The National Statistical Office’s (NSO) third-quarter GDP estimates, which were eagerly awaited for cues on whether India’s economic slowdown had indeed bottomed out, have thrown economy watchers for a loop instead. The 4.7 per cent growth rate for the third quarter of FY20 would have cheered commentators and signalled a steadying of the boat, had the NSO left its previous estimates of 5.1 per cent for Q1 and 4.5 per cent for Q2 untouched.
But as it happens, the NSO has confounded everyone by significantly pegging up its previous quarters’ estimates. Its latest revisions now show that India’s GDP, which was expanding at a reasonable 5.6 per cent in Q1 and lost momentum to 5.1 per cent in Q2, saw growth tumble to a low of 4.7 per cent in Q3. After this data release, with disruptions to India’s manufacturing sector from the coronavirus pandemic still playing out, it appears rather premature for government spokespersons to be flagging ‘green shoots’ based on one-off indicators such as car sales. In fact, this confusion over India’s GDP trajectory amply demonstrates why India’s policymakers are often behind the curve in responding to adverse developments. Their late and often ineffectual, responses have a lot to do with the lack of timely and reliable data on which responses can be premised.
In this context, it is good to see researchers from Reserve Bank of India (RBI) publish a working paper last week that devises a model to ‘nowcast’ quarterly GDP growth instead of waiting for the NSO’s estimates, which arrive with a two-quarter lag. The staffers have designed three dynamic factor indices using commonly available high-frequency indicators, after sifting through 28 such indicators, to help policymakers make real-time assessments of the economy. The indices, when back-tested, have shown reasonable ability to provide early warning signs of a sharp deceleration in activity. Hopefully, such nowcasting models will equip the Centre with a more reliable guidepost to gauge the health of the economy than one-off data points such as car sales or the sale of movie tickets.
However, a key limitation faced by such nowcasting attempts is that researchers have to rely on a rather limited set of data points to devise them. For instance, while India has a number of high-frequency indicators on the industry leg of the GDP, its services leg is estimated through indirect proxies such as railway freight, tax collections and foreign tourist arrivals. On consumption, researchers elsewhere have access to rich data on disposable incomes, retail spending and non-farm payrolls, but RBI has had to make do with IIP-consumer goods and auto sales. The efficacy of any econometric model depends heavily on the quality of its inputs. For nowcasting models for India to work effectively, it is imperative that RBI prevails upon the Centre to tone up both the quality and breadth of high-frequency data put out by its agencies and stop the practise of burying inconvenient data.