The Great Indian Growth Puzzle, Decoding the Chasm Between GDP Numbers and Ground Reality

The Great Indian Growth Puzzle, Decoding the Chasm Between GDP Numbers and Ground Reality

In the high-stakes theater of global economics, India has consistently played the role of a star performer over the past decade. Official statistics have painted a picture of remarkable resilience, with Gross Domestic Product (GDP) growth rates often hovering around the enviable 7-8% mark, even as the world grappled with pandemic aftershocks and geopolitical turmoil. This narrative of a booming, unstoppable Indian economy has become a cornerstone of national policy and international investment discourse. However, a growing chorus of economists, analysts, and citizens is asking an uncomfortable but critical question: Why does this headline-grabbing growth feel so disconnected from the lived experiences of weak job markets, stagnant incomes, and struggling small businesses? The answer lies in a complex web of methodological shifts, statistical biases, and political economy, creating a profound puzzle that demands urgent resolution.

The genesis of this puzzle can be traced back to 2015, when India undertook a significant overhaul of its GDP calculation methodology. The base year was updated from 2004-05 to 2011-12, a routine and necessary step to reflect a more current economic structure. However, the changes went far beyond a simple base-year update. The new methodology incorporated massive amounts of data from the Ministry of Corporate Affairs’ MCA-21 database, shifted the primary measure from Gross Domestic Product at factor cost to GDP at market prices (which includes indirect taxes like excise duties), and began relying more heavily on the formal, corporate sector to estimate the performance of the entire economy.

While intended to modernize India’s statistical system, these changes inadvertently introduced several distortions that have clouded the true picture of economic health.

The Methodological Fault Lines

1. The Corporate Bias and the Invisible Informal Sector:
India’s economy is a dualistic giant. Alongside a dynamic, globally competitive formal sector exists a vast, sprawling informal economy that employs over 80% of the workforce and contributes nearly half of the total output. This informal sector—comprising small workshops, household enterprises, and street vendors—is notoriously difficult to measure. The pre-2015 methodology relied heavily on established surveys like the Index of Industrial Production (IIP) and the Annual Survey of Industries (ASI), which had mechanisms to capture this informal activity.

The shift to the MCA-21 database marked a fundamental pivot towards corporate data. While this data is more standardized and frequent, it systematically under-weights the informal sector. When a small, unregistered firm struggles or shuts down, its decline may not be captured in the corporate data. Conversely, when a large formal firm gains market share from informal players, it shows up as growth in the GDP numbers, even if the overall economic pie remains the same or shrinks. This creates a “formalisation bias,” where the GDP growth rate reflects the expansion of the formal sector rather than the health of the entire economy.

2. The “Single Deflation” Problem:
A technically complex but critically important issue is how India accounts for inflation in its GDP calculation. India primarily uses the “single deflation” method. This means it estimates the value of output in a sector at current prices and then deflates it using a single price index (like the Wholesale Price Index) for that sector’s output to arrive at “real” growth.

The international best practice, followed by most OECD countries, is “double deflation.” This method separately deflates the value of outputs and the cost of inputs. The difference is crucial. Consider a car manufacturer facing a sharp rise in steel and oil prices (inputs). If the company cannot fully pass these costs onto the consumer (output prices), its profit margins are squeezed, and its real value-added growth is low or negative. The single deflation method, by using only the output price, can overstate real growth in such a scenario. It fails to accurately capture the profit squeeze that occurs during periods of high input cost inflation, precisely the situation India has often faced with volatile global commodity prices.

3. The Divergence from High-Frequency Indicators:
The most compelling argument for skepticism comes from the glaring disconnect between the high GDP growth rates and a suite of high-frequency, real-world indicators. A seminal 2019 paper by former Chief Economic Adviser Arvind Subramanian presented forensic evidence of this divergence. He demonstrated that in the period following the methodological changes, the correlation between GDP growth and indicators like electricity consumption, two-wheeler sales, airline passenger traffic, and index of industrial production broke down dramatically.

These indicators, which had historically moved in lockstep with GDP, began telling a far more subdued story. Subramanian concluded that between FY2012 and FY2017, India’s average annual growth was likely overestimated by about 2.5 percentage points. In other words, the celebrated 7% average growth was, in reality, closer to 4.5%. This analysis resonates with public sentiment; robust growth should logically translate into more power consumed, more vehicles sold, and more goods transported, a chain of evidence that appears weak in the current context.

The Persistence of High Growth Numbers: A Confluence of Factors

Given these methodological issues and weak correlating indicators, how do the high growth estimates persist? Several factors contribute to this phenomenon.

• The Base Effect and Deflator Dynamics: Nominal GDP (calculated at current prices) can be inflated by factors like high indirect taxes or general inflation. If the deflators used to convert this to “real” GDP underestimate the true rate of inflation, it artificially boosts the real growth number. This has been a recurring concern, particularly when input cost inflation is not fully reflected in the output price indices used for deflation.

• The Consumption and Government Spending Engine: Even when private investment and manufacturing are weak, other components of GDP can prop up the numbers. Consistent government spending on infrastructure, subsidies, and welfare schemes directly adds to GDP. Similarly, consumption, often fueled by government transfers, tax cuts, and easy credit, can keep the growth figure aloft. The economy may be growing on the back of state-led demand and consumption, masking weakness in private capital formation—the true bedrock of long-term, sustainable growth.

• Revised Weights and the Services Sector: The 2015 rebasing increased the weight of the services sector, which is generally easier to measure through formal channels like corporate data. The rapid growth of the digital economy, IT services, and financial services is captured efficiently, potentially overshadowing the struggles in more traditional, employment-intensive sectors like manufacturing and small-scale trade.

• Statistical Smoothing and Political Economy: Early GDP estimates are frequently revised. Critics argue that there is a tendency for upward revisions over time, and the initial, widely reported figures often present the most optimistic picture. In any large democracy, there is an inherent political incentive to showcase strong economic performance, which can create subtle pressures on the presentation and timing of statistical releases.

The Policy Conundrum and the Path to Credibility

The reliance on potentially overstated growth figures creates a dangerous policy conundrum. If policymakers believe the economy is growing robustly at 7-8%, their focus may remain on managing inflation and fiscal deficits. However, if the actual, underlying growth is significantly lower, the economy may desperately need stimulus and targeted support to combat unemployment and underinvestment. The recent policy push to boost consumption, while politically popular and a quick way to stimulate demand, may be treating a symptom rather than the disease. The core ailment may be a lack of productive capacity and weak private investment, which consumption-led growth cannot fix in the long run.

To restore trust and ensure that policy is based on a stubborn truth rather than pliable statistics, India must undertake a series of robust reforms to its statistical system:

1. Embrace Double Deflation: India must begin the transition to the double deflation method, particularly for the manufacturing sector, to accurately capture value-added growth and eliminate the inflation-driven overestimation bias.

2. Revamp Informal Sector Measurement: The statistical machinery must be strengthened to conduct more frequent and comprehensive surveys of the unorganized sector, households, and small enterprises. Leveraging digitization and big data can help create a more realistic picture of this vital part of the economy.

3. Integrate Real-Time Indicators: Official GDP estimates should be cross-validated against a composite index of high-frequency indicators like electricity consumption, GST e-way bills, freight traffic, digital transactions, and employment data from periodic surveys. Significant divergences should trigger a thorough review and public explanation.

4. Ensure Methodological Transparency and Institutional Independence: Full methodological details, including all underlying data and assumptions, should be published to allow for independent verification. Most importantly, India must follow the best practices of countries like the UK and Canada by legally safeguarding the independence of its national statistical organizations, insulating them from any political or administrative pressure.

Conclusion: Growth for What?

The ultimate test of economic growth is not its statistical magnitude but its quality and impact. Is India’s growth generating sufficient, high-quality employment? Is it reducing inequality and improving living standards for the majority? Is it strengthening the country’s manufacturing base and export competitiveness? If the growth is primarily a statistical artifact, driven by a formalisation bias and state-led consumption, its durability is questionable, and the vision of Viksit Bharat (Developed India) will remain a distant mirage.

India is undoubtedly a nation on the move, with immense potential and a vibrant entrepreneurial spirit. However, to truly harness this potential, it needs an economic compass that points true north. By reforming its statistical system to produce data that is as robust and credible as its aspirations, India can ensure that its policies are targeted, its progress is real, and its dream of inclusive development is built on the solid foundation of fact, not the shifting sands of statistical illusion.

Q&A: Unpacking India’s GDP Debate

1. What was the core change in India’s GDP calculation method in 2015, and why is it controversial?

The core changes in 2015 included shifting the base year to 2011-12 and, more significantly, moving the primary data source from established surveys of industries (IIP, ASI) to the corporate-facing MCA-21 database. It also changed the main measure from GDP at factor cost to GDP at market prices. The controversy stems from the “corporate bias” this introduced. By relying more on large, formal companies, the new method likely undercounts the vast informal sector (employing 80% of the workforce). This means GDP growth may now be tracking the expansion of the formal sector rather than the health of the entire economy, making the numbers look stronger than the ground reality might suggest.

2. What is the difference between “single deflation” and “double deflation,” and why does it matter?

• Single Deflation: India’s current method. It calculates the value of a sector’s output at current prices and then uses a single price index (for outputs) to convert it into “real” growth.

• Double Deflation: International best practice. It separately adjusts (deflates) the value of a sector’s outputs and the cost of its inputs using different price indices.

Why it matters: Double deflation is more accurate. Imagine a car maker facing soaring steel costs (input) but unable to raise car prices (output) much. Its real profits are squeezed. Single deflation might still show healthy growth, but double deflation would accurately capture the profit squeeze, showing lower or even negative real value-added growth. India’s method can thus overstate growth during periods of high input cost inflation.

3. Former CEA Arvind Subramanian argued that growth was overestimated by 2.5 percentage points. What evidence did he use?

Subramanian’s evidence was based on a breakdown of the historical correlation between GDP growth and a set of high-frequency, real-world indicators that are harder to manipulate. He showed that before the 2015 methodological change, metrics like electricity consumption, two-wheeler sales, airline passenger traffic, and index of industrial production moved closely in sync with GDP. After the change, this correlation collapsed. The GDP numbers showed high growth, but these indicators told a much weaker story. Since it’s improbable that a genuinely booming economy would see stagnant electricity use or vehicle sales, he concluded the GDP numbers were likely overstating the actual growth.

4. If key sectors like manufacturing and investment are weak, what is driving the high GDP growth numbers according to the article?

The high numbers are likely being driven by a combination of factors:

• Government Spending: Significant public expenditure on infrastructure, subsidies, and welfare schemes directly adds to GDP.

• Consumption: Demand fueled by government transfers, tax cuts, and easy credit can keep the GDP figure high, even if the underlying capacity for investment is weak.

• Formalisation Bias: The growth of large formal companies, captured efficiently by new data methods, may be offsetting the uncaptured decline in the informal sector.

• Deflator Issues: If the inflation adjusters (deflators) are too low, they artificially inflate the “real” growth number.

5. What are the key steps India can take to improve the credibility of its economic data?

To restore trust, India should:

• Methodological Reform: Begin adopting double deflation, especially for manufacturing.

• Better Informal Sector Tracking: Conduct more frequent and comprehensive surveys of households and small businesses to accurately capture the informal economy.

• Use Real-Time Data: Integrate high-frequency indicators (e.g., power demand, GST collections, freight data) into the estimation and validation process.

• Ensure Transparency and Independence: Publish full methodological details for independent verification and legally safeguard the autonomy of statistical bodies like the National Statistical Commission (NSC) to prevent political interference.