Context: The National Statistical Office (NSO) released a new series of National Accounts Statistics (NAS), reporting manufacturing GVA of ₹38.6 lakh crore in 2023-24, equal to 14.7% of GDP at current prices.
- An alternative estimate based on official Annual Survey of Industries (ASI) and Annual Survey of Unincorporated Sector Enterprises (ASUSE) data produces a substantially lower figure, raising questions about the reliability and methodology of the official estimate.
Manufacturing Sector: Two Broad Segments
- Organised/formal manufacturing includes registered factories and companies, while the unincorporated/informal sector consists largely of small factories, workshops and household enterprises outside the corporate/factory framework.
- ASI provides production and value-addition data for the factory sector, while ASUSE covers unincorporated enterprises; together, they broadly capture manufacturing activity.
The GVA Discrepancy
- For 2023-24, ASI + ASUSE data yield an Alternative Estimate (AE) of ₹27.4 lakh crore, compared with the official NAS estimate of ₹38.6 lakh crore.
- Thus, the official estimate is 40.9% higher than the alternative estimate, a gap too large to be readily explained by minor definitional or methodological differences.
- Since ASUSE is also the source used for the informal-sector component of NAS, and the unincorporated sector accounts for only 13.9% of manufacturing GVA, the major source of the discrepancy appears to be organised manufacturing.
Why MCA-21 Data Matter
- Under the revised NAS methodology, the NSO uses MCA-21 corporate balance-sheet data from the Ministry of Corporate Affairs to estimate organised manufacturing GVA.
- This approach, introduced during the previous 2011-12 base-year revision, partly replaced ASI-based estimation and has continued in the latest series with modifications.
- MCA-21 contains statutory financial filings of registered companies, but questions arise regarding the coverage, classification and scaling-up of companies for estimating the manufacturing universe.
Employment-Based Cross-Check
- Employment data can be used to validate GVA by applying technical ratios—output/value-added generated per worker—derived from ASI and ASUSE datasets.
- The Periodic Labour Force Survey (PLFS) 2023-24 estimated 697.5 lakh workers in manufacturing.
- In contrast, ASI and ASUSE datasets account for only 532.9 lakh workers associated with the official GVA, leaving 164.6 lakh residual workers.
- Differences in employment definitions and survey methodologies mean this comparison is not exact, but it provides a useful cross-check of the official GVA estimate.
Accounting for Residual Companies and Workers
- The gap partly reflects 2,72,534 MCA companies not covered among the 78,618 private companies captured by ASI.
- Many residual companies may be non-factory private companies, while some residual workers may belong to very small unincorporated enterprises outside ASUSE coverage.
- Applying relevant ASI-ASUSE technical ratios to these residual workers produces an estimated additional GVA of around ₹3.6 lakh crore.
- Adding this to the alternative estimate of ₹27.4 lakh crore gives a potential manufacturing GVA of about ₹31 lakh crore.
The Unexplained Portion
- Even the adjusted estimate of ₹31 lakh crore remains 24.5% below the official ₹38.6 lakh crore figure.
- The adjusted estimate accounts for only about 80.3% of the official GVA, leaving approximately ₹7.6 lakh crore, or 19.7%, unexplained.
- This residual difference constitutes the central statistical puzzle surrounding the new manufacturing-sector GDP estimates.
Possible Explanation: Value Addition Outside Factories
- The NSO argues that establishment-based ASI may miss value addition occurring outside factory premises, such as head-office operations, marketing, distribution and R&D.
- However, existing research has questioned whether such activities can explain the magnitude of the observed discrepancy.
- Another possibility is that the scaling-up of MCA-21 data from sampled/active companies to the wider corporate universe may generate an inflated estimate if the size and composition of the underlying universe are inadequately verified.
GVA
- Gross Value Added (GVA) measures the value created by producers and is calculated as output minus intermediate consumption.
- At the aggregate level, GDP = GVA at basic prices + net taxes on products.
- Sectoral GVA is therefore crucial for understanding the contribution of manufacturing, agriculture and services to economic activity.
Important Concept: Base-Year Revision
- National accounts periodically revise their base year and estimation methodology to reflect structural changes in the economy and improve data coverage.
- The current debate illustrates why revisions must balance new data sources and methodological improvements with transparency and independent validation.
Way Forward
- The MCA-21 database and NSO's estimation methodology should be made sufficiently accessible for independent statistical scrutiny.
- Cross-validation using ASI, ASUSE, PLFS and corporate data can help identify coverage gaps and inconsistencies.
- Greater transparency is essential to determine whether the higher NAS estimate represents a fuller measurement of manufacturing activity or possible overestimation.
Conclusion: The issue highlights the importance of data quality, methodological transparency and independent verification in measuring India's economic performance. Resolving the manufacturing GVA gap is essential because the credibility of GDP statistics directly influences economic policymaking, fiscal planning, investment decisions and assessment of structural transformation.
