Public scrutiny of official statistics is a vital feature of any democracy, especially when those numbers shape perceptions of growth, welfare, and policy success. In India, criticism of national accounts intensified after the base year was revised to 2011–12. As the country prepares for another round of base and methodological changes, it becomes important to distinguish between substantive critique and noise driven by resistance to change or selective interpretation of data.
Four Broad Strands of Criticism
Criticisms of India’s national accounts broadly fall into four categories. The first resists methodological change and loss of comparability with older series. The second selectively uses indicators to support prior beliefs. The third alleges systematic bias and imputes political motives. The fourth acknowledges the complexity of measuring a large, heterogeneous, and rapidly evolving economy, while offering feasible improvements. Only this last category meaningfully advances the debate, though the first three have dominated public discourse.
Status Quo Bias and Resistance to Change
The first set of criticisms is rooted in familiarity with older methods. However, economic change inevitably requires statistical adaptation. The 2011–12 rebasing aligned India with the United Nations System of National Accounts (SNA) 2008, shifting the unit of analysis from factories to enterprises. This necessitated moving from a small RBI sample of around 2,500 firms to the Ministry of Corporate Affairs’ MCA-21 database, covering several lakh companies.
This transition was criticised on the grounds that the MCA database included inactive or “dummy” firms. Yet, relying on a tiny and unrepresentative sample was no longer viable in an economy with nearly two million active firms. Over time, statutory filing requirements and data cleaning improved the quality of MCA data.
Similar resistance emerged when India replaced the five-yearly employment survey with the quarterly Periodic Labour Force Survey (PLFS). Critics argued that detail was lost, but higher-frequency data proved invaluable for macroeconomic management and has since enabled far richer labour market analysis than before. Ironically, some of the same critics now oppose further moves towards monthly labour data.
Selective Use of Indicators
GDP estimation is complex in any country, more so in one as diverse as India. Yet, data issues are often highlighted only when official growth estimates are high or exceed forecasts, suggesting confirmation bias. If certain data configurations imply overestimation, the opposite configurations would imply underestimation—but these are rarely emphasised.
For example, the absence of double deflation in manufacturing was cited as overstating growth when wholesale price inflation (WPI) was below consumer price inflation (CPI). However, during 2021 and 2022, WPI exceeded CPI, implying that growth may have been underestimated in those years. Similar selectivity appears in arguments using bank credit growth as a “smell test”: weak credit growth in the 2010s was used to question GDP estimates, but strong post-pandemic credit growth alongside high GDP was not treated as corroboration.
Discrepancies between production- and expenditure-side GDP estimates are also selectively highlighted. Positive discrepancies are portrayed as evidence of overestimation, while negative discrepancies—which imply underestimation—are often ignored.
Informal Sector and Measurement Challenges
A persistent argument is that post-pandemic growth is overstated because formal-sector indicators are used to estimate informal-sector output, which may have shrunk. However, this critique often overlooks methodological changes already incorporated in the 2011–12 base revision. Output in unorganised manufacturing is now estimated using skill-weighted labour inputs, rather than assuming uniform productivity per worker.
The production approach remains the controlling total in national accounts, with the expenditure approach serving as a cross-check using independent commodity flow data. Discrepancies between the two fluctuate in both directions, suggesting the absence of systematic bias. In fact, cumulative discrepancies from FY21 to FY26 have been negative, implying possible underestimation of production.
Moreover, household consumption—where informal sector activity is most visible—is estimated as a residual after accounting for government and corporate expenditure, making underestimation more likely than overestimation.
The IMF Grade and Its Interpretation
India’s national accounts received a “C” grade from the , often cited to discredit GDP estimates. However, this grade largely reflected delays in rebasing rather than alleged manipulation. On inflation measurement—central to arguments about overstated real growth—India received a higher grade, weakening claims that growth is systematically inflated. Emerging economies face structural constraints in data collection, and even China received a similar grade.
Constructive Criticism and Ongoing Reforms
Valid criticisms are already being addressed. These include updating the base year, expanding the use of high-frequency surveys, incorporating more administrative datasets, and introducing double deflation where granular price indices are available. Regular surveys of unincorporated enterprises are replacing older proxy-based methods in services. Methodological changes are transparently communicated, with feedback invited.
As expenditure-side data improves, GDP discrepancies—which have ranged between +3.3% and –4.1% of GDP since 2020–21—are expected to narrow. Since revisions are likely to correct both over- and under-estimation, large changes to overall growth trends are unlikely.
Separating Critique from Cynicism
India’s statistical system operates under challenging conditions: rapid structural change, frequent external shocks, and vast heterogeneity. Constructive criticism that recognises these constraints is essential. What undermines public trust, however, is the tendency to impugn the integrity of professional statisticians to score political points. Debate should focus on improving measurement, not delegitimising institutions.
What to Note for Prelims?
- India shifted to SNA 2008 with the 2011–12 base revision.
- MCA-21 database expanded coverage of corporate sector.
- PLFS provides high-frequency labour market data.
- GDP is estimated using production and expenditure approaches.
What to Note for Mains?
- Challenges of GDP measurement in a large informal economy.
- Role of base-year revisions and methodological updates.
- Limits of proxy indicators like credit growth.
- Importance of constructive critique for statistical credibility.
