Ishant Deshmukh, Research Assistant, Jnana Prabodhini – Social Sciences Study Centre
Over the past week, India’s GDP numbers have been on everyone’s lips. What started as a technical debate over GDP methodology, the kind of discussion usually confined to policymakers and economics classrooms, has now found its way into our everyday conversations, lunch breaks and evening tea. And wherever you turn your attention, to newspaper headlines, television debates or your social media feed, the GDP numbers are there again, refusing to leave.
So, what is all the hullabaloo about?
Now, there are two types of GDP that are important to understand: real GDP and nominal GDP.
Real GDP, or GDP at constant prices, removes the effect of price changes. It helps us answer the question: “Has the economy actually produced more goods and services?”
Nominal GDP, or GDP at current prices, measures the value of the economy using the prices prevailing at the time. It answers a different question: “What is the economy worth at today’s prices?”
So, why do we need both?
Because one number cannot tell us both things at the same time. If we looked only at nominal GDP, an increase could simply reflect higher prices rather than greater production. If we looked only at real GDP, we would miss the actual rupee value of the economy at current prices. Real GDP helps us understand growth; nominal GDP helps us understand value. We need both to get a fuller picture of the economy.
On 31st August, the Ministry of Statistics and Programme Implementation (MOSPI) released its GDP quarterly estimates for the first quarter of FY 2026-27 (April–June 2026). According to these estimates, real GDP grew 7.8% year on year. In value terms, real GDP stood at ₹81.36 lakh crore, compared with ₹75.46 lakh crore in Q1 FY 2025-26 (April–June 2025).
Nominal GDP stood at ₹88.27 lakh crore in Q1 FY 2026-27, compared with ₹80 lakh crore in the same quarter a year earlier, registering a growth of 10.3%.
Data Source: MOSPI
You might wonder: so, what? These are just numbers. GDP estimates are released every quarter. The numbers go up, the numbers go down, some economists discuss them on our TV screens, we change the channel to something more interesting, and most of us carry on with our day.
So why, this time, has what should have been a fairly routine release of economic data opened the floodgates to a much larger debate?
The answer is that, this time, the GDP numbers seem to have developed a bit of a split personality. Depending on which number and which calculation you look at, the GDP debate, and the perceived health of the economy, can look remarkably different.
India’s former Finance Secretary (2019) and Economic Affairs Secretary (2017–2019), Subhash Chandra Garg, argued in an interview with NDTV that India’s actual GDP growth in the first quarter of FY27 was 2.6%, rather than the 7.8% reported by the government.
Garg was not the only one surprised by the number. The 7.8% growth figure came in higher than what many economists and even the Reserve Bank of India (RBI) had expected. The RBI had projected growth of around 7%. The fact that the economy grew even faster than these expectations, despite concerns over oil-price shocks and global supply-chain disruptions, made the number all the more striking.
But there was another reason this GDP release drew so much attention. This was not simply another quarterly update. It came after the government introduced a new GDP series, changing the base year from 2011-12 to 2022-23 and revising some of the historical GDP estimates. And it is these changes to the way GDP is measured, and the revisions that followed, that lie at the heart of the current controversy.
Garg’s argument, put simply, starts with what happened to last year’s nominal GDP. When the government originally released the Q1 FY 2025-26 estimates, GDP at current prices (nominal GDP) was around ₹86 lakh crore. When the government later introduced the new GDP series, that estimate for the same quarter was revised down to around ₹80 lakh crore, a reduction of roughly ₹6 lakh crore.
Why does this matter? Because the growth rate for Q1 FY 2026-27 is calculated by comparing the current quarter with the same quarter a year earlier. Using the revised ₹80 lakh crore nominal GDP for Q1 FY26 as the starting point, Q1 FY 2026-27’s nominal GDP of ₹88.27 lakh crore represents a 10.3% increase. But if the earlier estimate of ₹86 lakh crore had remained unchanged, the increase would have been only about 2.6%.
This is the crux of Garg’s argument: the downward revision of last year’s nominal GDP has changed the starting point against which this year’s nominal GDP is compared. A lower starting point makes the increase appear much larger.
However, Garg’s comparison has an important problem. The ₹86 lakh crore figure belonged to the old GDP series, while the ₹80 lakh crore figure came from the new series. In other words, he was comparing a number calculated under the old methodology with one calculated under the new methodology. Economists who have criticised his argument say this is an “apples-to-oranges” comparison; the two numbers are not strictly comparable.
There is another important distinction. Garg’s 2.6% figure is a calculation of nominal GDP growth, using GDP at current prices. The government’s 7.8% figure, however, is real GDP growth, calculated at constant prices after accounting for price changes. So, the 2.6% figure cannot, by itself, be treated as an alternative calculation of the government’s 7.8% real growth rate.
Garg’s calculation has since been examined and challenged by several economists and has become the subject of considerable discussion in leading newspapers and opinion pieces. But while the debate has generated plenty of commentary, many of its underlying concepts and nuances have not always been explained in a way that is easy for a non-economist to follow.
That is what we will try to do here. Rather than simply asking whether Garg is right or wrong, we will first try to understand what these numbers actually mean, why different methods can produce very different-looking results, why the methodology needs to change, and what these changes mean for how we measure the economy.
To answer that, we first need to understand what the new GDP series is actually trying to do and why it was introduced.
With time, the structure of the economy changes. What mattered to the Indian economy ten or fifteen years ago may not carry the same weight today. New industries emerge, consumer habits change, and the way people work and spend evolves. Consider something as ordinary as CDs, DVDs and VCRs. They were once a familiar part of household consumption. Now, you’d be lucky to find someone who even remembers where they kept their VCR. In their place have come OTT (Over-the-Top) subscriptions and a whole range of digital services that barely featured in the consumption patterns of the older GDP series.
That is why countries periodically update their GDP series. India generally aims to revise its GDP base year every five years, so that the reference year remains reasonably up to date and the GDP series continues to reflect changes in the structure of the economy, new data sources and improved methods.
India has revised its base year several times over the years, with earlier base years including 1999–2000, 2004–05 and 2011–12. The latest revision, however, did not follow a strict five-year cycle. This was partly because the Indian economy underwent significant changes during this period. The rollout of GST in 2017 brought a valuable new administrative data source, but the years around the rollout also required time for consolidation. The COVID-19 pandemic then severely disrupted economic activity, making the pandemic years less suitable as benchmark years.
There was also an important issue of data availability. Following the recommendations of the National Statistical Commission (NSC), the government had planned a Household Consumption Expenditure Survey (HCES) for 2020–21. MoSPI subsequently decided to conduct two consecutive surveys to help inform a decision on revising the base year for major macroeconomic indicators, including GDP and CPI. However, the COVID-19 pandemic delayed the launch of the survey. Once economic activity returned to normal, the two surveys were conducted for 2022–23 and 2023–24.
For the current revision, the Advisory Committee on National Accounts Statistics considered 2022–23 a normal economic year, and important survey data needed for estimating national income were available for that year. This made 2022–23 a suitable reference year for the new series. The latest series therefore shifts the base year from 2011–12 to 2022–23, with the aim of making GDP estimates more representative of the economy and improving the data and methods used to measure it.
But the revision is not only about choosing a new base year. The information available to measure the economy has also improved. The new series makes greater use of sources such as GST records, corporate financial information, the Periodic Labour Force Survey, the Annual Survey of Unincorporated Sector Enterprises and administrative databases. These newer sources can give statisticians a better picture of parts of the economy that are otherwise difficult to measure.
There is therefore another reason for the methodological change. As better and more detailed data become available, statisticians can improve how they measure economic activity and account for changing prices when calculating real GDP.
So, updating a GDP series is neither unusual nor, by itself, suspicious. The rationale is quite simple: if the economy changes and the information we have about it improves, the way we measure the economy should change too.
But this raises an obvious question. If the new GDP series uses new weights, classifications and data sources, how can it be used to calculate GDP for earlier years when some activities were either much smaller or did not exist at all?
This is where the back series comes in. A back series is essentially the process of recalculating past GDP estimates using the revised methodology and data framework of the new series, so that historical estimates can be compared with the new ones.
But this does not mean that today’s economy is simply projected backwards. In an earlier back-series exercise, for example, newly covered activities such as software and communication services were incorporated only for the years in which those activities actually existed in the economy. In other words, a newer activity is not simply inserted into an older year because it has become important today.
For the current revision, MoSPI says that the revised methodology will be used to recalculate historical estimates up to the previous base year. For years further back, the series will be spliced; the newer and older GDP series are joined together to create one continuous historical record.
There is one important caveat: the detailed methodology for the current 2022–23 back series has not yet been finalised. MoSPI says the final approach will be decided in consultation with the Advisory Committee, with the back series expected by December 2026.
We now know why GDP series are updated. But that still leaves us with a more interesting question: how do these changes actually affect the GDP number?
This brings us to the nitty-gritty of the new GDP series. Broadly, the changes involve three things: the reference point we use to measure the economy, the information we use to measure it, and the way we turn that information into a measure of real growth. We have already looked at the first two. It is the third that gets a little more technical, so let us understand it step by step.
We have already seen that nominal GDP reflects both changes in economic activity and changes in prices. But if we want to know whether the economy actually produced more goods and services, we need to separate these two effects.
This is where deflation comes in. In simple terms, deflation means removing the effect of price changes from a nominal value to arrive at a real, or constant-price, measure. The measure of how prices have changed relative to a base period is called a deflator.
The standard formula of GDP Deflator = (Nominal GDP ÷ Real GDP) × 100
So, put simply: Deflation is the process. A deflator measures the price change used in that process.
For something like the output of a factory, this may seem fairly straightforward. We can simply ask: how much of the increase in the value of its output came from producing more, and how much came from higher prices?
But GDP is not simply the value of everything produced. It is ultimately built from the value added by different industries. This brings us to Gross Value Added, or GVA.
GVA measures the value that a business or industry adds to the economy, the value of what it produces minus the value of the intermediate inputs it uses to produce it.
A useful way to think about the relationship is this: GVA tells us how much value producers add, while GDP adds taxes and subtracts subsidies on products from that amount.
GDP = GVA + Product taxes − Product subsidies
We do not need to get into this distinction in detail here. What matters for our discussion is that GVA captures the value added by producers, what they produce minus what they use to produce it.
And that is where things get interesting.
Let us understand this with an example of a furniture factory. The figures are hypothetical and are used purely for simplicity and ease of understanding.
Suppose that in Year 1, the factory produces 10 tables, each priced at ₹10, giving a total output value of ₹100. The wood, electricity and other intermediate inputs used to produce them cost ₹60.
Its GVA is therefore: ₹100 − ₹60 = ₹40
Now imagine that in Year 2, the factory produces 20% more tables, 12 instead of 10. At the original price of ₹10 per table, those 12 tables would be worth ₹120.
But prices have changed too. Suppose the price of the tables has increased by 8%, while the prices of the factory’s inputs have increased by 20%.
The factory’s output is therefore worth: ₹120 × 1.08 = ₹129.60
For simplicity, assume that the factory uses the same physical quantity of inputs as in Year 1, but those inputs have become 20% more expensive: ₹60 × 1.20 = ₹72
So, at current prices, its nominal GVA is: ₹129.60 − ₹72 = ₹57.60
At first glance, we might compare ₹57.60 with ₹40 and conclude that GVA has grown by 44%. But that figure reflects both the increase in production and the changes in prices.
So how do we separate the two? This is where the distinction between single deflation and double deflation becomes important.
Under the single-deflation approach used earlier, nominal GVA was adjusted using a common price index, such as the Wholesale Price Index (WPI), to arrive at real GVA. The problem is that the prices of what an industry produces and the prices of what it uses as inputs do not necessarily move together.
In our example, output prices have risen by 8%, while input prices have risen by 20%. Applying one common price adjustment to GVA would therefore treat two very different price movements as though they were the same.
The limitation becomes more apparent with WPI, which primarily covers goods, is significantly influenced by commodity prices, and does not cover services.
This is where double deflation comes in. Instead of applying one common price adjustment to GVA, double deflation adjusts output and inputs separately.
We first remove the 8% increase in the price of the factory’s output:
Real output = ₹129.60 ÷ 1.08 = ₹120
We then remove the 20% increase in the price of its inputs:
Real inputs = ₹72 ÷ 1.20 = ₹60
Only now do we calculate real GVA:
Real GVA = Real output − Real inputs = ₹120 − ₹60 = ₹60
So, in our example:
Nominal GVA = ₹57.60 and Real GVA = ₹60
The result may initially seem counter-intuitive: real GVA has grown faster than nominal GVA, even though nominal GVA includes price changes. But that is precisely because GVA is the difference between output and inputs. The factory’s output prices rose by only 8%, while its input prices rose by 20%. The faster rise in input prices squeezed its nominal value added.
By adjusting the two sides separately, double deflation gives us a way to distinguish changes in prices from changes in the underlying volume of economic activity. India’s move toward double deflation is also in line with IMF recommendations.
By now, we have answered quite a few questions. But there is one final question that a curious reader, and hopefully you, might ask.
How do statisticians actually know that output prices rose by 8% while input prices rose by 20%?
This is where price indices such as the Producer Price Index, or PPI, come in.
A price index is essentially a way of tracking how the prices of a particular set of goods or services change over time. It helps statisticians separate a change in the rupee value of something from a change in its actual volume or quantity.
The Output PPI measures the prices received by producers for the goods and services they sell. In our furniture factory, it can therefore capture the change in the price received for its tables.
The Input PPI, meanwhile, measures the prices producers pay for the inputs they purchase. In our example, it can capture the change in the prices of wood, energy and other inputs used by the factory.
The two measures are therefore designed to capture two different sides of the production process:
There is one more piece to the puzzle. The economy does not produce only physical goods. A large part of economic activity comes from services, such as banking, insurance, telecommunications, transport and other service activities. Since the WPI basket primarily covers goods, it cannot by itself capture the price movements of these services.
This is why the government is also planning to develop price measures for services, along the lines of approaches used in the US and European Union, so that changes in service prices can be measured more accurately. The initial framework covers areas such as banking, insurance, securities, telecom, air transport and railways, with a Banking Services Producer Price Index among the measures being introduced first, which will cover services such as deposit-taking, lending and other banking activities where banks charge fees or commission
So, the broader idea is not simply to find one price index for the entire economy. It is to use appropriate price information for different parts of the production process and different types of economic activity.
So, what do we take away from all this?
Perhaps the easiest thing to do in a debate like this is to pick a side. Calling someone an idiot is certainly easier than understanding a GDP deflator. Taking sides takes seconds; understanding why two GDP numbers differ takes patience and a little exercise for the mind.
But that is also what makes debates like this useful. They give us a chance to stumble into subjects we would probably never study on an ordinary Tuesday evening. A GDP debate can lead us to GVA, deflators, price indices, and, before we know it, we have spent a good amount of time thinking about how a furniture factory buys wood.
And perhaps that is not such a bad thing in this age of mindless doomscrolling and endless reels.
We certainly do not need to become economists. But we can ask better questions, learn enough to make sense of the numbers, and resist the temptation to turn every technical disagreement into a spectacle of shouting, interruptions and name-calling, like the ones we see on prime-time television channels.
So, read more, learn more, and try to understand the concepts behind the headlines.
For readers who want to explore the debate further, the following material is useful. We encourage you to read these along with the hyperlinks provided throughout this article for a deeper understanding of the arguments, methodology and context surrounding the latest GDP estimates.
1. New GDP series to fully capture gig economy: Mospi’s Saurabh Garg
2. Arguments over India’s GDP growth numbers, data & new methodology, CEA V Anantha Nageswaran joins in
3. MoSPI Secretary Saurabh Garg On 7.8% GDP | Rejects Data Manipulation Claims