IMF rating of National Accounts Statistics has raised concerns about the country’s macroeconomic outlook. IMF has assigned the Second-Lowest ‘C’ Rating to India’s NAS and Inflation Data. Read here to know the significance and implications of the rating.

The International Monetary Fund (IMF) has assigned a ‘C’ rating to India’s National Accounts Statistics (NAS) and inflation data, indicating that the country’s macroeconomic datasets have shortcomings that somewhat hamper effective surveillance.

This is the second-lowest grade, raising concerns about data quality, transparency, and methodological modernisation.

Table of Contents

What Does the IMF Rating Signify?

The IMF grades countries on a four-point scale from A to D, assessing the quality, transparency, timeliness, and methodological rigour of national data.

A: Data adequate for economic surveillance
B: Some shortcomings, but broadly adequate
C: Shortcomings that somewhat hamper surveillance
D: Serious shortcomings that significantly hamper surveillance

IMF rating ‘C’ indicates that while the country publishes large volumes of data, methodological and structural issues limit its reliability, especially for short-term analysis.

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For a US$3.7 trillion economy seeking to attract global investment, this matters. Investors, rating agencies, and international organisations increasingly rely on timely, high-frequency, and methodologically sound data for assessing economic health.

Why did India receive a ‘C’ IMF rating?

The IMF highlights several areas where India’s statistical infrastructure urgently needs modernisation.

Outdated Base Year for GDP and CPI Calculations

One of the most pressing concerns is India’s continued use of 2011-12 as the base year for calculating GDP, GVA, CPI, and WPI. Over the last 13 years, the Indian economy has undergone dramatic restructuring:

Digital and platform-based sectors have expanded
Renewable energy, electric mobility, and fintech sectors have risen rapidly
Consumption patterns have shifted with rising urbanisation and digital lifestyles
MSMEs have evolved in structure and scale

An outdated base year means the price structure and consumption basket no longer represent the economy’s current reality.

This can lead to over- or underestimation of both growth and inflation, potentially distorting policy decisions.

Many advanced and emerging economies revise their base years every five years or adopt continuous chain-weighting methods; India’s pace lags this global practice.

Insufficient Use of Updated Household and Labour Surveys

The IMF notes that India’s national accounts require greater integration of modern data sources, including:

Household Consumption and Expenditure Survey (HCES)
Periodic Labour Force Survey (PLFS)
GSTN datasets
Digital administrative records
Real-time industry data

Using outdated or incomplete surveys means the informal sector, which employs nearly half of India’s workforce, may be poorly represented in national accounts.

The pandemic amplified structural changes, digitisation, gig work, shifts in rural–urban consumption, but the existing datasets capture these trends only partially.

Lack of Seasonally Adjusted Data for Quarterly GDP

India does not publish seasonally adjusted quarterly GDP data. This is a major limitation because the economy has predictable seasonal patterns, festive consumption spikes, monsoon-driven agricultural cycles, and financial-year-end effects.

Without seasonal adjustment:

Quarter-on-quarter comparisons can be misleading
Short-term economic turning points are harder to detect
Investors and policymakers cannot reliably gauge real momentum

Most major economies, including China, Brazil, South Africa, and OECD nations, publish seasonally adjusted data, making India an outlier among G20 peers.

Outdated Statistical Techniques for Quarterly National Accounts

The IMF flags the need for modernising the compilation of India’s quarterly national accounts. Many estimates rely on:

Extrapolated trends
Partial administrative datasets
Legacy ratios from older surveys

This approach introduces lags and revisions and reduces the sensitivity of GDP calculations to real economic shifts.

India’s quarterly GDP numbers often undergo significant revisions, sometimes years later, indicating methodological limitations.

Over-Reliance on Wholesale Price Index (WPI) for “Single Deflation”

India relies heavily on WPI for deflating nominal GDP to obtain real estimates in certain sectors. However, WPI is:

Goods-centric
Weakly representative of services
Not reflective of producer-specific price changes

The absence of a full Producer Price Index (PPI) forces the system to depend on WPI, which can introduce cyclical biases.

For instance, in years of commodity volatility, WPI movements may distort estimates of manufacturing GVA, masking actual productivity trends.

Outdated CPI Basket and Weights

India’s CPI also uses the 2011–12 consumption basket. Since then:

Healthcare costs have risen
Digital services and telecom consumption have exploded
Education and housing patterns have changed
Food habits have diversified

An outdated basket leads to inaccurate inflation measurement, which has consequences for:

RBI’s inflation targeting
Wage indexation
Poverty estimation
Real income analysis

The IMF notes that timely CPI updates are critical for maintaining data credibility.

IMF’s Recommendations for Data Modernisation

To improve India’s rating, the IMF recommends a comprehensive overhaul of the statistical architecture. Key suggestions include:

Conduct Regular Benchmark Revisions: India should adopt a five-year revision cycle for GDP, CPI, and WPI base years. This aligns with global best practices followed by the EU, US, Japan, and Australia.
Introduce a Producer Price Index (PPI): A robust PPI is essential for accurately measuring factory-gate inflation and ensuring better deflators for national accounts. It also helps businesses and policymakers track input costs more effectively.
Scale Up Use of Administrative and Digital Datasets: The IMF encourages India to integrate:

GSTN invoices
Corporate filings
Digital payments data
Satellite and geospatial imagery
E-commerce transaction records

Publish Seasonally Adjusted GDP and High-Frequency Indicators: This would bring India in line with global standards and improve the interpretability of short-term trends.
Modernise Statistical Techniques: The IMF recommends adopting:

Chain-linked volume measures
Contemporary sampling frameworks
Automated data validation tools
Big-data analytics

These tools are essential for capturing an economy increasingly driven by technology, services, and innovation.

Why Does Data Quality Matter?

A ‘C’ rating is not just about statistical integrity; it has broader implications.

Monetary and Fiscal Policy: Accurate inflation and output data are critical for decisions on interest rates, taxes, subsidies, and welfare spending.
Investor Confidence: Global investors rely on consistent and transparent data. Weak data credibility can raise risk premiums and reduce foreign investment appetite.
International Comparisons: Data quality affects India’s ranking in competitiveness, development indices, and global research studies.
Evidence-Based Policymaking: Poor quality data leads to misallocation of resources, ineffective welfare targeting, and flawed economic strategy.
Credibility as a Global Economic Leader: As India seeks a larger role in the G20 and global economic governance, strong data systems are indispensable.

Conclusion

India has already initiated some efforts. The Ministry of Statistics and Programme Implementation (MoSPI) is working on revising the base year, developing a PPI, and building integrated data platforms. Yet the pace remains slower than global expectations for an economy of India’s scale.

The IMF’s ‘C’ rating should be seen not as criticism but as an opportunity. Modernising India’s statistical architecture will not only enhance international credibility but also improve policymaking, drive investment, and support inclusive growth.

A robust, transparent, and modern data ecosystem is essential for India to fully leverage its economic potential in the decades ahead. The goal should be clear: move from a ‘C’ to an ‘A’ rating and become a global exemplar in economic data governance.