by Akhil Dua, Pinaki Mukherjee, Radhika Pandey, Ila Patnaik, Pramod Sinha, Ajay Shah.
India's emergence as a market economy has been accompanied by the emergence of business cycle fluctuations that are similar to those seen in market economies [link, link]. In understanding business cycle conditions, and in crafting institutional arrangements for stabilisation, it is essential to properly measure output and prices.
The problem
India is in reasonably good shape on measurement of prices, with older CPI-IW data and now the new CPI. (Using WPI as a measure of prices is wrong, but you don't have to make this mistake; the statistical system does have CPI-IW and then CPI). Measurement of output, in contrast, has presented serious difficulties.
The index of industrial production is widely used as a measure of business cycle conditions. However, it reflects only manufacturing, which is a small part of the economy. In addition, it is riddled with serious difficulties of measurement.
The other key measure that has been widely used is quarterly GDP data. However, contrary to what textbooks teach us, in India, quarterly GDP data is constructed without information about the demand side. In addition, there are two important concerns about the quarterly GDP data from the viewpoint of business cycle analysis:
- Agriculture is included in the overall GDP data -- as it should -- but to a significant extent, fluctuations in agriculture reflect weather shocks and do not reflect underlying business cycle conditions.
- Spending by the government is counted as output in GDP data. However, it does not reflect underlying business cycle conditions. See Robert Higgs on this subject.
As a consequence, quarterly GDP data in India is not a good reflection of business cycle conditions.
Two steps towards measuring output
In order to address these issues, we have constructed two new series which, we feel, do a good job of measuring nominal output.
The first of these is GDP excluding agriculture and excluding government. This focuses upon the output of individuals, small firms and large firms, which is what the market economy and the business cycle is all about.
The second strategy consists of utilising firm data. Listed companies are required to release quarterly results. These results are pored over by accountants, auditors, senior managers, tax collectors, shareholders, etc. They are thus likely to have few mistakes of the sort which have plagued government statistics and survey-based information.
Finance companies have very different concepts underlying their accounting data, and are hence excluded. Oil companies sometimes experience very large jumps in their revenues owing to decisions by the government about administered prices. These fluctuations are not a feature of underlying business cycle conditions. Hence, oil companies are excluded. In short, we focus on all listed firms observed in the CMIE database other than finance and oil companies.
For each pair of quarters, we construct a panel of firms observed in both quarters, and work out the percentage change in the sum of net sales across all the firms. These percentage changes are used to construct a net sales index.
Non-agricultural and non-government GDP is the business cycle. It is made up of production by small firms (going down to one employee) and large firms doing both industry and services. This measure captures large firms in both industry and services and is thus a good proxy for what is going on in business cycle conditions.
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| Net sales of non-finance non-oil firms, and GDP ex-agriculture and ex-government. Nominal indexes, non-seasonally adjusted. |
The graph above shows these two time-series. The fact that the two series -- which are constructed from completely unrelated sources -- agree with each other across long periods of time is a source of increased confidence.
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| Net sales of non-finance non-oil firms, and GDP ex-agriculture and ex-government. Nominal indexes, seasonally adjusted. |
Our first step is to seasonally adjust both series. Once again, it is satisfying to see how well the two series agree with each other, even though they are quite unrelated on their underlying sources.
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| 2-Q moving average of growth of seasonally adjusted levels. Annualised per cent. |
Using this, we are able to compute nominal GDP growth. Once again, it is striking how well the two series agree with each other. Two major recessions are visible: 2001 and 2009.
This series shows much more macroeconomic volatility when compared with what we are used to with conventional data. This is perhaps unsurprising as government expenditure is a fairly stable series, and fluctuations of agriculture are noise. When these two are removed we see substantial macroeconomic volatility. This is not surprising, as India presently lacks the frameworks of stabilisation through either monetary or fiscal policy.
Conclusion
We feel that we now observe a good measure of output in India, with two different measures that are gratifyingly close to each other. These series are a valuable starting point for business cycle research.
The key flaw of this work is that it takes us till a nominal output index. The next big hurdle to cross is that of converting to real. The simplest strategy would be to just use the CPI-IW and then the new CPI.
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