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MAKING SENSE OF IMF RATING AND GDP DATA

Mint Mumbai

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December 03, 2025

India's Q2 growth surpassed expectations, but the IMF rated GDP data quality a 'C'. While India is addressing many of the issues, it's a reminder that the country cannot afford long gaps in statistical improvements.

- BY PRAGYA SRIVASTAVA & MANJUL PAUL

India's GDP data has always flummoxed experts. Last week, the news of a 'C' rating by the International Monetary Fund (IMF), highlighting shortcomings in the data, baffled the public at large, who were unsure what to make of an impressive 8.2% growth performance in the July-September quarter. While the GDP data issues are not new, what's new is the rating system by the IMF-started last year-that has brought to public attention what experts have been saying for close to a decade.

A'C' rating means India's GDP data has "some shortcomings that somewhat hamper surveillance".

To begin with, the fundamental issue is the use of a base year from over a decade ago, that is 2011-12. Besides that, opacity in the use of deflators to calculate growth after stripping the effect of inflation, huge mismatches in estimates between the production and expenditure approaches, and inadequate coverage of the informal sector are other issues.

These have made India and China only two prominent countries with high-growth track records, but inadequate or suspect data. Even the up-and-coming Vietnam, which has a poor statistical record, scored an 'A' on GDP data quality.

LATEST IMPACT

The issues highlighted by the IMF have long plagued the GDP numbers, leading to official growth numbers sometimes out of sync with broader expectations.

This also played out in the last quarter, making 8.2% growth in Q2 a less reliable indicator of underlying economic activity Many economists shed light on the 8.7% growth in nominal GDP, down from 8.8% in the April-June quarter and 10.8% in the January-March quarter. The main identifiable culprits behind real GDP growth surging despite slow nominal rise were deflator issues and high discrepancies in data.

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