GDP growth: India’s GDP numbers have begun to raise more questions than they answer. Official data show real growth of 8.2% in Q2 FY26, yet businesses, households, and market analysts struggle to reconcile this with everyday economic conditions. The dissonance has revived scrutiny of how growth is measured—and why headline numbers appear increasingly detached from high-frequency indicators. Against this backdrop, the GDP series revision scheduled for February 2026 carries significance well beyond statistical housekeeping. It is an attempt to align national accounts with an economy whose consumption structure has quietly but decisively changed.
How Indian households are actually spending
The Household Consumption Expenditure Surveys (HCES) for 2022-23 and 2023-24, released by the Ministry of Statistics and Programme Implementation (MoSPI), capture a break from long-established patterns. For the first time since Independence, both rural and urban households spend a larger share of their budgets on non-food items than on food.
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Expenditure on transport, education, health, electricity, fuel, housing services, and durable goods now outweighs spending on cereals and edible oils. This is not a cyclical blip. It reflects rising incomes, the spread of service markets, and the steady urbanisation of consumption preferences—even in rural India. When national accounts continue to infer consumption largely from the production of goods, they risk misreading an economy where services increasingly anchor household demand.
Why the commodity-flow method no longer works
MoSPI’s decision to move away from an almost exclusive reliance on the commodity-flow approach marks a structural shift in thinking. Under the existing framework, private final consumption expenditure (PFCE) is estimated as a residual—what remains after accounting for output, intermediate use, exports, inventories, and government spending.
This approach was defensible when household surveys were infrequent and manufacturing data were relatively robust. It is increasingly untenable in a service-heavy economy. Treating consumption as “what is left over” works poorly when large parts of spending leave little trace in conventional production datasets.
The hybrid consumption framework under the new series
The revised GDP series proposes a hybrid estimation model. Consumption of 61 widely used items, largely food, will be drawn directly from HCES data. Another 34 items—including mobility services, fuels, electricity, health, and education—will be estimated using administrative sources such as vehicle registrations, airline and railway passenger data, petroleum sales, and electricity generation. Only the residual will continue to be inferred through production-side balancing.
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The acceptance that no single method can capture a complex, service-dominated economy is an important departure from past practice. Triangulation is no longer a methodological preference but a necessity.
Yet even this broadening risks undercounting a growing slice of India’s economy. A rising share of household spending now takes place through platform-mediated and semi-formal services—ride-hailing, food delivery, online tutoring, telemedicine, freelance work, and small digital commerce. These transactions leave uneven administrative trails: partially visible in GST data, imperfectly captured in household surveys, and often absent from conventional production statistics. The services shift is therefore not just from goods to services, but from formal to digitally intermediated activity, complicating any clean mapping of consumption to value added.
This helps explain why headline growth appears detached from lived experience. When large volumes of spending sit in grey zones between accounting categories, triangulation improves accuracy only at the margins. Without deeper integration of tax, payments, and platform-level data, even a hybrid framework risks reproducing the same disconnect—only with greater technical sophistication.
Growth versus high-frequency indicators
The methodological rethink also reflects discomfort with the widening gap between GDP growth and high-frequency indicators. Real GDP growth averaging close to 8% in H1 FY26 sits uneasily with an Index of Industrial Production (IIP) expansion of around 3% over the same period.
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Manufacturing value added appears strong in quarterly GDP estimates even as factory output has slowed. Air passenger traffic weakened year-on-year in parts of the July–September quarter. Foreign tourist arrivals remain below last year’s levels, and GST revenue growth has struggled to sustain momentum. Economists at Nomura have noted that this divergence complicates any assessment of whether GDP numbers are reliably reflecting the economy’s health.
The deflator problem inflating real growth
Part of the distortion lies in India’s approach to estimating real growth. For many sectors, nominal values are deflated using a single price index, rather than applying proper double deflation to inputs and outputs separately. When input and output prices diverge—as they have in recent months—this shortcut can materially inflate real value added.
The problem is most acute in services. Nominal service-sector output is often deflated using wholesale price indices that exclude services altogether. With input prices rising more slowly than output prices, the method mechanically boosts real growth. The result is GDP data that exaggerate momentum relative to the price pressures households actually face.
Measurement issues are also evident on the income side of GDP, which the revision leaves largely untouched. Recent national accounts show a widening gap between corporate profits and labour incomes, with wage growth lagging even as aggregate value added accelerates. This divergence reinforces scepticism around consumption-led growth. Revising how spending is estimated may narrow part of the gap, but it does not resolve inconsistencies between output growth and income distribution.
Nor does the GDP debate play out evenly across the country. National aggregates mask sharp inter-state divergences, with services-heavy states pulling up averages even as large agrarian and manufacturing-dependent states struggle with weak demand. For households in these regions, strong national growth offers little reassurance. Statistical refinement at the centre cannot correct for the political economy of uneven growth on the ground.
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Known weaknesses, delayed fixes
These shortcomings are not unknown to policymakers. Senior officials have acknowledged that when input and output prices move in different directions, growth estimates can be overstated or understated depending on the method used. The absence of a fully operational producer price index (PPI) remains a critical gap.
The International Monetary Fund, in repeated assessments of India’s national accounts, has flagged these limitations. The forthcoming GDP revision is therefore less a technical adjustment than an admission that incremental fixes can no longer substitute for structural reform of the statistical toolkit.
Updating how consumption is classified
The decision to adopt the COICOP 2018 classification, replacing the 1999 standard, is another overdue reset. Aligning with current UN norms allows consumption to be measured by purpose rather than commodity, better reflecting how households think about spending.
In an economy where a smartphone functions simultaneously as a communication device, payment instrument, and entertainment platform, conceptual clarity is not cosmetic. It is central to credible measurement.
A necessary correction, not a verdict
There is also a credibility dimension that technical fixes alone cannot settle. Frequent revisions, delayed releases, and methodological changes over the past decade have made GDP data a subject of public scepticism rather than quiet acceptance. Even defensible improvements now arrive in a climate of diminished trust. Restoring confidence will require not just better methods, but greater transparency in how revisions alter past estimates and what those changes imply for policy.
This matters because mismeasurement is not a neutral error. Overstated real growth can encourage premature fiscal tightening; understated consumption can distort monetary policy calibration. The costs are borne not in spreadsheets, but in policy choices.
India is no longer an economy where consumption can be inferred from grain output and factory dispatches. The new GDP series is not a verdict on how fast the economy is growing. It is an overdue attempt to ensure that statistics reflect structure. Without that alignment, growth numbers will continue to confuse more than they clarify.