India’s revised GDP series with base year 2022-23, which will be released on 26 Febraury 2026, introduces significant methodological changes, with administrative tax data—particularly the Goods and Services Tax (GST) —playing a larger role in national accounts compilation.
The revision reflects a broader shift by the Ministry of Statistics and Programme Implementation (MoSPI) towards integrating high-frequency administrative datasets to improve coverage, timeliness and internal consistency in economic measurement. Central to this effort are recommendations of a sub-committee on new data sources, which examined the use of GST data maintained by the Goods and Services Tax Network (GSTN).
Under the revised framework, GST return data has become a key input in estimating private corporate sector output, allocating gross value added (GVA) across states, refining the corporate sector universe, and strengthening quarterly GDP estimates.
Earlier, sectoral and state-wise allocation of output relied largely on survey-based datasets such as the Annual Survey of Industries (ASI), alongside corporate financial filings and other indicators. While these sources continue to be used, the updated methodology links company-level financial data from the Ministry of Corporate Affairs (MCA) database with GST registrations to derive a more transaction-based distribution of output.
Using identifiers such as Permanent Account Number (PAN) and Corporate Identity Number (CIN), national-level corporate financials are mapped to state-level GST registrations (GSTINs). As companies are required to register separately in each state where they operate, outward supplies reported in GST returns are used to apportion output across states. This provides a more direct statistical basis for estimating state-level GVA, replacing earlier proxy-based allocation methods.
The revised series also changes how non-filing or inactive companies are treated in GDP estimation. Previously, statistical adjustments were applied to account for companies that did not file financial returns, often using proxies such as paid-up capital to impute output. The new methodology cross-verifies MCA-listed companies with GST filings to assess actual economic activity.
Entities are categorised based on GST reporting status—those reporting transactions, those filing nil returns, those not filing returns, and those without linked GST registrations. This cross-referencing is expected to reduce overestimation arising from inactive entities and improve the accuracy of imputations for non-reporting firms.
GST data is also being used more extensively in compiling Quarterly National Accounts (QNA). Monthly information on outward supplies serves as a high-frequency indicator for sectors such as trade, real estate, communication and professional services. The sub-committee has recommended deeper use of HSN- and SAC-level data to develop more granular short-term indicators, potentially improving advance and provisional GDP estimates.
However, the sub-committee has said that greater reliance on GST data requires careful statistical treatment. As GST returns are collected for tax compliance rather than national accounting, variations in compliance behaviour, policy changes or system adjustments may influence reported data. Ensuring accurate mapping between corporate filings and GST registrations, and accounting for coverage differences across return types, will remain important for maintaining the robustness of the revised GDP framework.