Context

The latest GDP data witnessed significant revisions that have gone largely unnoticed.

The GDP data revision and its criticism

• Revisions an act of due diligence: In the last few years there has been a lot of noise regarding the data revisions.
  • The need for closer examination: While part of revision requires closer examination, we must be fair to our statistical system as such revisions are, in large part, due diligence and happen globally.
• Schedule of NSO estimates
  • First estimate: The NSO releases the first estimates of any fiscal year in January.
  • Revises the January’s first estimates in February.
  • And then again in May.
  • Simultaneous revision in February: Simultaneously, it revises the previous year estimates in February, alongside the February data release.
• Suspicion of statistically protecting the 5% growth: The primary criticism, with the current year’s fiscal data, is that the revisions in February for 2019-20 and the 4th revision in 2018-19 are almost identical, implying that the sanctity of 5 per cent growth was statistically protected.

Examining the criticism purely on the data

• Precedence of 1^st and 2^nd quarter revision: There is precedence to the first and second quarter revisions for the current financial year that happen in February.
  • For example, while in the current fiscal, the cumulative downward revision was close to Rs 30,000 crore.
  • In FY19, there was even a greater upward revision of roughly Rs 86,000 crore in February.
• Is there precedence of such large first-time revisions? Yes, there has been since 2014-15. In 2018-19, the first-time data was revised by a sharp Rs 1.43 lakh crore, while in 2017-18, it was revised by an even larger Rs 1.69 lakh crore.
• Revision in the same direction: The simultaneous revisions are mostly in the same direction, though different in magnitude, and hence it is unfair to say that the 2018-19 data was revised downwards to protect the 2019-20 numbers.

What was the problem?

• Uncertainty: The problem has been that the global and domestic uncertainties in 2017-18 and 2018-19 have been so swift that it has been virtually impossible to predict the outcome initially.
  • While in 2017-18, the final estimates were progressively higher.
  • In 2018-19, while the interim estimates were higher, they were drastically scaled-down later as the impact of the NBFC crisis began to unfold.
• The US example: The US Fed had also missed the possibility of the US economy bouncing back in 2018 on the back of tax cuts when in 2015 it had projected the economy to expand by only 2 per cent, only to change it to 3 per cent in 2018 (almost at par with scale of revisions in India).

Why such unconditional biases arise?

• Asymmetric loss function: It is common for such unconditional bias to arise due to the fact that the statistical reporting agency produces releases according to an asymmetric loss function.
  • For example, there may be a preference for an optimistic/pessimistic release in the first stage, followed by a more pessimistic/optimistic one in the later stage.
• Cost factor: Intuitively, one might argue that the cost of a downward readjustment of the preliminary data is higher than the cost of an upward adjustment.
• This asymmetric loss function is not so relevant at the reporting stage but at the forecasting stage.
• Interpreting the data revision: A statistical reporting agency like the NSO simply does not have all the data at hand and has to forecast the values of the yet to be collecting data.
  • It is at that moment that the asymmetric loss function comes into play.
  • So, we must be careful about interpreting data revisions by the NSO by attributing ulterior motives as we more often tend to do.

India lagging in the use of data analysis

• Unlike countries across the world, India is still significantly lagging in its use of data analysis.
  • Methodologies based on thin surveys: Some of the current methodologies of data collection is based mostly on thin surveys.
  • Not supported by the data in public domain: It is also not supported by data available in the public domain that are more comprehensive, less biased and real-time in nature, based on digital footprints.
  • The end result is that we end up publishing survey results that are misleading.

Way forward

• Development of big data and AI bases ecosystem: We must develop an ecosystem that is high quality, timely and accessible.
  • Big data and artificial intelligence are key elements in such a process.
  • Big data helps acquire real-time information at a granular level and makes data more accessible, scalable and fine-tuned.
• Use of payment data: The use of payments data can also help track economic activity, as is being done in Italy.
  • Different aggregates of the payment system in Italy, jointly with other indicators, are usually adopted in GDP forecasting and can provide additional information content.

Conclusion

To be fair to both the RBI and the NSO, the volatility of oil prices and structural changes in the economy make the forecasting of inflation and GDP a difficult job indeed. However, we should supplement our existing measurement practices with “big data” to make our statistical system more comprehensive and robust.

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