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Various ways of seeing an elephant called Indian data

4 min read . Updated: 24 Oct 2021, 10:38 PM ISTRajrishi Singhal

Correct data and its proper treatment are vital for finding solutions to our economic problems

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Here’s a secret: Even Oxford University historian Peter Frankopan is susceptible to the charms of Daniel Craig. Better known for his popular book New Silk Road, Frankopan decided to dedicate a full episode of his podcast to the latest James Bond release, No Time to Die. While that can be mildly surprising, what made it remarkable was a statement by a guest on the show, David Omand, who is a retired bureaucrat and has headed many of the UK’s security and intelligence establishments. The world of intelligence and secret agents is slippery, full of contradictions and fluid moralities; Omand, however, is concerned how data— dumb by itself—gets moulded differently in different hands, often even contradictorily. And then he said this: “We are all actually Bayesians, but we don’t think about it in formal terms."

Espionage and Bayesian mathematics? Why not. Intelligence work is often painstaking and boring—lacking the cinematic thrills or accoutrements—and perhaps employs Bayesian methods to calculate the probabilities of risks and outcomes. Think George Smiley, John Le Carre’s fictional spy and Bond’s antithesis: short, podgy, bespectacled and cerebral, yet self-effacing to a fault. It is easy to envision Smiley using Bayesian mathematics to extract his counterpart, spymaster Karla, from behind the Iron Curtain. The Bayesian framework, conceptualized by 18th century British Presbyterian minister Thomas Bayes, basically enables mathematical analysis to include the probability of an event occurring based on previous knowledge of conditions associated with it. The chance of X, say, given that Y happened.

Bayesian solutions are used extensively even in public health. A June 2021 article by Reserve Bank of India (RBI) deputy governor Michael Debabrata Patra and his colleagues employs Bayesian methodology to study the interplay between the yield curve and macroeconomic factors. Many articles by RBI officials have also used Bayesian equations. Clearly, data needs optimal treatment to get optimal results. The pandemic has forced policymakers to confront multiple economic conundrums that defy past theoretical frameworks. Authorities can employ different solution frameworks, including Bayesian platforms, to overcome India’s seemingly intractable economic challenges, but, crucially, it requires correct data and the correct use of data.

Here is an example. The Indian government’s ideological aversion to fiscal gaps has shaped its tepid response to the prolonged economic slowdown. While the pandemic forced government to announce fiscal support, most of it was below-the-line stuff. The International Monetary Fund’s (IMF) recent report on India surprisingly suggests that the government should adopt a more accommodative fiscal policy: “Fiscal space has been reduced by the increase in public deficit and debt, and higher fiscal risks. However, the sizable economic slack projected for the near-term, higher fiscal multipliers, potential adverse impact of the pandemic on output in the medium term and favorable debt dynamics suggest that it is appropriate to provide additional fiscal support in the near term… Additional support could be underpinned by targeted spending on social protection, employment support and health spending." Coming from the IMF, known for its aversion to fiscal deficits, it indicates broader concerns about the pandemic exacerbating India’s poverty levels. The government will now need to balance two critical objectives: higher fiscal support in the short term and a medium-term fiscal consolidation programme.

But, more importantly, the IMF is clearly looking closely at granular data on poverty while the government obsesses over headline numbers. The premature celebration over vaccination numbers, for example, conveniently glosses over the fact that about 70% of Indian adults are still not fully vaccinated.

The second example is RBI’s dogged pursuit of an accommodative monetary policy, which pre-dates even the pandemic, in the hope that lower interest rates will induce private-sector capital investment. Unfortunately, that needle has not moved despite a prolonged period of record low interest rates. Again, there is no comprehensive analysis yet on what deters private-sector investments. The government’s current focus on infrastructure investment is expected to crowd in private sector investment. The IMF, while supportive of such expenditure, feels private-sector participation is likely to remain low-key in the medium term. This also has implications for the government’s fiscal planning.

The third problem relates to central banks in advanced economies. Most central banks in rich nations are scanning inflation data to ascertain if rising prices, due to a break-down in supply chains, are transitory or not; if the latter, it may force central banks to raise interest rates. Experts feel that certain components of the inflation index, such as fuel prices, might remain elevated for longer periods. Bank of England governor Andrew Bailey has already indicated that a rate hike might be unavoidable in the UK. But here is the dilemma: Given the faltering and uneven growth across economies, any interest-rate hike could choke nascent growth. The ripple effects of globally high inflation and low growth could wrack India’s growth impulses.

Clearly, RBI and the government need a credible rear-guard action plan, backed by data but devoid of stale ideologies