India's statistical system needs an update: Thankfully, next year’s base-year revision offers a chance to reform it

But these debates miss a key point. India’s core methodology for GDP estimation is broadly sound and internationally aligned; the real weakness lies in the broader statistical ecosystem—data-sets that haven’t kept up with structural shifts, outdated reconciliation tools and price measures that struggle to reflect fast-changing production and consumption.

The result is an over-interpretation of the headline number without the context needed to read it properly. Unless we modernize this architecture, we will keep debating symptoms rather than the underlying issues that matter for interpreting GDP data in a rapidly evolving economy.

Manufacturing—an 80-20 measurement split that distorts the narrative: Two core indicators, manufacturing gross value added (GVA) and the Index of Industrial Production (IIP), often seem to diverge. This isn’t a contradiction, but a feature of the system. About 80% of manufacturing GVA comes from the organized corporate sector, estimated from quarterly filings of roughly 1,500 firms that report sales, input costs and operating expenses.

This aligns with global practice and recent data shows solid momentum: corporate manufacturing has been growing 10-20% this year, with earnings before interest, taxes, depreciation and amortization rising by about 9.6%.

The remaining 20% comprises quasi-corporate, unincorporated and informal units, and is harder to measure. These enterprises don’t file quarterly accounts, so the statistics ministry uses the IIP as a proxy, applying volume growth and converting it to current prices via the wholesale price index (WPI).

But the IIP tracks physical output, not value added; it misses margins, input costs, product upgrades and service intensity. It cannot really mirror value addition. The result: earnings look strong and GVA remains high, but the IIP moves differently. This divergence reflects our statistical design, not economic stress. We need better ways to track the informal sector.

GDP discrepancies reflect data gaps: The growing gap between production-based and expenditure-based GDP has evoked valid concerns. While the global norm is to keep such discrepancies within 3% of GDP, India has recently crossed that point. The divergence stems from uneven data quality. Production GDP draws on frequent high-detail sources—corporate filings, administrative data and sectoral statistics, while expenditure GDP relies on lower-frequency consumption and investment surveys, which are often revised substantially later.

Two structural issues aggravate this mismatch: A base revision delay, which means weights no longer mirror the present economy, and the absence of regular ‘supply and use tables’ (SUTs), which are standard reconciliation tools across advanced economies. We haven’t produced input–output tables for 15-18 years and our SUTs are compiled only after annual estimates are released. But the global best practice is to compile SUTs before we finalize a year’s estimates and use them for quarterly and advance projections. This approach would eliminate discrepancies in annual data and sharply reduce them in quarterly estimates.

The paradox of IMF’s ‘A’ versus ‘C’ rating: India’s statistical weaknesses become clearer if viewed through global evaluations. Many emerging economies, including India, face a familiar paradox: the International Monetary Fund (IMF) may award an ‘A’ for National Accounts but a ‘C’ for the overall statistical system.

GDP methodology is just one element of the IMF’s review, which also covers the whole statistical ecosystem: public finance data, external-sector reporting, monetary and banking statistics, financial-sector disclosures and the reconciliation frameworks that link them.

Gaps in fiscal coverage, delays in financial reporting, inconsistencies across administrative data-sets and a weak SUT/input-output table foundation drag down the composite grade even though our National Accounts meet global norms. In other words, India’s GDP gauge is well-designed but the dashboard around it needs an update.

Next year’s base-year revision offers us a window to realign our statistical system with the economy it measures. Five reforms are particularly urgent:

One, create a corporate manufacturing growth index: The ministry of corporate affairs can leverage MCA-21 data-base filings to create a transparent, high-frequency index that bridges the gap between GVA and the IIP. Such an index would track manufacturing, provide markets clarity and reflect significant quarterly variations (often 10-20% across segments).

Two, use the ASUSE to track the informal sector directly: The Annual Survey of Unincorporated Sector Enterprises (ASUSE) offers a basis for direct measurement of informal-sector GVA. Integrating this data with quarterly GDP after the 2026 revision would reduce our reliance on IIP proxies

Three, institutionalize SUTs and reinstate input-output tables: Both these are non-negotiables in advanced statistical systems. They reconcile production, expenditure and income accounts, remove discrepancies and support GDP interpretability.

Four, update deflators: For more accurate estimation of real GDP growth, we should employ a broad suite of producer price indices that includes a producer price index (PPI) for goods and services, both for inputs and outputs (industry-wise where needed). The WPI alone will not suffice.

Five, invest in statistical capacity: Inter-agency platforms, modern IT systems, data engineers and other skilled statistical staff form the backbone of reliable measurement. We must strengthen it.

A modern economy needs a modern statistical system. Today, corporate performance looks strong but we have no dedicated index to track it; deflators skew interpretation; discrepancies widen because our reconciliation tools are outdated; and the informal sector is estimated through proxies never designed for that role.

None of this discredits India’s GDP, but it does constrain how precisely we can read the economy. India’s ambitions demand sharper statistical clarity. The 2026 base-year revision is a chance to upgrade the system for the next decade. If we get it right, our data confidence will rise to match the economy’s maturity.

The authors are, respectively, former director general, ministry of statistics & programme implementation, and chief statistician, Pahle India Foundation (PIF); and senior fellow, PIF.