India’s GDP Makeover: What Is Changing?

In November 2025, India’s national accounts data received a “C” grade from the International Monetary Fund, reflecting concerns around coverage gaps. Meanwhile, the Indian government is planning to release a new GDP (gross domestic product) series this year as part of a routine base-year revision. In this post, Pramanik and Sengupta discuss two key changes being planned, as well as the implications for state-level GDP estimates.

For any government to make good policy decisions, it needs a reliable “GPS” for the economy. In India, however, that GPS has been glitchy. Over the last few years, economists and global institutions have raised red flags about how we calculate our Gross Domestic Product (GDP) – the most comprehensive indicator of economic health.

The most recent wake-up call came in November 2025 when India received a “C” grade for its national accounts data in the International Monetary Fund’s Data Adequacy for Surveillance (DAS) assessment (International Monetary Fund (IMF), 2025).

While India fares well at releasing data on time (earning an “A” for the frequency and timeliness of data releases) or at the granularity of estimates (graded “B”), the quality of the data is where the problems lie. In particular, IMF’s concerns relate to coverage gaps (graded “C”), including an outdated base year, limited recent benchmark data, gaps in measurement of the informal sector, and continued reliance on single deflation methods for estimating real GDP.

To elaborate, we are still judging today’s economy using a “base year” from 2011. This is akin to navigating 2025 traffic with a map from 2011. A huge part of India’s economy – small roadside shops and local labourers – is hard to track. Our current methods often miss the pulse of this informal world. And the methods used do not accurately separate “real growth” from “price rises” (inflation) across different industries.

According to the IMF, India’s poor rating for national accounts indicates that while the data are broadly usable for monitoring growth and structural change, certain methodological gaps still constrain the robustness of economic surveillance.

Many of the concerns raised by the IMF have been consistently highlighted by analysts and economists over the past few years as important drawbacks in the existing data.

The good news is that the Ministry of Statistics and Program Implementation (MoSPI) is planning to release a new GDP series in 2026 which will hopefully address most of the issues highlighted by the critics and the IMF alike. In the sections below, we discuss two main changes that are being planned, throw light on the implications of the base-year revision for state-level GDP estimates and briefly talk about how the latter can be improved.

Why are we changing how we measure the economy?

The United Nations System of National Accounts (SNA) 1993 recommends that countries update the base year of their national accounts every five years. This ensures that the GDP data reflect current production structures, updated relative prices, and new technological advancements (Eurostat et al. 1993).

While many advanced economies follow a five-year revision cycle, India has historically revised the base year of its national accounts roughly once every decade – moving from 1960-61 to 1970-71, 1980-81 and 1993-94. This pattern briefly shifted to five-year revisions in the late 1990s and 2000s (1999-2000, 2004-05 and 2011-12), before the latest cycle again stretched to a 10-year gap (2022-23). Simply put, India’s current economic data continue to be benchmarked to the structure of the economy as it existed in 2011-12. To address this, the National Statistics Office (NSO) is preparing a major update: starting in early 2026, India will adopt a new base year of 2022-23.

Along with the base year revision, the NSO is also planning to revamp GDP measurement by introducing new datasets and more recent surveys, expanding sectoral coverage, and improving price indices – enhancements that should better capture the structure of a fast-changing economy. In particular the following two major changes are worth noting.

Tracking the ‘hidden’ economy: The informal sector

One of the major challenges in measuring India’s economy is the unorganised or informal sector – the millions of small shops, unincorporated businesses, household-sector enterprises, and self-employed people who do not always leave a paper trail. In the current system, a part of the informal sector activity is not measured directly and is instead imputed using a set of simplifying and often unrealistic assumptions. This refers mostly to non-agricultural, non-construction activities.

Under the current “benchmark-indicator” method, the NSO does not measure these activities every year. Instead, they constructed a benchmark gross value added (GVA) in 2011, using the Effective Labour Input method. The NSO took the GVA per worker from the 2010-11 Unincorporated Enterprise Survey and multiplied this by the total number of workers from the 2011-12 Employment and Unemployment Survey. To figure out the GVA for all subsequent years, they did not go back and count again because fresh surveys were not available. Instead, they extrapolated the old figures using growth proxies from the formal, that is, the private, corporate sector.

For example, the growth seen in large, registered factories (from the Annual Survey of Industries (ASI)) was used as a proxy for the growth of small, unregistered enterprises. This approach rests on the assumption that the formal and informal sectors move in tandem.

However, it suffers from two limitations.

First, the assumption that unorganised-sector activity evolves in a manner similar to the organised sector broke down during major shocks such as demonetisation (2016), the implementation of GST (goods and services tax) (2017), and the Covid-19 pandemic. These shocks hit the unorganised enterprises much harder than the bigger firms but the old methodology failed to capture that difference. As a result, this led to a gap between what the official numbers have been conveying and what has been happening on the ground.

Second, the current method depends on outdated productivity and employment benchmarks, leading to a growing divergence from actual informal-sector dynamics as the years go by.

The upcoming 2022-23 GDP series marks a significant departure from this reliance on proxies. Rather than assuming that the informal sector growth mimics that of the big corporations, the NSO will now utilise two primary annual datasets:

(i) Annual Survey of Unincorporated Sector Enterprises (ASUSE): Providing yearly data on the performance of unorganised sector businesses.

(ii) Periodic Labour Force Survey (PLFS): Providing regular updates on employment trends and workforce size.

The unorganised sector GVA will now be calculated every year as value added per worker obtained from ASUSE multiplied by total workforce as obtained from PLFS. By integrating these annual sources, the 2022-23 series eliminates the need for decade-old benchmarks. This shift moves India toward global best practices, ensuring that the GDP data reflects the true volatility and dynamism of the informal economy.

Refining real GDP: Solving the deflator problem

While nominal GDP tracks the rupee value of the total output produced in the economy, real GDP is the metric most relevant to policymakers because it adjusts for price changes to show actual growth in production. To calculate this, the NSO must “deflate” nominal values using price indices. However, India’s current methodology deviates from international standards in two critical ways that may have led to an overestimation of recent economic growth.

Price indices used for deflation: In most advanced economies, production is deflated using a Producer Price Index (PPI). Since India lacks a PPI, the NSO relies mostly on the Wholesale Price Index (WPI). In some ways this makes sense because the WPI is arguably the closest available substitute, as it measures the price of goods at the factory gate. But the WPI suffers from two drawbacks. First, the WPI is heavily weighted toward commodities like oil and metals, and it does not track the price of services. Using a commodity-heavy index to deflate any part of the service-sector growth (such as hotels and restaurants, finance and trade, storage and warehousing, etc.) creates a mismatch. Secondly, when global oil prices fall, the WPI also falls. Under current methods, this can register as a “price deflation” for the services sector, even if the actual cost of providing those services is rising. This can artificially inflate the reported “real” growth rate of the service sector.

Single versus double deflation: The second issue concerns the “deflation procedure”. In most advanced countries, real manufacturing GVA is computed using a methodology known as “double deflation”. This involves a three-step process: (i) Deflating the value of total output using an output price index. (ii) Deflating the cost of raw materials (inputs) using an input price index. (iii) Subtracting the real inputs from the real outputs to find the “real GVA”.

In India, except for the agricultural sector, the NSO uses single deflation, wherein it takes the final nominal GVA and deflates it by a single price index. This works only if the prices of raw materials and finished goods move together.

However, if input prices (like energy or steel) fall sharply while output prices remain steady, nominal profits will rise. Since real GDP is supposed to be measured at ‘constant prices’, this increase needs to be deflated away. Double deflation will do this easily. But under single deflation, this increase in profit may get misread as an increase in real production.

The 2022-23 GDP series introduces two major corrections:

Manufacturing: The NSO will move toward double deflation, using data on specific input prices. The only exception will be manufacturing activities that use a substantial share of imported inputs and reliable price indices for these inputs are not available.Other sectors: For sectors where double deflation remains difficult, the NSO will shift from single deflation to volume/single extrapolation. While single deflation assumes that the ratio of input and output prices is constant, volume/single extrapolation assumes that input-output ratio in volume terms remains broadly stable in the short run. According to the NSO, the latter approach is more reasonable because it focuses on the physical volume of activity and avoids distortions caused by using the same price index for both inputs and outputs. In case of some services activities, the single extrapolation will be done using the relevant sub-components of the Consumer Price Index (CPI) (such as for real estate services, personal services, education, health, etc.).

These changes aim to bring India’s national accounts closer to the SNA standards. However, complete updates to the real GDP estimates will occur only after the ongoing base-year revisions of all underlying indices (WPI, CPI, etc.) are completed, which is expected by the end of May 2026.

Implications for regional accounts: The fiscal tug-of-war

A critical question for the 2026 revision is how it will alter Gross State Domestic Product (GSDP) estimates. GSDP is the primary denominator used to calculate a state’s fiscal health, and hence, any change in its measurement directly impacts a state’s budget through two competing channels.

Expanding the capacity to borrow: Under India’s Fiscal Responsibility and Budget Management (FRBM) framework, states are allowed to borrow up to 3% of GSDP (with some flexibility depending on other fiscal indicators). If as a result of the 2026 revision, a state’s GSDP estimates get revised upward, this creates more borrowing headroom, thereby enabling the state to borrow more for funding essential projects such as infrastructure, without technically breaching its fiscal limits.The risk of reduced central transfers: While a larger GSDP helps with borrowing, it can be a disadvantage when it comes to receiving money from the central government. The Finance Commission uses a “wealth-based” formula to decide how to distribute tax revenues among states. A significant portion (45%) of these transfers is based on how far a state’s per-capita income is from the wealthiest state. If the 2026 revision shows that a state is “richer” than previously thought, its “income distance” from the benchmark narrows. Statistically, the state appears less in need of support, which could result in a smaller share of central tax devolution.

These channels work in opposite directions: higher GSDP can raise borrowing capacity but reduce central tax devolution. The net fiscal effect will vary across states depending on the magnitude of the revision and the structure of their economies. Ultimately, the 2026 series will force state finance departments to recalibrate their long-term plans. For some, it will be an opportunity for expansion; for others, it may require a tighter belt as central support shifts elsewhere.

Strengthening regional data: From ‘apportionment’ to a bottom-up approach

A long-standing challenge in India’s official statistics is how we calculate the economic output of individual states. Historically, for GSDP estimations, the NSO has relied heavily on a method called apportionment, especially for private corporations in the manufacturing and services sectors. Under the apportionment method, the government first calculates the national-level GVA for a specific sector (such as hotels and restaurants or telecommunications) and then “slices the pie” among the states. They do this using proxy indicators – such as the number of employees or the level of local consumption in each state, etc.

The more accurate and credible alternative is a bottom-up approach. Instead of slicing a national pie, this method builds the state’s economy from the ground up by aggregating the actual value added by every enterprise operating within that state’s borders. While we already have some “bottom-up” data for factories and unincorporated enterprises from surveys such as the ASI and ASUSE, a major gap remains in the organised service sector (such as IT firms, consultancy firms, etc.), which is large, and heterogeneous. Currently, no regular survey tracks these entities at the state level.

To bridge this gap, MoSPI has proposed the Annual Survey of Incorporated Service Sector Enterprises (ASISSE), with the first round of data expected by 2027. However, the transition to a truly modern GSDP framework does not have to wait for the central government.

By launching their own surveys of local service-sector and construction firms, proactive states can shift to a bottom-up GSDP framework years before the national rollout. They can even provide empirical evidence that can help shape how the National Accounts Division designs future methodologies for GSDP estimation and also gain a more precise understanding of their own tax base and sectoral strengths.

Ultimately, moving away from “proxies” and toward “actuals” will turn GSDP from a top-down approach into a more precise bottom-up method for regional economic planning.

The road ahead: From correction to continuous evolution

India’s national accounts system is already among the most detailed in the developing world. The 2026 data revision, powered by new, high-frequency enterprise data and more precise deflation techniques, represents a significant step toward a more accurate and credible measurement of the economy.

Having said that, in a modern economy, where statistical system must evolve continuously, the primary challenge is not the intent but the pace of change. A statistical system that updates only once a decade may struggle to keep pace with an economy that is undergoing rapid structural transformations.

The recent scrutiny from the IMF and the domestic academic community should not be viewed as a setback. Rather, it provides the necessary momentum to strengthen India’s statistical infrastructure and institutionalise several critical reforms:

Normalising the five-year cycle: Moving forward, India must transition to a predictable, five-year revision cycle to help ensure that policy decisions are always based on contemporary, updated data.

The importance of a “back series”: When a new GDP series is released, it can create a “break” in the data, making it difficult to compare today’s growth with the past. Providing a credible back series is essential for researchers to conduct long-term trend analysis.

Seasonal adjustments: To understand the economy’s momentum in real-time, the NSO should move toward releasing seasonally adjusted GDP and CPI series. This allows analysts to distinguish between a genuine economic slowdown and a predictable seasonal dip (such as the post-festival lull).

The upcoming 2026 overhaul of India’s national accounts data is more than a technical exercise. It is an opportunity to reassert India’s commitment to transparency and integrity in official data and to provide a more honest and credible account of the overall economic health. For a country with global economic ambitions, a world-class statistical infrastructure is not optional – it is a foundational requirement.

The authors gratefully acknowledge the discussions and comments received from various people based on the earlier drafts of this piece. In particular, they extend their heartfelt thanks to Preksha Gupta, Shankar Narayanan, and Manasi Narasimhan.

The views expressed in this post are solely those of the authors, and do not necessarily reflect those of the I4I Editorial Board.

Dr Rajeswari Sengupta is an Associate Professor of Economics at the Indira Gandhi Institute of Development Research in Mumbai. Santanu Pramanik is a Lead Statistician at the Centre for Effective Governance of Indian States (CEGIS).

This article has been republished from Ideas for India. Read the original article. 

This article went live on January seventh, two thousand twenty six, at one minutes past five in the evening.

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