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Manufacturing Output in New GDP Series
The new gross domestic product series, with base year 2011–12, has mostly replaced the Annual Survey of Industries with corporate financial data for estimating manufacturing value added. This has resulted in its higher share in GDP and a faster growth rate (compared to the older series). The Central Statistics Office claims that the new series better captures value addition, as ASI reportedly left out activities outside the factory of an enterprise. This claim is probably not true, as is evident from closer examination of a sample of ASI primary schedules.
The views expressed in this article are those of the authors and not their employer organisations.
In 2015, the Central Statistics Office (CSO) introduced the new series of National Accounts Statistics (NAS) with 2011–12 as the base year, replacing the earlier series with the base year 2004–05. The new series has followed the guidelines of the United Nations’ System of National Accounts (UNSNA) 2008, replacing the earlier template of UNSNA 1994. The revision has, as always, introduced some newer methodologies and updated many databases. However, dramatic and unexpected changes in the levels and growth rates of the gross domestic product (GDP) (and its principal sectors) have caught public and policymakers’ attention, raising doubts over the veracity of the new GDP estimates.
Specifically, the manufacturing sector estimates in the new series are in the eye of the storm, since its share in GDP at current prices is larger by about two percentage points (compared to the old series), and its annual growth rates are significantly higher—with a change even in the direction of growth in some cases. For instance, for 2013–14, the growth rate of manufacturing gross value added (GVA) at constant prices swung from (-)0.7% in the old series, to (+)5.3% in the new series (Figures 1a and 1b). Such wide variations in the growth rates for the same years reported by the two series of the same publication, expectedly, drew widespread criticisms, especially since the new estimates were quite at variance with other macro correlates (Nagaraj 2015a).
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