The biggest nightmare of Indian economists is the quality of data. Sample this from the latest Index of Industrial Production (IIP) release for August . Stainless or alloy steel grew by 160.8 percent, air conditioners grew by 80.1 percent and plastic machinery including moulding machinery grew by 59.4 percent. Yet again, items showing high negative growth are telephone instruments that include mobile phones and accessories, which shrunk by 57.2 percent, while computers were down 49.5 percent. How can air conditioner grow by 80 percent year-on-year or plastics by 60 percent when the Gross domestic product (GDP) is growing by 5 percent? How can telephone instruments fall 58 percent in a month when companies have reported more telephone connections? Economists and businessmen are constantly thrown aback by the amazing volatility of the IIP data. Their horrors magnify when they are told the IIP data is used to calculate GDP and other data. Other big gaps in India’s statistics are the lack of labour data. India must be one of the few countries with no single unemployment figure, not even a lagged one, specially when 60 percent of India’s GDP comprises services. Nevertheless, the data is infrequent and extremely sketchy. Financial services in the last several quarters grew in double digits, but economic activity remained weak and hence, the number sustains to be a suspect; as several policy makers have said the biggest constraint to good policies is good data. CNBC-TV18’s Latha Venkatesh spoke about the quality of India's macro-economic data with TCA Anant, Chief Statistician and Pranob Sen, Chairman, National Statistical Commission. Below is the edited transcript of the discussion:


 Q: Why is the IIP data so full of such extreme numbers and it is year-on-year, we cannot even say it is seasonal? 

kaustav You may be aware sometime back Maruti factory in India which produces virtually all its cars from Gurgaon had a serious labour trouble. Their output went to zero in a very short period of time. For a fixed single entity, these types of fluctuations do happen. There could be any number of reasons for why a single entity sees volatility in its production. Now in principle when you disaggregate IIP down to individual items, the volatility you are seeing is a reflection of how many entities are producing that item and how many of them are being captured in IIP. Remember IIP is an index that has a certain statistical construct. In other words, it has a fixed base and a fixed set of entities that are tracked through a period of time. Now you can turnaround and say why not do something else but that is a different problem. This index is defined like this and this definition is given very clearly in its methodology. 

Q: While one off volatile data like a minus 160 percent or plus 160 percent can be explained by a strike, my sense is and I am pretty certain about this that the volatility is far more than the number of strikes we have. Now as the head of the statistics commission is this a problem you are addressing, how are you planning to address this if at all? Sen: In a sense, it links up to what Anant said. The fact of the matter is that as a country for a large number of individual products there are a very few number of companies which totally dominate. Now it is not just a question of strikes, every firm, every unit will have a planned down time, no plant can work 365 days a year on the same basis. There are plant downtimes that get reflected in the way the data gets captured. Now this would have been less of a problem if there had been a large number of units because then hopefully, what would have happen is things would average out across the companies. Q: Can we correct it in some fashion? These are days of such high automation, surely we can connect this electronically to either excise duty data or capture it in a different fashion so that a range of sectors or companies in the unorganised sector are captured. Is their some way to correct it given the evolution in electronics? Sen: There are natural limitations to what can be done in terms of volatility. There are two ways of reducing volatility, the first of course is to increase the number of units but given the fact that we do have a very long tailed industry structure for most products, you cannot get away from what is happening. There are very few companies that produce the bulk of the output and therefore whatever volatility is there in their production is going to get reflected in the data. The second way of doing it is that you aggregate. So instead of having individual product level you have category level data that will be smoother. But again, if you look at in terms of the use that people make of this information, you either take it at the totally aggregate level that is the IIP itself which is used essentially by finance companies and finance departments to really track the movement of the economy, it is used by us to calculate the quarterly GDP and so on. On the other hand, you have industry level analysts who what to have the industry level data and there is a product level data. So you have got both the extremes and you just have to live with it. They are telling you different things and what it is telling you is legitimate at both ends. Q: As we move from WPI to CPI. CPI has not evolved in the same lines as WPI – that same level of disaggregation is not there and there is a lot of confusion, for instance part of the diesel gets reflected in the core CPI – that is transportation. In that miscellaneous and others the level of detail is not there. Is one of the projects before you and the CSO to improve the disaggregation in the CPI? Sen: The data is disaggregated enough. We don’t just release it and we do not release it for precisely the same reason that Anant said that you are going to get a huge amount of volatility in each of at the product level. These are indices and indices are composed of taking a number of things putting them all together and then expecting that their individual fluctuations will cancel it themselves out. But in so far as the database is concerned, we have it down to the individual products. Q: I am asking you about why aren’t disaggregated CPI data released? Anant: There are two things – one, CPI is a new series and before we release disaggregate data, we want to be sure that the data flow is smooth. At the disaggregated level CPI data suffers from one limitation which we are working to make sure that our release pattern confirms to that and in a large number of items this consumes to issues relating to specification change. We collect data on items. Items go in and out of market based on peoples’ preferences and characteristics. When an item goes out, CPI has a method of replacing it with the nearest item that has become more popular. For example, a particular type of clothing or a particular type of fan or electricity product or whatever is being used by the consumer may not be available and something else has come in. however, whenever specifications change the price level changes. CPI has a well-defined method, its there, you will have to read the detailed methodology has to how specification changes are handled and introduced into the index calculation. When we release disaggregated data, these will have to be flagged and pointed out. Yes, it is under consideration but the caveats that will accompany the release have to be clearly delineated, worked out before we can start to process of release.