Yesterday, the Indian government admitted a huge error in the Index of Industrial Production (IIP) data for January 2012 and corrected the growth rate from a healthy 6.8% to a dismal 1.1%:
... during the compilation of IIP for January, 2012, the sugar production was wrongly taken as 134.08 lakh tonnes in place of actual figure of 58.09 lakh tonnes. ... Immediately after detection of the error, the revised IIP numbers and growth rates for the month of January, 2012 have been compiled. ... the IIP for January 2012 has been revised from 187.9 to 177.9 and, therefore, growth rate over the corresponding period of previous year has been revised from 6.8% to 1.1%.
In my view, the fact that the government has a monopoly in the production of official statistics leads to poor quality, low accountability and lack of innovation. Perhaps, these problems are worse in an emerging economy and the costs of the public sector monopoly are less severe in developed countries. But the problem is not confined to emerging markets.
Even in the US, the seasonal adjustments being used for various official statistics has been called into question (see for example, here, and here). The whole process of seasonal adjustment is ripe for disruptive innovation. First of all, the reliance on a Gregorian calendar for seasonal adjustment is increasingly inappropriate in a world where some of the fastest growing economies with large populations base their principal holidays on a lunar calendar (China, India and the entire Islamic world). China’s influence on commodity prices is so great that it is possible that the commodity price component of seasonally adjusted prices even in the developed world are probably distorted by the incorrect use of Gregorian seasonality adjustment. Via inventories and collateralized commodity financing, this might be an issue for some financial data series as well. Who knows, some large global central banks may be getting their monetary policy wrong because of Gregorian seasonal adjustments!
Secondly, I would argue that the whole idea of seasonality adjustment is an abdication of responsibility by the econometrician. Wherever we use time as an independent variable, it is a proxy for omitted variables that are more fundamental. A time trend, for example, proxies for variables like population growth, technological progress, inflation and productivity improvements. A seasonality adjustment is also a proxy for more fundamental physical and economic variables like temperature, rainfall, holidays, advance tax payment due dates, government bond issuance calendars and the like. It is far better to model these variables directly so that the economic model is more robust and meaningful. The belief that economic variables have a different behaviour in different months solely because of the position of the sun in the zodiac is astrology and not economics. Seasonality adjustments need to move from the age of astrology to the age of econometrics.
Such radical changes are unlikely to happen so long as official data is provided by a monopolist (whether in the public or in the private sector). The time has come in my view to crowd source the creation of official statistics. The government should simply make the digitized raw data publicly available and should not publish anything else. There would be no official Index of Industrial Production, but the government website would have the raw production statistics submitted by various businesses. Yes, not the aggregate sugar production, but the sugar production of each sugar mill in the country. Every user would be free to choose what outlier tests to run, what aggregation algorithm (for example, mean, median or trimmed mean) to apply on this raw data, which base year and which base year weights to adopt, and which index computation methodology (Laspeyres or Paasche, arithmetic mean or geometric mean) to use in computing indices at whatever level of aggregation or disaggregation he or she wishes.
The lack of an authoritative index may also reduce systemic risk in the economy because different indices computed by different agencies may be giving a different picture of the economy. We would probably have less herding and more muted boom-bust cycles. Like the story of the six blind men and the elephant, each of the competing privately produced indices would be a partial and therefore incomplete view. That however is far superior to one blind man and the elephant – because the one blind man does not even know that his understanding is incomplete.