The statistics ministry is working on reducing “drastic revisions” in the GDP numbers, through making changes in the method of its computation, an official source told FE.
The aim is to make tweaks in the computation method by 2026 – when the new GDP series will be officially introduced, the source said. “We want minimum revision. Currently revisions are of the range of 40-50 basis points, we want this to be reduced,” the person said.
In recent quarters, questions have been raised about the sharp changes in estimates of GDP and its various constituents, as well as high frequency indicators like index of industrial production. The national income data for the first and second quarters of last fiscal year, for instance, were revised upwards by 40 and 50 basis points respectively, from the first to second provisional estimates.
Experts also have been critical of the incompatibility between the rates of growth of GDP and its main constituents like private consumption expenditure and the sharper-than-usual gap between the GDP and gross value added (GVA). Furthermore, nominal GDP estimates have been said to be not truly reflecting the inflation situation.
The statistics ministry has constituted a committee, known as Advisory Committee on National Accounts Statistics (ACNAS), to deliberate upon the various aspects of base revision of the GDP. The committee is meeting on regular intervals to come up with a detailed methodology for the compilation of the GDP numbers.
As per sources, the first GDP estimates, on the new base year will be released in February 2026. The ACNAS is currently debating whether to choose 2023-24 or 2022-23 as the new base year of GDP, which currently is (2011-12), but the statistics ministry is of the view that 2022-23 should be the new base year.
Sources say the new base will use data from Annual Sector of Unincorporate Sector Enterprises (ASUSE) 2022-23, Household Consumption Expenditure Survey (HCES) 2022-23, and Periodic Labour Force Survey (PLFS) to capture various aspects of the economy. “In addition, GST data is likely to be used in GDP computation,” an official said.
For using GST data, to measure consumption activity, the statistics ministry is coordinating with the Goods and Services Tax Network (GSTN), to develop a framework under which “anonymous information” could be exchanged between the two organisations, the sources said.
Further, to improve the computation of real GDP numbers, the ministry is working on improving the method of deflation. Sources say the method of deflation will be improved to double deflation, as the single deflation method is a “less sound” method of arriving at real estimates.
Double deflation is a method through which nominal outputs are deflated through an output deflator, and inputs are deflated using a separate input deflator. Currently, India uses a single deflator, using an input price deflator – the Wholesale Price Index (WPI), which inflates the real growth figure in situations when input prices diverge from output prices. In FY24, WPI had averaged (-)0.7%, which had inflated real GDP growth, say economists.
Former Chief Statistician of India TCA Anant said: It would be more accurate to do double deflation than single deflation, provided we have correct and appropriate data. “Double deflation should ideally be done through the Producer Price Index (PPI), instead of WPI, which also measures input costs of services.”
The PPI is an index that measures the average changes in prices that producers receive for their goods and services produced and are captured at the factory gate or at the service provider site. This excludes the taxes, transport, trade margins and other charges that are imposed when those products reach consumers or as inputs to other producers. In other words, it is the suppliers’ price. PPI tracks price movements in both goods and services.
PPI is different from WPI in the way that it measures the average change in prices received by producers and excludes indirect taxes. WPI captures the price changes at the point of bulk transactions and may include some taxes and transportation costs. PPI also removes multiple counting bias inherent in WPI. The shedding of additional costs on products imposed by taxes and transportation makes it a more accurate gauge of price movements.