise since 2018 — evidence of a state government leaning harder on VAT relief than its peers once the acute crisis passed.

The household arithmetic

For a household with one two-wheeler commuter doing 20 km a day at roughly 40 km/litre, a ₹10 rise in petrol adds about ₹150 a month — not dramatic in isolation, but compounding. A commuter using 15 litres a month paid about ₹1,110 in 2018 at ₹74/litre; at 2025 prices of ₹103.50/litre, the same 15 litres costs ₹1,553 — an extra ₹443 a month, ₹5,310 a year, and more than ₹37,000 cumulatively across seven years, before accounting for anything else fuel prices touch indirectly: freight costs baked into vegetable prices, diesel costs baked into farming, ATF costs baked into flight fares.

The 2026 test

Which brings the story back to this year. The Strait of Hormuz tensions that pushed Brent above $110–115 in parts of 2026 are not a new category of risk — the basket has weathered the 2008 supercycle, the 2020 demand collapse, and the 2022 war-driven spike. What is new is India’s position going into this shock: a Russian crude relationship built over four years that has, at various points, supplied over a third of India’s imports at a discount, a strategic petroleum reserve programme that has matured since its early pilot phase, and — for the first time in the Basket’s 25-year history — a genuinely fast-growing electric vehicle fleet absorbing some of the marginal fuel-demand growth that would otherwise have made this shock even more expensive.

None of that insulates India from the current spike. It does mean the country is entering its latest oil shock with more shock absorbers than it had in 2008, 2014, or even 2022. Whether those absorbers are enough is now, as it has been for twenty-five years running, mostly a question of what happens in the Gulf — not in Delhi.

Literature Review

The empirical literature on India’s fuel-price pass-through has a clear before-and-after hinge point: 2010, when petrol pricing was formally deregulated (diesel followed in 2014). Bhattacharya and Sen’s foundational study on oil-price transmission found that pass-through to Indian retail prices strengthened markedly once administered pricing began giving way to market-linked formulas — precisely the mechanism this piece traces through the tax-versus-crude build-up of a Delhi litre. More recent work extends the same question to the asymmetry angle explored above: a 2022 study on diesel price reforms found India’s crude-import demand has become steadily more price- and income-inelastic over time relative to earlier estimates, consistent with the pattern this analysis documents of taxation, not crude cost, absorbing most of the swing in any given price cycle.

A parallel strand of policy-institute research — most usefully summarised in ORF’s recurring analyses of petroleum tax revenue — has quantified precisely how much of the 2015–2020 low-crude window was captured as excise revenue rather than passed to consumers, the same asymmetry this piece identifies independently from the raw price data. That literature also flags a genuine equity tension this piece does not resolve: fuel taxation is mildly regressive in incidence terms, even as it functions as a de facto carbon price — India’s implicit fuel excise tax per tonne of CO2 has been estimated to exceed comparable rates in the US and China. Whether that trade-off is being managed well, as opposed to simply being convenient for the exchequer, remains a live and under-researched question as India’s 2026 oil shock forces a fresh round of tax-versus-pass-through decisions.

References

• Bhattacharya, K., & Sen, P. (2012). Is the Oil Price Pass-Through in India Any Different? Energy Policy.
• Diesel Price Reforms and Asymmetric Oil Price Pass-Through to Inflation: An Indian Perspective. (2022). ScienceDirect / IIMB Management Review-affiliated study.
• Observer Research Foundation. (2024). India’s Tax Revenue from Petroleum Products: Golden Eggs That May Kill the Goose?
• Petroleum Planning & Analysis Cell (PPAC), Government of India — monthly Indian Crude Oil Basket and retail price bulletins.
• Indian Oil Corporation (IOC) — retail selling price data, four-metro series.
• U.S. Energy Information Administration (EIA) — historical Brent crude series.

Data: Petroleum Planning & Analysis Cell (PPAC), Indian Oil Corporation, U.S. Energy Information Administration. Analysis by Pawan. A companion interactive dashboard is available here.

The line that keeps climbing

Strip away the debates and the headline is simple. Gross GST collection has grown from ₹7.41 lakh crore in its first, partial year (FY2017-18) to ₹22.09 lakh crore in FY2024-25 — essentially a tripling in eight years. FY2025-26, still eleven months in as this is written, has already logged ₹26.18 lakh crore.

Two things jump out. The first is the V-shape around FY2020-21 — a 7% pandemic-year contraction followed by a 30.9% rebound, the sharpest single-year swing in the dataset, and a reminder that GST collection is really just a proxy for how much India is buying, selling, and building in any given month. The second is that growth has been decelerating in percentage terms even as the absolute numbers get larger — 59% in year two, 9.4% in year eight. That’s not decline; it’s a large base doing what large bases do. A tax system covering ₹22 lakh crore cannot keep growing at the pace of one covering ₹8 lakh crore.

What October 2025 quietly did to the numbers

Anyone squinting at FY2025-26’s monthly figures will notice something odd: growth, which had been running at 9–20% for most of the year, suddenly turns negative in October, November, and December 2025 — minus 0.3%, minus 8.0%, minus 4.2% — before recovering in January.

The explanation isn’t a slowdown in the economy. It’s a policy switch. The GST Compensation Cess — the special levy on tobacco, aerated drinks, and automobiles that was supposed to fund states’ revenue guarantees for exactly five years after GST launch — finally began its scheduled wind-down in October 2025. Cess collections that had run at roughly ₹23,449 crore in October 2024 fell to ₹14,340 crore in October 2025, then to ₹8,001 crore in November and ₹8,515 crore in December. The core taxes — CGST, SGST, IGST — kept climbing through this period. The headline number just stopped reflecting it, because one of its four components was being switched off in real time.

It’s a useful lesson in reading tax data: a “decline” in gross collection and a decline in economic activity are not always the same statement.

The refund machine got faster, not just bigger

A less-discussed but genuinely striking trend sits in the refund data. Annual GST refunds grew from ₹3.21 lakh crore in FY2020-21 to ₹6.43 lakh crore in FY2024-25 — a 5.94× increase — and the refund-to-gross ratio climbed steadily from 8.4% to 11.6% over the same period.

That sounds like the tax department giving away more money, but read the other way, it’s the opposite story. IGST refunds — overwhelmingly claims from exporters filing under the Letter of Undertaking scheme — dominate the refund book. A rising refund rate alongside rising net collections means India’s export base is growing and the GSTN’s processing pipeline is keeping pace with it, rather than sitting on exporters’ working capital the way it did in the system’s chaotic early years. Compliance data backs this up directly: GSTR-3B filing compliance, a rough proxy for how functional the return-filing machinery actually is, sat at 89.2% in FY2018-19 and has held above 98.9% since FY2023-24 — a genuine turnaround from the administrative gaps the Comptroller and Auditor General’s own 2019 compliance audit had flagged in the system’s early years (CAG, 2019).

Eight crore taxpayers, and 0.5% of them carry the load

If there’s one table in the GST dataset that deserves to be printed and pinned to a wall in North Block, it’s the taxpayer-constitution breakdown. As of June 2025, India had 1.53 crore active GST registrations. Proprietorships — the corner shop, the single-owner trading firm, the neighbourhood parlour — make up 80.14% of that base. Public Limited Companies make up 0.50%.

Proprietorships contribute 13.27% of GST revenue. Public Limited Companies contribute 33.83%.

Run the leverage ratio — revenue share divided by taxpayer share — and a Public Limited Company contributes 67.7 times its population share to the tax base. A Proprietorship contributes 0.2 times its share. Public Sector Undertakings, a mere 3,475 entities, punch in at an extraordinary 469.5× leverage, contributing 9.39% of all GST revenue from 0.02% of registrants.

This is not a scandal — large, formal, capital-intensive businesses have always paid disproportionate tax everywhere in the world, and the IMF’s own periodic assessments of India’s GST have argued its multi-rate structure does more to protect low-income consumers than a single-rate alternative would (IMF, 2018). But it does put a number on something that’s often asserted impressionistically in Indian tax-policy debates: GST’s revenue base is extremely concentrated at the top of the formal economy, even though its compliance burden — filing, invoicing, e-way bills — falls on all 1.53 crore registrants roughly equally. That asymmetry is arguably the single biggest unresolved design tension in GST as it enters its ninth year.

The e-way bill is now a better GDP proxy than GDP

Buried in section 8 of the underlying dataset is a number that deserves far more attention than it gets: e-way bill generation — the digital permission slip required to move goods worth over ₹50,000 across state lines or within them — has grown 3.24× since FY2018-19, from 43.35 crore bills a year to 140.71 crore in FY2025-26 (11 months). The assessable value of goods covered has crossed ₹32.7 lakh crore.

Because e-way bills are generated in real time, at the point goods actually move, they arguably tell you what’s happening in the goods economy faster and more honestly than any survey-based indicator India publishes. When intra-state EWB growth outpaces inter-state growth, as it has in recent years, it’s a signal of shorter, more localised supply chains — plausibly linked to warehousing decentralisation under GST’s own “One Nation, One Tax” logic finally removing the incentive to route goods through fewer, larger interstate hubs.

Maharashtra’s lead is not shrinking — but the chase is getting interesting

Maharashtra alone contributes north of 21% of all state-level domestic GST — more than the bottom fifteen states combined. But the fastest-growing large states in FY2024-25 were not the usual suspects: Haryana grew 16.0% year-on-year and Delhi grew 15.9%, both comfortably outpacing Maharashtra’s own growth and reflecting the expansion of services-sector activity — IT, finance, logistics — around the National Capital Region rather than manufacturing.

Eight years in, what the ledger actually says

None of this requires anyone to have an opinion about GST as a political project. The eight-year dataset simply says: revenue has nearly tripled; compliance infrastructure that was genuinely broken in 2018-19 now works; refunds move faster; the formal, large-enterprise economy carries a wildly disproportionate share of the tax base; and a real-time logistics indicator now exists inside the tax system that didn’t exist before it. Whatever else GST turns out to have been — messy launch, contested rate structure, endless Council meetings — it has, almost as a side effect, built India one of its best pieces of high-frequency economic infrastructure. The next test, as the Compensation Cess fully sunsets through 2026, is whether the system’s next chapter is written with the same discipline as its recovery from a pandemic-year collapse.

Literature Review

GST’s academic literature divides fairly cleanly into two eras. The pre-2017 era was dominated by revenue-neutral-rate modelling — the Chief Economic Advisor’s 2015 committee report on the RNR remains the reference point, and it’s worth noting how far actual collections have outrun its original projections. International public-finance scholars writing before implementation, most influentially Michael Keen’s work on VAT design for the IMF, framed GST’s central design tension well in advance: a single-rate VAT is administratively simpler and less distortionary, but India’s multi-slab structure was always going to trade some of that simplicity for political feasibility and revenue protection on sensitive goods. The dataset examined here is, in a sense, eight years of evidence on how that trade-off actually played out.

The post-implementation literature has grown into a genuinely large body of work — a 2023 bibliometric review counted over 400 Scopus-indexed publications on GST between 2017 and 2022 alone — but it skews heavily toward compliance-cost and revenue-collection studies, with comparatively little research on the taxpayer-concentration question this piece foregrounds. The 2019 Comptroller and Auditor General compliance audit remains one of the few official assessments to document GSTN’s early-years administrative gaps in granular detail, gaps that the filing-compliance improvement documented above (89.2% to 98.9%+) suggests have been substantially closed. On the specific question of who actually bears India’s indirect tax burden, the IMF’s periodic Selected Issues papers on India have consistently argued that GST’s multi-rate structure, imperfect as it is, does more to protect low-income consumers than a single-rate alternative would — a finding that sits somewhat in tension with the stark taxpayer-leverage numbers in this analysis, and a genuine open question for future incidence research using GSTN microdata.

References

• Government of India, Ministry of Finance. (2015). Report on the Revenue Neutral Rate and Structure of Rates for the Goods and Services Tax. Chief Economic Advisor’s Committee (Chairman: Arvind Subramanian).
• Comptroller and Auditor General of India. (2019). Report No. 11 of 2019: Compliance Audit — Department of Revenue: Goods and Services Tax.
• Keen, M. (2013). The Anatomy of the VAT. National Tax Journal, 66(2), 423–446.
• Keen, M. (2009). What Do (and Don’t) We Know About the Value Added Tax? Journal of Economic Literature, 47(1), 157–170.
• International Monetary Fund. (2018). India: Selected Issues. IMF Country Report No. 18/255.
• GSTN / gstIndia R package documentation and release notes, v2.1.0.
• Ministry of Finance, Government of India — monthly GST collection press releases, April 2018–February 2026.

Data: gstIndia R package (v2.1.0), aggregating Ministry of Finance and GSTN releases, through February 2026. Analysis and R code (data.table) by Pawan. A companion interactive dashboard is available here.

Rural India did not get the memo urban India got

The national poverty numbers hide a split that matters more than the national average itself. Urban poverty nearly halved over the seven years, from 17.7% to 10.2%. Rural poverty barely moved: 20.6% to 19.7%, a decline so small it is close to statistically indistinguishable from no change at all. Whatever growth engine powered India’s income gains between 2004-05 and 2011-12, it was disproportionately an urban engine, and rural India — where roughly two-thirds of the surveyed households live — largely watched from outside it.

The social-group gap that a national average erases completely

If urban-rural is the first fault line the data exposes, social group is the second, and it’s sharper. By 2011-12, Adivasi (tribal) households had a poverty rate of 34.8% — more than three times the Forward Caste rate of 7.1%, and still the highest of any group in the survey, exactly as it had been seven years earlier. Dalit households (21.6%) and Muslim households (18.3%) both sat roughly double the national average.

Every group’s poverty rate fell over the seven years — this is not a story of some groups gaining while others lost ground in absolute terms. But the relative gap barely narrowed, echoing Vanneman and Dubey’s influential distinction between “vertical” inequality (income-quintile gaps) and “horizontal” inequality (caste and religious-group gaps), drawn from these same two IHDS waves: India’s growth narrowed the former only modestly while leaving much of the latter intact (Vanneman & Dubey, 2013). Adivasi households were 5.0 times as likely to be poor as Christian/Sikh/Jain households in 2004-05, and 9.4 times as likely in 2011-12 — the ratio actually widened, even as every group’s absolute poverty rate declined. Growth, in other words, was a rising tide, but it did not lift all boats by the same amount, and the boats that started lowest gained the least in relative terms.

Migration is what poor households do, not what anyone chooses casually

IHDS-II asked, specifically, about seasonal labour migration — household members who left temporarily, typically for one to six months, to work elsewhere. The pattern that emerges is stark and consistent across every way the data can be cut. Rural households were more than four times as likely to have a recent out-migrant as urban households (8.8% vs. 2.1%). Migration prevalence fell steadily and steeply as income rose: 12.4% of the poorest fifth of households had a recent migrant, against just 2.0% of the richest fifth.

The same disadvantage pattern that shows up in poverty repeats almost exactly in migration: Adivasi (10.9%) and Dalit (8.4%) households were roughly four times as likely to send a migrant as Forward Caste (2.7%) or Brahmin (3.7%) households. And migrant-sending households were significantly more likely to be poor overall — 26.2% versus 15.7% for households without a migrant (chi-square = 201.8, p < 0.001), a statistically decisive gap.

None of this proves which way the causal arrow points, and the report is careful to say so: poverty could push a household to send a migrant, the disruption of a household member leaving could itself contribute to poverty, or both could stem from a shared underlying disadvantage. A 2022 study using the same two IHDS waves with propensity score matching and instrumental variable regression found that permanent migration’s effect on household income follows an approximate U-shaped pattern, while seasonal migration’s effect declines more uniformly (Population Research and Policy Review, 2022) — a genuine causal refinement that the cross-sectional prevalence statistics above cannot themselves provide. What the cross-sectional data can establish is the association, cleanly: seasonal migration in India is overwhelmingly a strategy of the rural poor, not a lifestyle choice of the mobile middle class, and it is disproportionately male — roughly 89% of recent migrants recorded in the data were men, moving more often to another state (56%) than staying within their own (41%), most commonly through contractor-arranged placement (53%) rather than a self-arranged job, a gendered pattern first documented in detail by Desai and Banerji’s earlier work on male migration and left-behind wives using IHDS-I alone (Desai & Banerji, 2008).

Whose children benefit when a father migrates — a sharper question than it first appears

A useful complication comes from outside the IHDS report itself. Kriti Vikram’s peer-reviewed 2021 study, using the same two IHDS waves with more rigorous causal methods (propensity score matching, which approximates a controlled comparison by matching migrant and non-migrant households on observed characteristics), asks a narrower and different question: among households where a father migrates long-term — over six months, not the seasonal one-to-six-month migration IHDS-II measures broadly — do sons and daughters benefit equally in school?

They do not. Vikram finds that when fathers migrate for extended work, sons show more consistent gains in reading and arithmetic achievement than daughters do — a finding that adds real nuance to the seasonal-migration story above. It also, at first glance, seems to sit oddly next to IHDS-II’s broader finding that migration is concentrated among poorer, Adivasi and Dalit households: Vikram’s long-term-migrant fathers skew, if anything, slightly more likely to be Brahmin and less likely to be Scheduled Tribe. The two findings aren’t actually in conflict — they describe two different populations of migrants (short seasonal moves versus long-term paternal absence), which is worth remembering any time “migration” gets treated as a single, uniform category in policy discussions.

Illness follows poverty as closely as migration does

The health module, available only for IHDS-II, adds a third strand to the same pattern. Chronic illness climbs sharply with age, as expected — but it is also significantly more common among poor households than non-poor ones (chi-square = 925.0, p < 0.001), a relationship that plausibly runs in both directions: illness reduces earning capacity and pushes a household toward poverty, while poverty itself limits access to preventive care and raises exposure to health risks in the first place. Disability, measured separately as functional difficulty with walking, hearing, or vision, rises just as sharply with age (from under 2% under 35 to 29.6% at 60-plus) but shows almost no income gradient — a genuinely different pattern from chronic illness, and a reminder that “poor health” is not one thing in this data; disability and chronic illness behave differently, and conflating them would obscure exactly the distinction that matters for targeting policy.

What a decade-old survey still has to say to 2026

IHDS-II is now itself over a decade old, and the report is candid about what that means: nominal income comparisons across the two waves overstate real gains, because they aren’t adjusted for inflation, and none of the associations here — migration and poverty, illness and poverty — can be read as proof of causation from a cross-sectional snapshot alone. But the structural pattern the two waves jointly establish — genuine, broad income growth; real but incomplete poverty reduction; social-group and rural-urban gaps that improved in absolute terms but not in relative ones — is exactly the pattern most subsequent Indian survey data, from PLFS to NSS consumption rounds, has continued to describe in the years since. The two IHDS rounds may be old data. The shape of the inequality they documented has proven considerably more durable than the survey itself.

Literature Review

IHDS has generated one of the largest secondary-research literatures of any Indian household survey, and the inequality strand of that literature anticipates almost exactly the tension this piece foregrounds. Vanneman and Dubey’s widely cited chapter on horizontal and vertical inequalities in India, built on the same two IHDS waves used here, distinguishes between “vertical” inequality (the income-quintile gaps) and “horizontal” inequality (the caste and religious-group gaps) and finds that India’s growth story of the 2000s narrowed the former only modestly while leaving much of the latter intact — the same asymmetry this analysis documents through poverty ratios rather than Gini decomposition. Desai and Banerji’s earlier work on male migration and “left-behind” wives, using IHDS-I alone, established the gendered household-disruption dimension of migration that this piece’s aggregate migration statistics necessarily flatten out.

On migration specifically, more recent work has moved past simple prevalence statistics toward causal identification: a 2022 study in Population Research and Policy Review, using propensity score matching and instrumental variable regression on the same two IHDS waves, found that permanent migration’s effect on household income follows an approximate U-shaped pattern while seasonal migration’s effect is more uniformly declining — a nuance the cross-sectional migration-poverty association in this piece cannot itself establish, and a useful caution against reading any single migration statistic as evidence of a uniform “migration effect.” Taken together, the IHDS secondary literature consistently supports this piece’s central claim: that broad-based income growth and persistent horizontal inequality are not contradictory findings requiring reconciliation, but the two halves of the same well-documented story.

References

• Vanneman, R., & Dubey, A. (2013). Horizontal and Vertical Inequalities in India. In J. Gornick & M. Jäntti (Eds.), Income Inequality: Economic Disparities and the Middle Class in Affluent Countries. Stanford University Press.
• Desai, S., & Banerji, M. (2008). Negotiated Identities: Male Migration and Left-Behind Wives in India. Journal of Population Research, 25(3), 337–355.
• Desai, S., Dubey, A., Joshi, B. L., Sen, M., Shariff, A., & Vanneman, R. (2012). India Human Development Survey: Technical Paper No. 1. NCAER & University of Maryland.
• Internal Migration and Rural Inequalities in India. (2022). Population Research and Policy Review (Springer).
• Vikram, K. (2021). Fathers’ Migration and Children’s Educational Outcomes: Evidence from India. International Migration Review.
• Desai, S., Vanneman, R., & National Council of Applied Economic Research. India Human Development Survey (IHDS), 2005 [ICPSR 22626] and IHDS-II, 2011-12 [ICPSR 36151]. Inter-university Consortium for Political and Social Research.

Data: India Human Development Survey I (ICPSR 22626, 2004-05) and II (ICPSR 36151, 2011-12), NCAER & University of Maryland. External validation: Vikram (2021), International Migration Review. Analysis by Pawan. A companion interactive dashboard is available here.

The Fertilizer Import Portfolio: NPK and Its Dependencies

Chemical fertilizers supply three primary macronutrients — nitrogen (N), phosphorus (P), and potassium (K) — each of which India sources through a distinct combination of domestic production and imports.

The Price Shock of 2021–22: A Fiscal Emergency

The fertilizer price crisis of 2021–22 was the most severe in India’s post-Independence agricultural history. The international price of urea, which had been USD 250–300/MT for much of 2019–20, crossed USD 900/MT at its peak in late 2021. DAP prices, which were USD 300–350/MT in 2020, reached USD 900–1,000/MT. MOP prices tripled from USD 200/MT to over USD 600/MT.

In normal years, India’s fertilizer subsidy bill runs to approximately ₹70,000–80,000 crore. In 2021–22, it crossed ₹2.5 lakh crore — more than three times the normal level — as the government absorbed the price differential between international prices and the controlled retail prices paid by farmers. This extraordinary expenditure crowded out other budgetary priorities and strained the fiscal deficit targets.

India’s fertilizer pricing architecture creates this fiscal risk structurally. Retail fertilizer prices — particularly for urea — are fixed by the government and have not been revised upward meaningfully in over a decade. The “Maximum Retail Price” of urea is approximately ₹5.36 per kg, while its economic cost (at 2021–22 international prices) was approximately ₹30–35 per kg. The entire gap is borne by the government through the fertilizer subsidy scheme. This system, while protecting farmers from price volatility in the short run, creates perverse incentives — excessive urea use (India over-applies urea relative to phosphorus and potassium, distorting the N:P:K ratio), soil degradation, and groundwater contamination — while also creating a massive, unhedged fiscal exposure to global commodity markets.

Structural Reform: What India Needs to Do

Addressing India’s fertilizer vulnerability requires action on multiple fronts.

Expanding domestic urea capacity is the first priority. The completed revival of the five sick urea plants has been helpful, but the pipeline needs to continue. New urea plants, particularly using coal-to-urea technology in coal-rich states like Jharkhand and Odisha, could reduce import dependence further — though this comes at the cost of higher carbon emissions and may conflict with India’s climate commitments.

Developing strategic fertilizer reserves is urgently needed. China maintains a 6–9 month fertilizer reserve; India holds barely a month’s worth of buffer stock. Creating a 3-month reserve of all three key fertilizers (urea, DAP, MOP) would provide a meaningful buffer against supply shocks and price spikes, reducing the pressure to accept unfavourable terms in crisis procurement.

Diversifying potash sourcing requires diplomatic investment. India needs to engage more deeply with Canadian producers, explore the viability of Jordanian potash, and develop relationships with emerging potash producers in Ethiopia and Congo. The government’s agricultural attaché offices at key embassies should be working proactively on this.

Reforming the urea subsidy architecture is the most politically difficult but most important structural change. Transitioning from a fixed-price subsidy to a direct benefit transfer (DBT) system — where farmers receive cash support and buy fertilizers at market prices — would eliminate the perverse incentives created by the current system, reduce over-application of urea, and decouple the government’s fiscal exposure from international price volatility. The Neem Coating of Urea initiative (introduced in 2015 to slow urea dissolution and reduce diversion) was a modest first step; the broader pricing reform remains unfinished business.

Investing in fertilizer-efficient crop varieties and soil health can reduce the absolute quantity of fertilizer needed. India’s fertilizer use efficiency — the kilograms of additional crop output per kilogram of nutrient applied — is well below global best practice. Better soil testing, precision application technologies, and the promotion of organic/bio-fertilizer supplementation can improve efficiency without reducing yields, gradually reducing the volume of imports required.

The Geopolitical Dimension: Fertilizers as a National Security Issue

The fertilizer crisis of 2021–22 should be viewed not merely as a commodity market event but as a national security stress test. Food production depends on fertilizers; if fertilizer supplies are cut off or priced out of reach for an extended period, Indian crop yields would fall, food prices would surge, and rural livelihoods would be devastated.

India’s geopolitical situation — with complex relationships with China (historically its largest urea supplier), Russia and Belarus (potash), and the Middle East/North Africa (phosphates and DAP) — means that fertilizer procurement is inescapably entangled with foreign policy. The government has shown pragmatism in this regard: India did not join the Western sanctions against Belarus or Russia and continued to negotiate fertilizer supplies through government-to-government channels. This realpolitik approach has helped manage the immediate supply crisis but does not address the structural dependency.

A deeper strategic investment in domestic production capacity, alternative supply relationships, and demand-side efficiency improvements is the only durable solution.

Conclusion

India’s fertilizer import dependency is a complex web of geological constraints (zero potash and limited phosphate reserves), policy distortions (urea subsidies that encourage over-use), and geopolitical exposures (concentration in a few supplier countries). The 2021–22 price crisis was the most expensive signal yet that this architecture is unsustainable. Reforming it requires political courage — particularly around urea pricing — as well as sustained investment in domestic manufacturing, strategic reserves, and soil health. The alternative — waiting for the next price shock to impose even greater fiscal and food security costs — is not an option that India’s agriculture, economy, or 700 million farming-dependent citizens can afford.

Data sources: Department of Fertilisers, Ministry of Chemicals; Ministry of Commerce (import statistics); World Bank Fertilizer Price database; IFA (International Fertilizer Association). Analysis by Pawan.

Understanding the PLFS Methodology

Before examining the results, it is important to clarify the methodological framework. The PLFS collects data on each individual’s activity during a reference year, classifying them into one of five broad activity statuses: (11–12) self-employed (own account workers and employers), (21) helper in household enterprise (unpaid family workers), (31) regular salaried/wage employees, (41–51) casual wage workers in public works and other work. Those who are seeking or available for work but not working are classified as unemployed (status code 81).

The Labour Force Participation Rate (LFPR) is defined as the proportion of the working-age population (15+) that is either working or actively seeking employment. The Worker Force Participation Rate (WFPR) counts only those actually employed, excluding job-seekers. The Unemployment Rate (UR) is the proportion of the labour force that is unemployed — i.e., seeking but not finding work.

The UPSS framework is somewhat generous in its classification of workers: a person who worked even one day in a subsidiary capacity during the reference year may be counted as a worker. This means UPSS-based labour market indicators tend to show higher WFPR and lower unemployment rates than the more stringent Current Weekly Status (CWS) or Current Daily Status (CDS) measures. The analysis here uses the UPSS framework.

Labour Force Participation: A Low Base, Especially for Women

India’s LFPR, measured on the UPSS basis, stood at approximately 49–51% across the four survey years (2018–19 to 2021–22). This figure — meaning roughly half of India’s working-age population is either working or looking for work — is lower than the global average and significantly below the rates observed in East Asian economies. Understanding why such a large proportion of India’s working-age population is outside the labour force is one of the central challenges for India’s economic policymakers.

The gender dimension of this picture is stark. India’s female LFPR — one of the lowest in the world among large developing economies — has historically been in the 20–25% range (UPSS basis). The PLFS data from 2018–19 showed female LFPR at approximately 24–26%, with a significant rural-urban gap (rural female LFPR is typically 10–15 percentage points higher than urban female LFPR, because rural women are more likely to be classified as workers through agricultural and household enterprise activities). Male LFPR stands considerably higher at approximately 75–77%, though this too is below what might be expected for an economy at India’s development stage.

The low female LFPR in India reflects a complex interplay of factors: social norms that restrict women’s mobility and constrain their participation in market work; the large share of women’s time absorbed in unpaid domestic and care work (which the PLFS counts as non-work); the relatively low education and skill levels of older cohorts of women; and the lack of safe, appropriately compensated employment opportunities that would attract women into the formal labour market. The paradox is that as education levels among young Indian women have risen dramatically over the past two decades, their measured labour force participation has not risen commensurately — the “education premium” is not yet translating into employment participation in India the way it does in most other economies.

The COVID-19 Impact: 2020–21 as a Stress Test

The PLFS 2020–21 round was conducted during the year of India’s most severe COVID-19 disruption, including the nationwide lockdown of March–May 2020 and the subsequent phased reopening. This round provides a unique window into the labour market impact of the pandemic.

The headline indicators from 2020–21 reveal a complex picture. The aggregate LFPR and WFPR showed significant movements, but not uniformly in the direction one might expect from the severity of the economic disruption. In fact, female LFPR in rural areas increased during 2020–21 — a pattern observed in several COVID-affected economies, explained by the “added worker effect”: when primary earners (typically male wage workers) lose employment, women enter the labour force in distress-driven work, often in agriculture or casual labour.

Urban male WFPR, by contrast, fell — reflecting the severe disruption to urban services, construction, and manufacturing employment during the lockdown period. The formal regular wage employment category — which is most characteristic of urban workers — contracted sharply, while self-employment and casual wage work in rural areas expanded, as migrant workers returned to their home villages and sought agricultural and MGNREGS work.

This “back to agriculture” effect during COVID is consistent with the longstanding role of India’s agricultural sector as an employment absorber of last resort. When the non-farm economy contracts, displaced workers flow back into farming households — not because agricultural productivity or incomes improve, but because the implicit flexibility of family farm labour provides a safety net that India’s formal social protection system cannot fully substitute.

Unemployment: Official Rates and the Hidden Problem

India’s officially measured unemployment rate on the UPSS basis has historically been reported as 2–6% — a figure that frequently puzzles observers who see evidence of widespread joblessness, educated youth struggling to find work, and under-employment in the informal sector. The key to understanding this apparent paradox lies in the design of the UPSS methodology itself.

Under the UPSS framework, an individual who works even occasionally in a subsidiary capacity is counted as “employed.” The vast majority of India’s labour force is self-employed — in family farms, petty trade, household enterprises — and virtually none of them are “unemployed” in the UPSS sense, because they can always perform some activity related to the family enterprise. The UPSS-measured unemployment rate, therefore, is best interpreted as capturing open unemployment among educated youth in urban areas — those who are actively seeking formal wage employment and refusing to accept casual or self-employment in the meantime.

This narrow unemployment concept misses the much larger problem of under-employment and disguised unemployment — the situation where workers are nominally employed but at wages and productivity levels far below their potential. The casual agricultural worker who works 120 days per year in the fields is “employed” in UPSS terms; the reality of their labour market situation — low income, high work insecurity, seasonal idleness — is invisible in the headline statistic.

The PLFS data for 2018–19 through 2021–22 shows unemployment rates (UPSS) in the range of 2–4%, with higher rates for educated youth (those who have completed secondary or higher education) and for urban workers. Female unemployment rates are lower than male rates in aggregate terms, partly because women are more likely to accept whatever work is available rather than holding out for formal employment.

Sectoral Composition: The Services Revolution and Agricultural Persistence

The sectoral distribution of India’s workforce reveals the persistent importance of agriculture as a source of employment alongside the rapid growth of the services sector. Agriculture — including crop cultivation, animal husbandry, fishing, and forestry — typically employs 45–50% of India’s total workforce on the UPSS basis, even though it contributes only 15–18% of GDP. This gap between agricultural employment share and agricultural GDP share is the classic definition of structural under-employment — too many workers chasing too little value addition.

The manufacturing sector’s share of employment has remained stubbornly low at approximately 11–13% of the workforce — far below what one would expect at India’s income level. India has not achieved the kind of labour-intensive manufacturing expansion that drove employment transformation in China, South Korea, Taiwan, and now Bangladesh and Vietnam. The “missing middle” of India’s industrial structure — the absence of a large, export-oriented labour-intensive manufacturing sector — is one of the most serious structural constraints on India’s ability to absorb its 10–12 million annual labour market entrants into productive employment.

The construction sector employs approximately 12–14% of the workforce — making it India’s second-largest source of non-farm employment. Construction employment is highly sensitive to infrastructure investment cycles and exhibits significant seasonal variation. The COVID lockdown devastated construction employment, and the displacement of construction workers was central to the humanitarian crisis of reverse migration in 2020.

Services — a broad category encompassing everything from domestic work and petty retail to IT services and banking — employ approximately 28–32% of the workforce. Within this category, the distribution is deeply bimodal: a small upper segment of high-skill, high-wage services (IT, finance, professional services) and a very large lower segment of informal, low-wage services (street vending, domestic work, transportation). The celebrated growth of India’s IT and business process services sector is real and important, but it employs only 4–5 million workers — a tiny fraction of the total labour force.

The Rural-Urban Employment Gap

One of the most important dimensions of India’s labour market is the rural-urban divide. Rural workers are more likely to be self-employed in agriculture, more likely to be casual wage workers, less likely to have formal employment contracts, and more likely to be paid in kind (in grain) rather than cash. Urban workers, while also substantially informal, have somewhat higher access to regular wage employment, better wage levels, and greater connectivity to the formal economy.

The PLFS data shows that the urban regular wage employment rate — perhaps the best single indicator of “good jobs” in India — is significantly higher in urban areas (35–40% of urban workers) than in rural areas (10–12% of rural workers). This gap reflects the concentration of large enterprises, public sector employment, and services industries in urban areas.

The rural-urban wage differential is substantial: urban workers earn on average 2–3 times the daily wage of rural workers, after controlling for occupation. This wage gap drives the massive rural-to-urban migration that has characterised India’s urbanisation process — but the pace of urban job creation in formal sectors has not kept up with migration flows, contributing to the expansion of urban informal employment and slum growth.

Policy Implications: What the Data Demands

The PLFS data, taken together across the four survey years, points to several priority areas for labour market policy.

Women’s employment is the largest underutilised resource: Increasing female LFPR by even 10 percentage points would add approximately 60–70 million workers to the productive economy — a demographic dividend that India has not yet captured. This requires investments in women’s safety in public spaces and workplaces, expansion of affordable childcare, and the design of flexible part-time work arrangements that accommodate the reality of women’s domestic responsibilities.

The quality of employment matters as much as quantity: India needs not just more jobs but better jobs — regular wage employment with social security coverage, predictable incomes, and career progression potential. The formalisation agenda — extending EPFO coverage, implementing the labour codes, and providing incentives for firms to hire on formal contracts — is central to this objective.

Skill development must be demand-driven: India’s skill development programmes have historically produced certificates without producing employable workers. The disconnect between training content and employer demand — in construction, manufacturing, healthcare, and digital services — must be closed through closer industry-government partnerships and outcome-linked funding for training providers.

Conclusion

The PLFS has been a transformative addition to India’s statistical infrastructure, providing annual insights into a labour market that is simultaneously enormous, diverse, and dynamic. The data from 2018–19 through 2021–22 tells a story of structural challenges — low female participation, agricultural over-reliance, limited formal employment — alongside resilience, as the labour market absorbed the COVID shock better than many feared. The agenda for improving India’s labour market outcomes is clear: more and better jobs, greater female participation, formalisation, and skill development. Executing this agenda at the scale India’s demographics demand is the defining economic challenge of the coming decade.

Data sources: Periodic Labour Force Survey (PLFS) microdata, Ministry of Statistics and Programme Implementation (MoSPI), Government of India. Analysis by Pawan.

The Anatomy of India’s Food Price System

India’s food pricing operates on two interlinked tracks. The wholesale track reflects prices in regulated agricultural produce market committee (APMC) mandis and wholesale distribution hubs — the price at which traders buy from farmers or import suppliers. The retail track reflects what consumers actually pay in local markets, kirana stores, and modern retail outlets. The gap between these two — the retail-wholesale spread — represents the cumulative cost of storage, transportation, processing, taxation, and the margins of the multiple intermediaries who sit between the farmer and the consumer.

In well-functioning markets, this spread should be stable and relatively narrow, reflecting genuine costs of distribution. In India, however, price spread data reveals a more troubled picture: spreads are often wide, highly variable across states and seasons, and frequently resistant to downward adjustment even when wholesale prices fall. This asymmetric price transmission — wholesale price increases rapidly pass through to retail prices, but wholesale price decreases do not — is a recurrent feature of Indian food markets that imposes a structural cost on consumers.

The government’s price monitoring system collects daily retail prices for 22 essential commodities across 200+ reporting centres, and wholesale prices from major mandis across the country. This granular data reveals patterns of price inflation that aggregate indices like the Consumer Price Index (CPI) — Food often obscure.

Cereals: The Managed Core

Among the four commodity groups examined here, cereals — primarily rice and wheat — represent the most heavily managed segment of India’s food system. The government sets Minimum Support Prices (MSP) for wheat and paddy annually, procures tens of millions of tonnes through FCI and state agencies, and distributes subsidised cereals to approximately 800 million beneficiaries under the National Food Security Act (NFSA) and subsequently the Pradhan Mantri Garib Kalyan Anna Yojana (PMGKAY).

This system largely insulates the retail price of wheat and rice from international price shocks. When global wheat prices surged in 2022 following Russia’s invasion of Ukraine, India’s domestic wheat retail prices rose modestly — in part because the government released stocks from its buffer reserves and banned wheat exports. The rice retail price trajectory has been similarly dampened by government intervention.

However, this managed stability comes at a cost. First, it requires enormous public expenditure — the PMGKAY alone costs the government approximately ₹3.5–4 lakh crore annually in food subsidies. Second, the suppression of market prices discourages private sector investment in cereal post-harvest infrastructure, cold storage, and processing. Third, the gap between PDS issue prices and market prices creates significant leakage, diversion, and “ghost beneficiary” problems that reduce the scheme’s actual welfare impact.

The retail-wholesale spread for rice in India is typically ₹5–8 per kg — roughly 15–20% of the retail price. For wheat, the spread tends to be narrower, at ₹3–5 per kg. These spreads are highest in remote states with poor road connectivity and weakest in states with well-developed APMC infrastructure and private trade networks.

Pulses: The Volatile Protein Source

If cereals represent the managed, stable core of India’s food price system, pulses represent its volatile, poorly managed periphery. Dal — the protein staple of hundreds of millions of Indian vegetarian households — has experienced some of the most severe price spikes in India’s recent food history. Arhar (tur) dal crossed ₹200 per kg in Delhi in 2023–24, a level that would have been unimaginable a decade earlier. Moong, urad, and masoor have each touched historic highs in various recent years.

The structural reasons for pulse price volatility are well-established. First, India’s domestic pulse production is highly weather-sensitive — pulses are grown predominantly in rainfed areas on residual moisture, making them particularly vulnerable to erratic monsoons. Second, pulse acreage is in constant competition with higher-value crops, and the absence of robust government procurement (until relatively recently) gave farmers weak incentives to maintain pulse cultivation areas. Third, the import regime for pulses has been erratic — the government alternates between imposing import duties (to protect domestic farmers) and suspending them (to control consumer prices), creating uncertainty for both producers and traders.

The retail-wholesale spread for pulses is notably large — typically ₹15–25 per kg, or 20–30% of retail prices. Unlike cereals, where a largely standardised product moves through established supply chains, pulses involve significant post-harvest processing (cleaning, milling, packaging), greater spatial price variation, and more fragmented retail distribution. The dal mill sector — which is largely unorganised and under-capitalised — is a significant contributor to the wide spread.

The government’s response to pulse price spikes has typically been a combination of import duty waivers, release of buffer stock, and export bans. While these measures can dampen acute price spikes, they do not address the structural supply constraints. A more durable solution would involve sustained investment in pulse research (drought-tolerant varieties, better yields), dedicated procurement and price support for pulse farmers, and investment in the dal milling and cold chain infrastructure.

Edible Oils: Imported Inflation

No commodity group better illustrates the transmission of global price shocks to Indian household budgets than edible oils. As detailed elsewhere in this analysis, India imports 14–16 million tonnes of edible oils annually — accounting for over 50% of its consumption requirement. This structural import dependence means that global edible oil price movements — driven by palm oil supply in Southeast Asia, soybean harvests in South America, or sunflower oil disruptions in the Black Sea region — translate almost directly into Indian retail price changes.

The transmission mechanism operates rapidly and with high fidelity in the upward direction. When international palm oil prices rose sharply in 2021–22, Indian retail palm olein prices in Delhi moved from approximately ₹90/litre to ₹165/litre within six months. The retail-wholesale spread for edible oils is typically ₹5–10 per litre — smaller in proportional terms than pulses, but significant in absolute terms given the large volumes consumed.

Downward price transmission, however, tends to be slower and incomplete. When international edible oil prices fell sharply in the second half of 2022 — palm oil fell from over USD 1,600/MT to below USD 900/MT within months — Indian retail prices declined more gradually, with retailers and distributors adjusting inventory positions before passing the reduction to consumers. This asymmetry systematically favours the supply chain intermediaries at the expense of households.

The government’s key policy tools for edible oil price management are import duty adjustments (duties on crude edible oils have been reduced to near-zero to keep domestic prices in check) and occasional direct market interventions through NAFED. But given the structural nature of India’s import dependence, these tools can only smooth price fluctuations at the margin — they cannot insulate Indian consumers from sustained global price increases.

Vegetables: The Seasonal Volatility Problem

Vegetables represent perhaps the most spectacularly volatile segment of India’s retail food price landscape. Tomato prices in Delhi famously crossed ₹200 per kg in July–August 2023 — a price so extraordinary that it entered political discourse and became the subject of WhatsApp memes across the country. This was not an isolated event; onion prices have triggered political crises in India on multiple occasions since Independence, and the dramatic seasonal swings in the prices of tomato, potato, and leafy vegetables are an annually recurring phenomenon.

The structure of vegetable price volatility is fundamentally different from that of cereals or pulses. Vegetables are highly perishable, cannot be stored for more than a few days without refrigeration, and are grown in concentrated clusters — Nashik for onions, Pune and Karnataka for tomatoes, Agra for potatoes — that create geographic price transmission problems when these clusters experience adverse weather or pest attacks.

India’s cold chain infrastructure for vegetables remains woefully inadequate relative to production volumes. Approximately 30–40% of the vegetable harvest is estimated to be lost post-harvest due to inadequate storage, poor roads, and lack of refrigerated transport. This wastage drives up the effective cost of the remaining produce, contributing to high retail prices even in years of good harvest. It also means that production surpluses — which should logically reduce prices — often go to waste rather than being distributed to deficit regions.

The retail-wholesale spread for vegetables varies enormously by commodity and season but typically ranges from ₹5–20 per kg — sometimes representing 50–70% of the retail price in markets with fragmented retail and poor road connectivity.

Policy Conclusions: What Works and What Doesn’t

Two decades of food price management experience in India has established a reasonably clear hierarchy of intervention effectiveness.

What works: Strategic public procurement and distribution for storable commodities like wheat, rice, and pulses can effectively dampen price spikes for these specific items. Direct benefit transfers (DBT) to farmers — such as PM-KISAN — provide income support without distorting markets. Investment in cold chain infrastructure, rural roads, and post-harvest management reduces food waste and improves spatial price integration.

What doesn’t work well: Short-term export bans, while effective in damping domestic prices immediately, damage India’s reputation as a reliable trading partner and depress farm-gate prices, creating a disincentive for future production. Import duty waivers are blunt instruments that do not distinguish between emergency needs and normal-year import requirements. Price controls in vegetable markets, when attempted, typically cause supply withdrawal and worsen shortages.

The most durable solution to India’s food inflation problem is structural: higher agricultural productivity, reduced post-harvest losses, better market integration across states, and a more effective safety net for low-income households. These are long-term investments whose payoffs are diffuse and delayed — which is precisely why they tend to be under-prioritised relative to headline-grabbing but ultimately ineffective short-term interventions.

Conclusion

India’s food price system is a reflection of the country’s extraordinary agricultural complexity: vast geographic diversity, 140 million farming households, a fragmented supply chain with multiple intermediaries, and a government that simultaneously tries to protect farmer incomes (through MSP and procurement) and consumer welfare (through subsidies and price controls). Navigating these competing objectives without distorting market signals is inherently difficult. Understanding the granular dynamics of retail and wholesale price movements — commodity by commodity, state by state, season by season — is an essential foundation for the better-targeted interventions that India’s food economy requires.

Data sources: Price Monitoring Division, Department of Consumer Affairs; National Sample Survey (NSS); FAOSTAT. Analysis by Pawan.

The Macro Picture: A Trade Profile Shaped by Energy and Commodities

India’s import basket is dominated by a small number of energy and commodity categories. Petroleum crude and products consistently account for 25–30% of total import value. Precious metals and gems (primarily gold, imported for jewellery fabrication and investment) account for 8–10%. Electronic goods — smartphones, components, consumer durables — add another 10–12%. Fertilizers, edible oils, and machinery round out the top import categories.

The energy import bill is the single largest driver of India’s trade deficit and its most structurally stubborn component. India’s domestic crude oil production has been declining for years — the major oil fields at Bombay High are in natural decline — while consumption has been rising rapidly with motorisation and economic growth. At current trajectory, India’s crude oil import dependence (already at 85% of consumption) will continue to rise. Every USD 10 per barrel increase in the crude oil price adds approximately USD 14–15 billion to India’s annual import bill — a sensitivity that makes the trade balance highly exposed to geopolitical shocks in the Middle East and elsewhere.

India’s export basket has evolved significantly over the past two decades. Petroleum products — refined products exported from India’s world-class refining complex (particularly Reliance Industries’ Jamnagar refinery, the world’s largest) — have become the single largest export category, accounting for approximately 14–16% of total merchandise exports. This is a remarkable transformation: India has leveraged its refining capacity to become a net exporter of petroleum products even as it imports the crude oil feedstock. The “refinery margin” — the spread between crude input costs and refined product export realisations — is essentially a value-added manufacturing activity embedded in the petroleum trade.

Beyond petroleum products, India’s key export categories include engineering goods, chemicals, pharmaceuticals, textiles and apparel, and agricultural products (principally rice, seafood, and spices). The pharmaceutical sector has grown into a global force — India is the world’s largest generic medicines supplier — and represents genuine high-value, innovation-driven exports. But it remains a relatively small share of total trade volumes compared to the low-value-added categories.

Cereal Exports: India as the World’s Rice Granary

India’s cereal export performance over the past two decades has been remarkable in its scale and concentrated in its composition. India is the world’s single largest rice exporter, accounting for approximately 40% of global rice trade in recent years. In 2022–23, India exported approximately 22 million tonnes of rice — more than the next three exporters (Thailand, Vietnam, and Pakistan) combined. This export supremacy reflects India’s large production base, competitive farm-gate prices, and the diversity of rice varieties grown across the country (non-basmati white rice for mass markets, basmati rice for premium markets in the Middle East, Europe, and North America).

The data shows a clear evolution in the composition of cereal exports. Wheat exports, which were significant in 2021–22 when India had bumper stocks and global supply was disrupted by the Ukraine war, were subsequently banned as domestic prices rose — a pattern that India has repeated several times over the past decade. Maize exports have been modest but growing, driven by expanding feed and starch industry demand globally. The government’s 2023 initiative to promote millets internationally — India hosted the International Year of Millets 2023 — may generate new export opportunities in niche premium markets, but volumes remain small.

The recurrent tension in India’s cereal export policy is between two legitimate objectives: generating export earnings and farmer income through market access, and controlling domestic food prices by keeping cereals within the country. When international prices are high, the pressure to ban exports increases — but export bans impose losses on farmers and damage India’s reliability as a trading partner. The ideal policy response would be to maintain export freedom but use domestic stock releases from government warehouses to moderate domestic prices — a more surgical approach than blanket export bans.

The Fertilizer Import Dependence: A Structural Trade Liability

India’s fertilizer imports represent one of the clearest cases of structural trade dependence in the country’s import basket. The country imports approximately USD 10–15 billion worth of fertilizers annually — roughly 2–3% of total imports — with significant year-to-year variation depending on international prices.

The 2021–22 price spike illustrated the fiscal dimensions of fertilizer import dependence dramatically. With urea prices crossing USD 900/MT and DAP prices approaching USD 1,000/MT, India’s fertilizer import bill approximately tripled — from roughly USD 7 billion in 2019–20 to nearly USD 21 billion in 2021–22. Since the government absorbs the entire price difference between international prices and subsidised retail prices, this effectively transferred the trade shock directly to the fiscal deficit.

From a trade policy perspective, reducing fertilizer import dependence requires investment in domestic manufacturing capacity (particularly for urea, where India has made progress through plant revivals and new builds), raw material security for phosphates (through long-term contracts with Moroccan and Jordanian producers), and demand-side efficiency improvements (better soil testing, precision application, and the development of nano-urea and bio-fertilizers as partial substitutes).

Petroleum Trade: India’s Biggest Import and Its Largest Export

The peculiar structure of India’s petroleum trade — crude oil is its largest import; petroleum products are its largest export — gives India’s merchandise trade an unusual character. India imports approximately 200–220 million tonnes of crude oil annually and exports approximately 60–70 million tonnes of refined petroleum products. The net petroleum trade balance is negative (India is a substantial net buyer in value terms), but the export revenue from refined products represents a significant economic contribution.

The Russia–Ukraine war created an unusual opportunity for India. As Western buyers moved away from Russian crude, India significantly increased its purchases of discounted Russian Urals crude — at times paying USD 20–30 per barrel below Brent crude prices. Indian refiners processed this cheap feedstock and exported the refined products to European markets that had banned Russian petroleum products. This triangular trade was enormously profitable for Indian refiners in 2022–23 and contributed to a significant improvement in India’s merchandise trade balance relative to what would have been expected given global oil prices.

However, this window of opportunity is not structurally permanent. As Western sanctions regimes evolve and Russian crude finds alternative outlets (primarily China), the discount on Russian crude available to India has been narrowing. India’s refinery sector needs to continue upgrading its technology and expanding its capacity to process a diverse range of crude grades.

The Export Ambition: What Would It Take?

India has articulated an ambition to achieve USD 1 trillion in merchandise exports by 2030. Given that exports stood at approximately USD 420–450 billion in 2022–23, reaching USD 1 trillion in seven years would require a compound annual growth rate of approximately 12–13% — considerably faster than India’s recent export growth.

Achieving this ambition requires a fundamental upgrade in the composition, not just the volume, of India’s exports. The key challenge is India’s under-representation in manufactured goods exports. China’s exports are approximately five times India’s in value terms, driven overwhelmingly by electronics, machinery, and industrial goods — categories where India’s presence is minimal. India needs to attract investment in electronics manufacturing (the Production Linked Incentive scheme for mobile phones is a beginning but only a beginning), develop supply chains in capital goods and machinery, and improve the competitiveness of its textile and apparel sector against Bangladesh and Vietnam.

Agricultural exports — including processed foods, spices, marine products, and dairy — represent an area of genuine competitive advantage for India. The growth in agricultural export value from approximately USD 30 billion (2013–14) to over USD 50 billion (2022–23) is encouraging. But this growth needs to be accompanied by improvement in food safety standards, traceability systems, and quality certification — all areas where India has faced challenges with export bans from the EU and other markets.

Trade Deficit Management: The Currency Dimension

India’s persistent trade deficit has implications beyond the trade balance itself. A large deficit must be financed — by capital inflows (foreign direct investment, portfolio investment, remittances from the Indian diaspora, and external commercial borrowing). Remittances, which have reached USD 100–110 billion annually, are India’s single largest source of foreign exchange and provide a critical buffer against trade deficit financing pressure.

The rupee-dollar exchange rate is an important variable in trade dynamics. A weaker rupee makes Indian exports cheaper in dollar terms (benefiting exporters) but increases the domestic currency cost of imports (adding to the import bill and contributing to inflation). The RBI’s exchange rate management has historically sought to prevent excessive rupee depreciation while allowing gradual adjustment — a delicate balance in an environment of global capital flow volatility.

Conclusion

India’s trade story is one of genuine achievement — from a minor participant in global trade to the world’s 10th largest trading nation — but also of structural challenges that will determine whether India can fulfil its ambition to become a major manufacturing and export powerhouse. Reducing the structural trade deficit requires not just export promotion but a fundamental transformation of India’s industrial base, supply chain participation, and trade policy architecture. The data on cereal exports, fertilizer imports, and petroleum trade tells a nuanced story: India is competitive in some areas (rice exports, refined petroleum), vulnerable in others (fertilizers, electronics), and still finding its footing in the manufactured goods categories that will define 21st-century trade leadership.

Data sources: Ministry of Commerce, India (export-import statistics); DGCI&S; World Trade Organization. Analysis by Pawan.

The Scale of the Problem: From Self-Sufficiency to Import Addiction

In the 1980s, India undertook the Technology Mission on Oilseeds (TMO), which dramatically expanded oilseed cultivation and reduced import dependence for a brief period. By the early 1990s, India came close to self-sufficiency in edible oils. But this achievement was short-lived. As incomes rose through the liberalisation era, per capita edible oil consumption began to climb steadily — from roughly 5 kg per capita per year in the 1980s to over 20 kg today. Domestic oilseed area expansion and yield improvements could not keep pace with this consumption surge.

The data from Ministry of Commerce records shows that by 2010–11, India was already importing approximately 8.7 million tonnes of edible oils annually. By 2023–24, this figure had crossed 16.5 million tonnes — an increase of nearly 90% over fourteen years. The import bill grew even faster because the surge in import volumes coincided with a global commodity supercycle and, critically, with the Russia–Ukraine war of 2022, which triggered an extraordinary price spike across all three major oil categories.

India’s self-sufficiency ratio — domestic edible oil availability as a percentage of total requirement — has been stuck below 50% for most of the past two decades. This means India depends on foreign sources for more than half of every litre of cooking oil consumed in the country. This is a structural vulnerability of the first order.

The Three-Oil Basket: Palm, Soybean, and Sunflower

India’s edible oil imports are dominated by three oil types, each sourced from a distinct geographic corridor and driven by its own demand and price dynamics.

The 2021–22 Price Shock: A Stress Test of India’s Oil Supply Chain

The financial year 2021–22 served as a brutal stress test for India’s edible oil supply chains. A confluence of three simultaneous shocks drove edible oil prices to multi-decade highs. First, the pandemic-era demand recovery in major consuming countries pushed global edible oil demand sharply upward. Second, poor palm oil harvests in Malaysia due to labour shortages (the plantation sector relies heavily on migrant workers who had returned home during COVID) reduced global supply. Third, and most critically, Russia’s invasion of Ukraine in February 2022 disrupted both sunflower oil supplies from the Black Sea corridor and soybean production in Argentina (via knock-on commodity market effects).

India’s total edible oil import bill crossed USD 20 billion in 2021–22 — an extraordinary expenditure that represented a severe drain on foreign exchange reserves and contributed directly to consumer price inflation. The retail price of mustard oil in Delhi touched ₹200 per litre; palm oil, the cheapest staple, crossed ₹140 per litre. For low-income households, which spend 30–40% of their food budget on edible oils, this was a severe income shock.

The government responded with emergency measures: reducing import duties on crude edible oils to zero, banning refined edible oil imports (to protect domestic refiners while ensuring crude oil supply), and releasing strategic reserves. These measures provided some relief, but they illustrated the fundamental problem — India had no deep strategic reserve of edible oils (unlike China, which maintains multi-month reserves), and its domestic production base was too small to provide a meaningful buffer.

Why Domestic Production Has Failed to Keep Pace

The failure of Indian oilseed production to meet domestic demand is rooted in multiple structural constraints. First, oilseed crops in India are predominantly rainfed and grown on marginal land, with yields far below global averages. India’s soybean yield is approximately 1.0–1.2 MT per hectare — roughly half the Brazilian level and a fraction of the US level. Mustard yields are similarly low by international standards.

Second, oilseed cultivation is in direct competition with cereals — primarily wheat and paddy — which are supported by far more robust procurement, price support, and input subsidy systems. Farmers have a strong rational incentive to grow wheat and paddy, which can be sold to government agencies at guaranteed MSP prices, over oilseeds, which must be sold in private markets at more volatile prices.

Third, the oil palm cultivation programme, which could theoretically provide the highest oil-per-hectare yields of any crop (~4–5 MT of oil per hectare versus 0.3–0.5 MT for soybean), has never scaled to anything approaching its potential. The National Mission on Edible Oils — Oil Palm (NMEO-OP), launched in 2021 with a target of expanding oil palm area to 10 lakh hectares by 2025–26, faces challenges of long gestation periods (oil palms take 5–7 years to reach commercial production), agro-climatic constraints, and the lack of a reliable cold-chain and processing infrastructure in potential cultivation areas like Andhra Pradesh, Telangana, and the Northeast.

Policy Levers and the Road Ahead

Reducing India’s edible oil import dependence requires action on three fronts simultaneously. On the supply side, India must invest far more aggressively in oilseed research, irrigation coverage for oilseed-growing regions, and procurement support for oilseed farmers. Raising soybean and mustard yields to even 60–70% of global best-practice levels would dramatically expand domestic production. Scaling up the NMEO-OP and solving its implementation bottlenecks is critical for long-term supply expansion.

On the demand side, India should encourage a gradual shift toward more diverse dietary fats, including domestic groundnut oil, sesame oil, and coconut oil, which are produced domestically in sufficient quantities for regional consumption. These oils have higher unit costs but also higher nutritional value and domestic supply resilience.

On the trade policy side, India should build a strategic edible oil reserve of at least 2–3 months of national consumption, financed through a modest cess on edible oil imports. Such a reserve would provide a meaningful buffer against the kind of supply shock that caused the 2021–22 price crisis, buying time for diplomatic and market interventions to take effect.

Conclusion

India’s edible oil import dependency is the product of decades of policy choices that prioritised cereal self-sufficiency over oilseed production — a choice that made sense in the food-insecure India of the 1960s and 1970s but has left the country structurally exposed to global commodity markets and geopolitical shocks in the twenty-first century. The 2021–22 oil price crisis was a warning. The window to act — through aggressive oilseed production enhancement, oil palm expansion, and strategic reserve creation — is open, but it will not remain so indefinitely. India’s kitchen economy, feeding 1.4 billion people three meals a day, deserves a more resilient foundation.

Data sources: Ministry of Commerce, India (import statistics); Ministry of Agriculture & Farmers Welfare (oilseed production and availability data); Solvent Extractors’ Association of India. Analysis by Pawan.