ET View: In terms of growth contraction, the worst seems over


The second advance estimates of national income for this fiscal and third quarter (Q3) measurement of gross domestic product (GDP) contain both grim figures and good news. The good news is that growth is back in positive territory for Q3, albeit a modest 0.4%, after steep contraction of economic activity in the first two quarters. The grimness pertains to an estimated minus 8% growth contraction for the fiscal 2020-21. And the way forward clearly is to step up investment and capital formation, so as to purposefully boost growth.

Notice that as per the latest advance estimates, gross fixed capital formation or GFCF, which denotes investment, at current prices, is down to a lowly 26.7% of GDP. Note that in our high-growth years over a decade ago, GFCF was routinely estimated at over 35% of GDP. The Union Budget has rightly proposed stepped-up infrastructural investments to shore up growth.

In terms of current prices, GDP for Q3 is estimated to have gone up by a credible 4.3%, over the like period in the previous year. It does reflect higher value addition, read dearer prices, in sectors like agriculture, given rigidities in supply amidst pandemic-imposed restrictions in delivery and logistics.

Further, GDP estimates for April-December does show steep contraction in growth in such sectors as manufacturing, minus 10.8%, construction, -17%, and trade, hotels, transport and communication, nearly -24%. And recovery in these segments would determine the overall growth momentum next fiscal.

The Central Statistics Office (CSO) has put out the second advance estimates of national income by extrapolating data from benchmark indicators like the index of industrial production. But CSO has pointed out that in the unprecedented pandemic year 2020-21, GDP estimates are likely to undergo ‘sharp revisions’ going forward, with more up-to-date data and actual figures.

We surely need to work on more real-time estimates of GDP by better leveraging technologies like artificial intelligence and machine learning.




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