A new academic study is reviving a long-running debate about the accuracy of India’s economic growth statistics, suggesting that the country’s GDP may have been misestimated for nearly two decades.
The working paper, “India’s 20 Years of GDP Misestimation: New Evidence,” by Abhishek Anand of the Madras Institute of Development Studies, Josh Felman of JH Consulting, and Arvind Subramanian of the Peterson Institute for International Economics, argues that India’s economic growth was likely underestimated during the boom years of the mid-2000s and subsequently overestimated during the following decade.
According to the authors, India’s annual growth between 2005 and 2011 may have been understated by roughly 1 to 1.5 percentage points, while growth between 2012 and 2023 may have been overstated by about 1.5 to 2 percentage points.
If these adjustments are applied, the picture of India’s economic trajectory changes significantly.
Rather than experiencing relatively steady high growth over the past two decades, the economy appears to have followed a different pattern: a strong boom in the mid-2000s followed by a period of slower—but still respectable—growth.
The study estimates that between 2011 and 2023, the Indian economy likely expanded at around 4–4.5 percent annually, compared with the roughly 6 percent average growth suggested by official statistics.
Long-running controversy
Questions about India’s GDP data have circulated among economists for years, particularly after the government introduced a new methodology for calculating national income in 2015. Critics argued that the revised numbers sometimes appeared inconsistent with other economic indicators such as exports, credit growth, electricity consumption, tax revenues, and industrial production.
The new paper attempts to rigorously evaluate those concerns by comparing official GDP estimates with a range of macroeconomic indicators and by examining the methodology used to calculate the data.
The authors note that skepticism about the numbers emerged partly because the GDP statistics painted a picture of consistently strong growth even during periods when other indicators suggested economic weakness.
“GDP numbers suggested that growth remained strong,” the authors write, even as the economy faced a series of shocks, including the global financial crisis, India’s domestic banking crisis known as the “twin balance sheet” problem, the 2016 demonetization shock, the rollout of the Goods and Services Tax, and the economic disruption caused by the COVID-19 pandemic.
The researchers identify two major methodological issues that they say contributed to the misestimation.
The first involves the way India’s informal sector—which accounts for a large share of economic activity—is measured. In the national accounts framework introduced in 2015, the performance of the informal sector was often estimated using data from the formal corporate sector.
This approach assumes that trends in the organized sector mirror those in the vast informal economy. But the authors argue that this assumption broke down after 2015, when several policy and economic shocks disproportionately affected small businesses and informal enterprises.
Demonetization in 2016 disrupted cash-based economic activity. The introduction of the nationwide Goods and Services Tax created compliance challenges for smaller firms. And the COVID-19 pandemic hit informal workers particularly hard.
Because formal sector firms were more resilient during these shocks, using them as proxies for the informal sector likely overstated overall economic performance, the paper argues.
The second issue relates to price deflators, which are used to convert nominal economic activity into real growth figures.
In many sectors, the deflators were based heavily on commodity prices, especially oil. When commodity prices declined sharply, these deflators fell as well, mechanically boosting measured real growth even if actual output did not increase proportionately.
This methodological choice, the authors argue, caused real GDP growth to be overstated during periods when commodity prices were falling.
A boom followed by slower growth
After adjusting for these methodological issues, the authors conclude that India’s economic trajectory looks different from the one presented by official statistics.
Instead of steady high growth over two decades, the adjusted data suggest that India experienced a clear boom between 2005 and 2011, followed by a slowdown beginning in the early 2010s.
Even so, the authors emphasize that the revised growth estimates still depict a reasonably strong economic performance by global standards.
Growth after 2011, although slower than official numbers suggest, remained robust compared with many emerging and advanced economies.
Why accurate GDP numbers matter
The paper also highlights why accurate national income statistics are critical for economic policymaking.
GDP data are used by governments, businesses, and central banks to guide decisions on fiscal policy, investment, and interest rates. If growth is overstated, policymakers may underestimate economic weakness and fail to respond appropriately.
Conversely, underestimating growth could lead to overly cautious policies.
“If the GDP numbers suggest that growth is strong when it is actually weak, businesses are liable to misinvest, households to overspend, and the central bank to maintain an excessively tight monetary policy,” the authors note.
The debate over India’s GDP data has intensified periodically since the methodology change in 2015, with economists both inside and outside India questioning aspects of the statistical framework.
The authors note that recent methodological revisions and consultations by Indian statistical authorities aim to address some of the concerns raised in the study.
However, they caution that it will take time to assess whether the new revisions fully resolve the measurement challenges.
The broader lesson, they argue, is that measuring economic activity in a large, complex, and partly informal economy like India’s is inherently difficult.
But improving those measurements is essential—not only for academic analysis but also for effective economic policymaking.
As the authors conclude, getting the numbers right matters because inaccurate data can distort perceptions of economic performance and lead to misguided policy choices.