The US, too, has gaps in GDP accounting, except that it uses the expression “statistical discrepancy”
I asked someone what her income was last month. She gave me a figure and produced a pay-slip. I next asked her how she had spent that income. She thought of various items, but couldn’t explain what had happened to R15,000. I told her I believed neither her income figure, nor her pay-slip. I am sure you agree mine was a stupid statement. Anyone who has had some exposure to economics and national income accounting knows there are three ways to compute national income—summing production, summing income (value of inputs) and summing expenditure. Conceptually, whichever way I aggregate, the answer should be identical. Because of multiple reasons, different sources of data being one, they differ. This isn’t an Indian problem. It occurs in every country, USA included. We have recently had a hue and cry about “discrepancies” in Q4 FY16 GDP estimates. If those discrepancies hadn’t been there, real growth would have been 3.9%, not 7.9%. “Fake”, “spin”, “jumla” and more colourful expletives have been used. What are discrepancies? GDP has been computed using the product approach, the production or supply-side. It is now computed using the expenditure approach, a bit like the demand-side. The gap between the two numbers is called “discrepancies”.
Notice that just because I have been unable to explain how some income was spent, that income doesn’t vanish into thin air, any more than R15,000 from a pay-slip becomes “spin”. There is no dispute about those goods and services (their value) having been produced. Notice that a country like the US also has such gaps, except that in the US, the expression used is “statistical discrepancy”. Perhaps the use of the adjective “statistical” lends legitimacy in the eyes of our commentators. In Q4 of FY16, how large were these discrepancies? R143,210 crore in constant prices and R172,106 crore in current prices. Suppose I were to ask that lady what her income was in the same month last year, not this year. Do you think she would have used some kind of deflator to derive her real monthly income before telling me? I doubt it. In all probability, she would have reported her nominal income. Indeed, national income is calculated at current prices and then converted into constant price numbers (and real growth) using deflators. Therefore, if I am going to spin a tale about cooked up figures, I should use the current price numbers. Apart from everything else, current price figures are higher than constant (2011-12 prices) ones.
However, commentators have all worked with constant price numbers, which further substantiates the proposition that they know precious little about national income accounting. An impression has also been conveyed that Q4 of FY16 is special, because of discrepancies. Discrepancies in current prices were R139,540 crore in Q4 of FY12 and R130,419 crore in Q4 of FY13. Nothing special about FY16. Such discrepancies have always existed. I have a series that goes back to FY07. (It can be dragged back earlier still.) Naturally, it’s best to express discrepancies as a percentage of GDP, since nominal figures are involved. For Q4 of FY16, that high current price figure converts to 4.7% of GDP. In quick estimates in Q4 of FY13, the share was 6.2% and in revised estimates in Q4 of FY14, the share was 5.9%. This is hardly a case of FY16 being an outlier. Why do we have such high discrepancies? After all developed countries may have discrepancies, but they aren’t this large. That’s because our expenditure data is bad and remember, “discrepancies” simply mean a residual category.
Expenditure has categories of private final consumption expenditure, government final consumption expenditure, gross fixed capital formation, change in stocks, valuables and net exports. Of these, we have some information on government final consumption expenditure, gross fixed capital formation and net exports. The rest of it, including private final consumption expenditure, is pure guesswork, particularly when quarterly data is concerned. (We weren’t really equipped, statistically speaking, to start a quarterly GDP series.) Therefore, as we go through the cycle of revised estimates and quick estimates under the old GDP series, and provisional estimates and first revised estimates under the new GDP series, those “discrepancy” numbers themselves change. To give one instance, under revised estimates, in Q4 of FY14, discrepancy/GDP ratio was 5.9% and became 0.7% in quick estimates. It is bound to be no different in Q4 of FY16 too. Once we have a full year’s data, we shouldn’t bother about quarterly GDP numbers any longer. They are not robust. In current (and constant) prices, we also have FY16 GDP data for the full year, not just quarters. This is the one that showed 7.6% real GDP growth for the entire year. This is also the one which shows discrepancies of R9,135 crore for the full year (0.1% of GDP). If a full year is superior to quarterly data, why didn’t commentators pick FY16, instead of fourth quarter of FY16? Clearly because headlines wouldn’t have grabbed the eyeballs.
“It is a capital mistake to theorise before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.” That’s a Sherlock Holmes quote from “A Scandal in Bohemia.” But the evidence suggests that even if one has data, one can twist facts to suit theories. Either commentators are ignorant (they don’t know economics), or there are spin columnists.
The author is Member, NITI Aayog Views are personal