India’s Economic Measuring Tape is Broken: What the IMF’s ‘C’ Grade Reveals and Why It Matters 

The International Monetary Fund (IMF) has reaffirmed its ‘C’ grade—the second-lowest rating—for India’s national accounts data, including GDP, indicating that methodological shortcomings significantly hamper economic surveillance. Key issues include reliance on an outdated 2011-12 base year, the use of wholesale price indices as deflators, and persistent discrepancies between production and expenditure GDP measurements, which collectively fail to adequately capture the vast informal sector and modern spending patterns. While inflation data received a slightly better ‘B’ grade, the Consumer Price Index also suffers from an outdated basket and weights.

The Indian government is working to address these gaps by updating base years and methodologies by 2026 and leveraging new data sources like digital payments, but these structural weaknesses in data quality continue to obscure the true picture of economic health, impacting policy accuracy, investment decisions, and inclusive development planning.

India’s Economic Measuring Tape is Broken: What the IMF’s ‘C’ Grade Reveals and Why It Matters 

Introduction: More Than Just a Grade 

In a quiet but devastating announcement that reverberated through financial circles, the International Monetary Fund (IMF) has once again given India’s national accounts statistics—the very foundation upon which economic policies are built—a ‘C’ grade, the second-lowest possible score. This assessment, buried within the IMF’s annual Article IV consultation report, reveals a troubling reality: India’s economic data suffers from “some shortcomings that somewhat hamper surveillance” just as the country positions itself as the world’s fastest-growing major economy . 

The timing could not be more significant. As India aspires to become a developed nation by 2047, the IMF’s critique strikes at the heart of its economic governance. The report highlights how outdated methodologies and an inadequately captured informal sector potentially distort the very picture of India’s economic health that policymakers, investors, and international institutions rely upon . This comprehensive analysis delves beyond the headline grade to explore what exactly ails India’s economic measurement systems, why accurately capturing the informal economy remains an elusive goal, and what steps are being taken to bridge these critical gaps. 

The IMF Report Card: Decoding the ‘C’ Grade 

The IMF’s data quality assessment uses a straightforward four-tier grading system: A, B, C, and D. India’s overall ‘B’ grade across all data categories might suggest adequacy, but the ‘C’ specifically assigned to its national accounts statistics—which include crucial indicators like Gross Domestic Product (GDP) and Gross Value Added (GVA)—signals serious methodological concerns . 

According to the IMF, a ‘C’ rating indicates that data “have some shortcomings that somewhat hamper surveillance” . This evaluation isn’t merely academic; it has real-world implications for how international investors, credit rating agencies, and global economic institutions perceive the reliability of Indian economic data. While the IMF acknowledged that India’s national accounts data are available at “adequate frequency and timeliness,” it pointed to fundamental flaws in how this information is compiled and processed . 

Table: IMF Grades for India’s Economic Data (2025 Article IV Consultation) 

Notably, this isn’t the first time India has received this mediocre grade for its national accounts—the same ‘C’ rating was assigned in the previous year’s assessment, indicating that data weaknesses have persisted without significant improvement . The IMF did acknowledge that plans to upgrade real sector statistics “are advancing,” but the pace of progress appears insufficient to address the fundamental concerns raised . 

Methodological Weaknesses: The Technical Fault Lines 

Outdated Base Year and Deflator Issues 

At the heart of the IMF’s criticism lies India’s continued reliance on the 2011-12 base year for calculating its national accounts . In the rapidly evolving landscape of the Indian economy, using a base year from over a decade ago means that the structure and relative importance of various sectors no longer reflect contemporary economic realities. New industries have emerged, consumption patterns have transformed, and production technologies have advanced—none of which are adequately captured in the current measurement framework. 

Compounding this issue is India’s use of wholesale price indices (WPI) as deflators in the absence of producer price indices (PPI) . Deflators are essential statistical tools used to adjust nominal values to real terms by removing inflation effects. The problem with WPI is that it primarily tracks prices at the wholesale level, potentially introducing what the IMF calls “cyclical biases” in measurements of economic growth . This methodological compromise becomes particularly significant in sectors where wholesale and retail price movements diverge substantially. 

Measurement Discrepancies and Informal Sector Gaps 

One of the most telling criticisms in the IMF report concerns the “sizeable discrepancies” that periodically emerge between the production and expenditure approaches to measuring GDP . In theory, these two methods should yield similar results since they’re measuring the same economic activity from different angles. The production approach calculates GDP by summing the value added by all producers, while the expenditure approach aggregates all spending on final goods and services. 

The persistent gap between these measurements, according to the IMF, “may indicate the need to enhance the coverage of the expenditure approach data and the informal sector” . This discrepancy suggests that significant portions of economic activity—particularly in the vast informal sector—may be slipping through the statistical cracks, potentially distorting the overall picture of economic growth. 

Technical Shortcomings 

The IMF also highlighted several technical deficiencies in India’s economic data compilation: 

• Lack of seasonally adjusted data: Without seasonal adjustment, it becomes difficult to distinguish underlying economic trends from normal seasonal patterns, complicating quarter-to-quarter comparisons . 

• Insufficient granularity: The IMF called for “further breakdown of Gross Fixed Capital Formation by institutional sector” and “further disaggregation of the quarterly production and expenditure approach estimates” to enable more detailed analysis of economic trends . 

• Publication lags: Critical data, including institutional breakdowns of investment, are published “with a significant lag,” reducing their utility for real-time policy assessment and decision-making . 

The Informal Economy: India’s Statistical Blind Spot 

Scale and Measurement Challenges 

The informal economy represents both a crucial component of India’s economic landscape and its most significant statistical challenge. According to India’s Ministry of Statistics and Programme Implementation (MoSPI), the informal sector contributed about 45% to the country’s total GDP in FY 2022-23 . From a labor perspective, approximately 61% of women workers in the non-agriculture sector work in informal sector enterprises, as per the Periodic Labour Force Survey in 2023-24 . 

The fundamental challenge in measuring informal economic activity lies in its very nature: unregistered, unregulated, and often undocumented . As one commentary notes, the informal sector “encompasses a diverse range of economic activities, including street vending, small-scale manufacturing, agriculture, and services, among others” . This heterogeneity, combined with the absence of reliable administrative records, makes traditional data collection methods inadequate. 

The Digital Transformation and Evolving Informality 

The nature of informality itself is evolving with digital penetration creating new forms of informal work. As Sanjeev Sanyal, Member of the PM Economic Advisory Council, noted in a recent MoSPI consultation, digital platforms have generated new categories of economic transactions involving “UPI payments, gig workers, social influencers, self-employment generated by digital intermediation platforms, yoga teaching” . These emerging forms of economic activity further complicate measurement efforts, as traditional surveys often fail to capture these digitally-enabled but informally-structured livelihoods. 

Gender Dimensions of Informal Work 

Nowhere are the limitations of current measurement frameworks more evident than in assessing women’s economic contributions, particularly in home-based work. An estimated 17.19 million women in India are engaged in home-based work, with 12.48 million involved in non-agricultural activities . These workers often remain statistically invisible, their economic contributions overlooked in official data. 

The challenges these workers face highlight the human dimension behind the statistical shortcomings. Women in home-based work earn approximately Rs. 24 per hour—half of what men earn (Rs. 48 per hour) and less than half of India’s recommended minimum wage of Rs. 46.88 per hour . In some sectors, the compensation is even more dismal—women peeling almonds are paid just Rs. 50 for cleaning 23 kg of almonds, a task requiring 12-16 hours of labor . 

Inflation Data: An Outdated Basket of Goods 

While India’s Consumer Price Index (CPI) received a higher ‘B’ grade from the IMF, it still suffers from significant methodological issues. The CPI scores well on frequency and timeliness, being published monthly with only a month’s lag . However, the IMF noted that the rating “reflects the outdated CPI base year, items basket, and weights (set in 2011-12), implying that the CPI basket likely fails to accurately represent current spending habits” . 

This outdated basket means that India’s official inflation measure likely overemphasizes commodities that were important to consumers over a decade ago while underweighting products and services that constitute significant expenditure shares today. The result is a potentially distorted picture of price pressures facing Indian households, with implications for monetary policy and social welfare programs. 

Government Response: Efforts to Modernize Economic Measurement 

Base Year Revision and New Data Initiatives 

Recognizing these limitations, the Indian government has initiated steps to modernize its statistical systems. The Ministry of Statistics and Programme Implementation (MoSPI) is currently working on updating the GDP and CPI base years from 2011-12 to 2022-23, with new series of both datasets expected to be released in early or mid-2026 . 

In January 2025, MoSPI organized a consultation on ‘Estimation of Informal Sector in GDP’ to broaden the methodology for capturing informal economic activity . The ministry is exploring enhanced use of administrative data sources like GST and digital payment systems and has begun preparation for a Statistical Business Register . Starting January 2025, monthly statistics on employment from the Periodic Labour Force Survey and quarterly estimates for the contribution of the unincorporated sector through the Survey of Unincorporated Sector Enterprises are expected to become available . 

Digital Innovation and Administrative Data 

The government’s approach increasingly recognizes the potential of digital payment systems and administrative records to fill gaps in traditional survey-based methodologies. As MoSPI Secretary Dr. Saurabh Garg highlighted, the ministry is examining databases like “PM Street Vendor’s AtmaNirbhar Nidhi (PM SVANidhi), Pehchan Cards to artisans (handicrafts), data on workers available with organizations such as Tea Board, Coffee Board, State Construction Boards, District Industry Centres” . These alternative data sources could provide more timely and comprehensive coverage of economic activities that have traditionally evaded formal measurement. 

Policy Interventions Addressing Informality 

Beyond measurement issues, the government has also implemented policy interventions to address challenges associated with informality: 

• The e-Shram Portal acts as a one-stop solution providing access to welfare schemes for over 300 million unorganized sector workers . 

• Pradhan Mantri Shram Yogi Maan-dhan (PM-SYM), a pension scheme for unorganized workers, aims to ensure old age protection . 

• Atal Pension Yojana has seen total gross enrolment cross 7 crore marks in October 2024 . 

• According to government claims, in the last 7 years, 7 crore people have transitioned to more secure, formal jobs as per EPFO records . 

Broader Implications: Why Accurate Data Matters 

Economic Planning and Policy Formulation 

The quality of economic statistics has profound implications for policy effectiveness. As the IMF noted, the shortcomings in India’s national accounts “somewhat hamper surveillance” . In practical terms, this means that policymakers may be making crucial decisions about interest rates, fiscal policies, and structural reforms based on an incomplete or potentially misleading picture of the economy. 

For instance, if growth estimates systematically undercount certain sectors due to methodological issues, resources may be misallocated, and policy priorities misjudged. Similarly, without accurate seasonally adjusted data, short-term economic management becomes more challenging, as it’s harder to distinguish underlying trends from seasonal fluctuations . 

Investment Decisions and Global Perception 

For international investors, reliable and comparable economic data is essential for assessing opportunities and risks. The IMF’s public critique, while measured in tone, potentially raises questions about the reliability of Indian economic indicators among global investment communities. This is particularly significant as India positions itself as an alternative to China in global supply chains and attracts foreign capital to fund its infrastructure and development needs. 

The World Economics organization, which also gives India’s GDP data quality a ‘C’ rating, notes that while the data is “adequate to illustrate the story of one of the world’s fastest growing economies over recent decades,” it still falls short of international standards . 

Social Welfare and Inclusive Development 

Perhaps most importantly, inaccurate economic measurement has profound implications for social welfare and inclusive development. When significant segments of the economy—particularly informal workers, women, and marginalized communities—remain statistically invisible, their needs and contributions risk being overlooked in policy design. 

The case of home-based workers exemplifies this concern. Without legal recognition and statistical visibility, these workers remain excluded from social security benefits, fair wage regulations, and basic labor protections . As one analysis notes, “The economic progress of a nation cannot only be understood in terms of its GDP. Healthy working conditions, living wages and legal protection as workers is something each” worker deserves . 

Conclusion: The Path Toward Statistical Integrity 

The IMF’s ‘C’ grade for India’s national accounts data serves as both a critique and an opportunity—a stark reminder of the statistical infrastructure gaps that need addressing as India aspires to become a global economic powerhouse. The challenges are significant: modernizing outdated methodologies, capturing the vast and evolving informal economy, and building statistical systems capable of measuring twenty-first-century economic activities. 

Yet, there are encouraging signs of progress. The government’s ongoing efforts to update base years, incorporate new data sources, and broaden methodological approaches demonstrate a recognition of these challenges. The increasing use of digital payment data, administrative records, and more frequent enterprise surveys represents a promising shift toward more timely and comprehensive economic measurement. 

The ultimate goal extends beyond improving grades in international assessments. It’s about building a statistical foundation capable of supporting evidence-based policies, enabling efficient resource allocation, and ensuring that all economic contributions—from the corporate boardroom to the home-based worker—receive appropriate recognition in the national economic narrative. 

As India continues its remarkable economic transformation, the parallel transformation of its statistical systems will be crucial not only for accurate measurement but for ensuring that the benefits of growth reach all segments of its society. The path to becoming a developed economy by 2047 requires not just economic growth but the statistical capability to measure it accurately, understand its distribution, and steer it toward inclusive ends.