Why Criticism of India’s GDP Data Needs More Care — and Less Noise

As India prepares for another round of base-year revision and methodological updates in its national accounts, debate over the credibility of official data has resurfaced. Such scrutiny is natural — even necessary — in a democracy. But not all criticism carries equal analytical weight. Since the 2011–12 base revision, critiques of India’s GDP and related statistics have broadly fallen into four categories. Understanding these distinctions is essential to separating legitimate concerns from rhetorical or politically motivated claims.

Why data revisions inevitably provoke controversy

Economic measurement is not static. As economies evolve — becoming more formalised, digitised, and diversified — statistical systems must adapt. India’s shift to the 2011–12 base year involved adopting the United Nations’ System of National Accounts (SNA) 2008, a global standard that required significant conceptual changes, including a move from a factory-based to an enterprise-based measurement of output.

Such transitions disrupt long data series, making historical comparisons harder. Resistance to change — often termed “status quoism” — is therefore common. Critics objected, for instance, to replacing the Reserve Bank of India’s limited corporate sample with the much larger Ministry of Corporate Affairs’ MCA-21 database, arguing that it contained inactive or “dummy” firms. But as India’s corporate universe expanded to nearly two million active firms, reliance on a small, unrepresentative sample became untenable. Over time, statutory filings improved and the database matured.

Status quoism versus statistical necessity

Similar resistance was seen when India moved from infrequent employment surveys to the quarterly Periodic Labour Force Survey (PLFS). Critics warned of loss of depth and comparability. In practice, higher-frequency labour data has strengthened macroeconomic analysis and policymaking, proving more reliable than the private estimates that once filled the gap. Yet calls to resist further upgrades — such as monthly indicators — continue, reflecting discomfort with change rather than methodological weakness.

Consumer expenditure surveys have faced comparable criticism, with improvements in methods said to break comparability with earlier rounds. But repeated surveys are precisely how statisticians test, verify, and benchmark new methods.

The problem of selective data use

A second category of critique involves selective use of indicators to support pre-existing conclusions. GDP estimation is complex everywhere, but in India such critiques often surface when growth exceeds expectations. Indicators that appear to signal overestimation are highlighted, while those suggesting underestimation are ignored.

For example, the absence of double deflation has been cited as inflating growth when wholesale price inflation (WPI) is below consumer price inflation (CPI). Yet during 2021–23, when WPI exceeded CPI, the same logic would imply growth was underestimated — a point rarely acknowledged. Similarly, weak credit growth during the 2010s was used as a “smell test” to question GDP numbers, but the argument disappeared when both credit and GDP rose post-pandemic.

Discrepancies do not automatically imply bias

Another frequent claim focuses on discrepancies between the production and expenditure approaches to GDP. Positive discrepancies are cited as evidence of overestimation, but negative discrepancies — which imply the opposite — receive less attention. Between Q1 FY21 and Q2 FY26, cumulative discrepancies were negative, suggesting underestimation of production rather than inflation of growth.

Household consumption, which captures much of the informal sector, is often measured residually because government and corporate expenditure data are more robust. This makes it more likely to be understated, not overstated.

Allegations of motivation and institutional credibility

A more serious — and weaker — line of criticism imputes political motivation or systematic bias to official statisticians. References are sometimes made to assessments by bodies such as the “International Monetary Fund”, which gave India a relatively low grade for national accounts. However, this assessment was driven largely by delays in rebasing, not by doubts about integrity. Comparable grades have been given to other large emerging economies facing similar measurement challenges.

Where criticism is both valid and useful

The most constructive critiques acknowledge the sheer complexity of measuring a vast, heterogeneous economy and focus on feasible improvements. Many such suggestions are already being implemented: more frequent surveys, use of additional datasets, planned double deflation where price indices permit, and regular surveys of unincorporated enterprises to reduce reliance on proxy ratios.

Rebasing to a more recent year, transparently communicating changes, and inviting feedback are all part of this process. As expenditure-side data improves, discrepancies are expected to narrow further.

What the debate should focus on going forward

Revisions that reduce both under- and over-estimation are unlikely to dramatically alter India’s growth trajectory. The real risk lies not in methodological change, but in eroding trust in professional statistical institutions through unfounded allegations. Constructive criticism — grounded in evidence and proportion — strengthens data systems. Noise, by contrast, obscures understanding at a time when accurate measurement matters more than ever.

As India updates its statistical architecture, separating analytical substance from polemics will be essential — for policymakers, researchers, and public debate alike.