Critically analyse the implications of adopting the double-deflator method in national accounts estimation.

The double-deflator method separately deflates output and intermediate consumption to calculate real value added. This is theoretically superior to single deflation, as it better captures price movements in both inputs and outputs.Advantages:Improved accuracy in real growth measurement.Better reflection of sectoral productivity changes.Alignment with international statistical best practices.However, the method requires detailed and reliable price indices for both outputs and inputs. In sectors where input price data is weak, estimation errors may arise. Thus, while conceptually robust, its effectiveness depends on high-quality underlying data.Overall, adoption signals methodological maturity and strengthens India’s statistical credibility, but continuous data refinement is necessary for optimal results.

New GDP Data Set to Transform Economic Accuracy in India

Q: What is the significance of revising the base year of GDP from 2011-12 to 2022-23?

Revising the base year of GDP is a standard statistical practice aimed at ensuring that national income estimates reflect the current structure of the economy. Over time, sectoral composition, consumption patterns, production technologies, and price structures change significantly. By shifting the base year from 2011-12 to 2022-23, the new series will better capture emerging sectors such as digital services, fintech, platform-based enterprises, and formalisation driven by GST.A more recent base year improves the relevance of price indices and weights used in constant price calculations, thereby enhancing the accuracy of real growth estimates. For example, the contribution of services, start-ups, and formal retail has expanded considerably since 2011-12. Using outdated weights may understate or overstate certain sectors’ contributions.From a policy perspective, updated base years improve comparability across time and strengthen macroeconomic decision-making. They also enhance India’s credibility in international assessments, especially when global agencies evaluate data quality and statistical robustness.

Discover how upgraded methodologies and new data sources will enhance the accuracy of India's GDP and GVA metrics for more reliable economic insights.

Surya

•February 27, 2026•5 mins read

India upgrades GDP data methodology

Not Started

1. Revision of Base Year and Statistical Modernisation

India’s new series of national accounts, to be released on February 27, 2026, introduces significant methodological and statistical upgrades aimed at improving the accuracy and granularity of GDP and GVA estimates. Such revisions are a standard international practice to ensure that macroeconomic data reflects structural changes in the economy.

The most visible change is the revision of the base year from 2011–12 to 2022–23. Updating the base year improves comparability across time and aligns national accounts with the present production structure, consumption patterns, and price movements.

Without periodic base revision, GDP data may misrepresent sectoral contributions and distort growth comparisons, affecting fiscal planning, monetary policy, and international credibility.

“Statistics are the eyes of the policymaker.” — P.C. Mahalanobis

Accurate national accounts are foundational to evidence-based governance. If statistical systems do not keep pace with structural changes, macroeconomic policy may be based on outdated assumptions.

2. Methodological Improvements in Sectoral Estimation

The Sub-Committee on Methodological Improvements has introduced sector-specific refinements to better measure value added.

In the non-financial private corporate sector, the earlier approach allocated a company’s entire GVA to the sector where the bulk of activity occurred. The new method distributes value added based on activity-wise revenue shares, enabling more accurate sectoral attribution.

For the general government sector, the new series incorporates the imputed value of housing services provided to government employees. Coverage of autonomous institutes and local bodies has also been expanded.

These changes aim to better reflect the true composition of output and avoid sectoral misclassification.

More precise sectoral allocation improves measurement of structural transformation. If multi-activity firms are misclassified, sectoral growth trends may be inaccurately interpreted.

3. Improved Measurement of Household and Informal Sector Activity

The household sector contributes significantly to India’s economy, especially through unincorporated enterprises. The earlier series relied on extrapolation methods.

The new series uses:

Annual Survey of Unincorporated Sector Enterprises (ASUSE)
Periodic Labour Force Survey (PLFS)

These will be used on an annual basis, enabling direct yearly estimation rather than interpolation.

Private Final Consumption Expenditure (PFCE) will also be measured more granularly using enhanced Household Consumer Expenditure Surveys and production-based estimates.

This reduces estimation errors in consumption — a major driver of GDP.

Given the size of the informal and household sectors, direct annual estimation enhances reliability. Without such improvements, GDP may under- or over-estimate grassroots economic activity.

4. Integration of GST Data and Corporate Activity

A major statistical upgrade is the expanded use of Goods and Services Tax (GST) data. Earlier, GST data was used mainly in quarterly estimates and selectively in annual accounts.

In the new series, GST data will:

Improve estimation of regional output of private corporations
Enhance measurement of value added by private firms
Help identify active companies
Improve estimation for non-reporting companies

This strengthens the link between administrative tax data and macroeconomic statistics, improving ground-level accuracy.

Administrative datasets like GST provide real-time economic signals. Their integration reduces reliance on proxies and improves transparency in output measurement.

5. Better Data from States, Banks, and Financial Institutions

The Sub-Committee on Incorporation of New Data Sources has expanded coverage across sectors.

States have improved reporting from:

Local bodies
State autonomous institutions

This enables more direct estimation rather than imputation.

In financial sector measurement:

Statistical Table Related to Banks in India (STRBI) by RBI will be used for public and private banks.
Proxy-based estimation of private NBFCs is being replaced by actual financial data from the Ministry of Corporate Affairs.

These changes improve reliability in measuring financial intermediation services.

Improved financial sector measurement is crucial because banking and NBFC activities significantly influence credit growth and investment trends. Weak data in this sector could distort macroeconomic assessments.

6. Strengthening Informal and Agricultural Sector Estimates

The new series uses ASUSE data more effectively to capture previously underrepresented activities such as:

Insurance agents
Gross Fixed Capital Formation (GFCF) in the unincorporated sector

In agriculture, updated methodologies incorporate studies from:

Inland Grassland and Fodder Research Institute
Central Marine Fisheries Research Institute
Central Inland Fisheries Research Institute
Agricultural Development and Rural Transformation Centre

This enhances sectoral precision in crop, fisheries, and allied activities.

Given agriculture’s role in employment and rural income, improved estimation strengthens rural policy design.

Accurate agricultural and informal sector data improves poverty assessment and rural development planning. Underestimation can lead to misallocation of public resources.

7. Technical Refinement: Double Deflator Method

The new series adopts the double-deflator method more extensively. This method deflates output and intermediate consumption separately to derive real GVA.

This improves the measurement of price changes and avoids distortions arising from single-deflator approaches.

Better incorporation of price movements ensures that real growth reflects actual volume changes rather than inflationary effects.

Precise deflation is critical for distinguishing real growth from price effects. Inaccurate deflation can misguide monetary and fiscal responses.

8. Implications for Policy and International Credibility

Upgraded national accounts have multiple implications:

Impacts:

Improved sectoral policy targeting
Better fiscal deficit and debt calculations
More accurate State-level GDP estimates
Enhanced international credibility of India’s data

The IMF previously graded India’s GDP and national accounts data as “C,” indicating scope for improvement. Methodological upgrades can strengthen global confidence in India’s macroeconomic statistics.

Reliable data supports investor confidence, sovereign ratings, and multilateral engagement.

Credible statistics underpin macroeconomic stability and investor trust. Weak statistical systems can undermine policy legitimacy and global standing.

Conclusion

The shift to a 2022–23 base year and the incorporation of new methodologies and data sources mark a significant evolution in India’s statistical architecture. By integrating GST data, improving informal sector estimation, refining financial sector measurement, and adopting the double-deflator method, the new series enhances accuracy and transparency.

In the long term, robust national accounts will strengthen evidence-based policymaking, fiscal prudence, and India’s credibility in the global economic system.

Quick Q&A

Everything you need to know

Revising the base year of GDP is a standard statistical practice aimed at ensuring that national income estimates reflect the current structure of the economy. Over time, sectoral composition, consumption patterns, production technologies, and price structures change significantly. By shifting the base year from 2011-12 to 2022-23, the new series will better capture emerging sectors such as digital services, fintech, platform-based enterprises, and formalisation driven by GST.

A more recent base year improves the relevance of price indices and weights used in constant price calculations, thereby enhancing the accuracy of real growth estimates. For example, the contribution of services, start-ups, and formal retail has expanded considerably since 2011-12. Using outdated weights may understate or overstate certain sectors’ contributions.

From a policy perspective, updated base years improve comparability across time and strengthen macroeconomic decision-making. They also enhance India’s credibility in international assessments, especially when global agencies evaluate data quality and statistical robustness.

One major improvement is the shift from assigning a company’s entire GVA to its dominant sector to an activity-wise revenue share approach. Earlier, diversified firms operating across sectors were classified based on their principal activity, which led to sectoral misallocation. The new method distributes value added according to actual revenue shares from different activities, producing more granular and realistic sectoral estimates.

In the general government sector, inclusion of housing services provided to government employees ensures better estimation of imputed services. Additionally, expanded coverage of autonomous bodies and local institutions reduces reliance on proxies and imputations.

These methodological refinements reduce distortions in sectoral GDP composition. For instance, a conglomerate involved in manufacturing and services will now reflect its true multi-sectoral contribution, leading to improved structural analysis and better-targeted economic policies.

India’s economy has a substantial informal and unincorporated component, including small enterprises, self-employed workers, and household-based activities. In the 2011-12 series, household sector estimates often relied on extrapolations. The new series uses Annual Survey of Unincorporated Sector Enterprises (ASUSE) and Periodic Labour Force Survey (PLFS) data annually, allowing direct estimation.

This shift is crucial because household enterprises contribute significantly to employment and output. More accurate measurement ensures that fluctuations in informal sector activity—such as during shocks like demonetisation or the pandemic—are better captured.

Furthermore, improved estimation of Private Final Consumption Expenditure (PFCE) using Household Consumer Expenditure Surveys enhances understanding of demand trends. Since consumption drives a large share of India’s GDP, better measurement strengthens macroeconomic forecasting and fiscal planning.

GST data provides near real-time, transaction-level information on business turnover across States and sectors. Previously, GST data was used mainly in quarterly estimates and limited annual sectors. The new series integrates GST data more comprehensively to estimate regional output and identify active companies.

For example, GST filings help identify non-reporting companies that may still be operational, reducing underestimation of corporate value added. It also improves regional allocation of output, enhancing State-level GDP estimates.

This integration reflects India’s broader formalisation process. As businesses shift into the tax net, GST data offers a more accurate representation of ground-level activity, reducing reliance on outdated surveys or assumptions.

The double-deflator method separately deflates output and intermediate consumption to calculate real value added. This is theoretically superior to single deflation, as it better captures price movements in both inputs and outputs.

Advantages:

Improved accuracy in real growth measurement.
Better reflection of sectoral productivity changes.
Alignment with international statistical best practices.

However, the method requires detailed and reliable price indices for both outputs and inputs. In sectors where input price data is weak, estimation errors may arise. Thus, while conceptually robust, its effectiveness depends on high-quality underlying data.

Overall, adoption signals methodological maturity and strengthens India’s statistical credibility, but continuous data refinement is necessary for optimal results.

To improve global statistical credibility, India should focus on greater transparency and documentation of methodologies. Publishing detailed metadata and back-series estimates would enhance comparability and trust among international agencies.

Second, strengthening coordination between Centre and States is crucial. Improved State-level reporting from local bodies and autonomous institutions should be standardised, ensuring uniform data quality across regions.

Third, investment in big data analytics—such as integrating digital payments, e-way bills, and satellite data—could further enhance real-time economic tracking. Capacity-building in statistical institutions and periodic independent audits would reinforce credibility. Such measures would not only improve ratings but also strengthen evidence-based policymaking.