The former assesses industry confidence levels while the latter focuses on output. IIP is a more reliable, representative index
The PMI for December has come in at an all-time high of 54.7 which has brought significant cheer to the economy. This comes on the back of smart growth witnessed in the core sector, of 6.8 per cent in November, which augurs well for a higher IIP growth rate. In these rather less than bright times, there is a tendency to pick up any such signal and extrapolate the same to the macro level to indicate that a turnaround, albeit a sharp one, has taken place. But what exactly does this PMI connote?
ELEMENTS IN PMI
Purchasing Managers’ Index is calculated on the basis of information received from companies on various factors that represent demand conditions. It is very different from industrial production which is indicative of actual production.
The PMI takes in responses from a company on a monthly basis on whether there has been improvement, deterioration or no change for a set of parameters relative to the previous month. Five questions have to be scored in this manner: new orders (weight of 30 per cent), output (25 per cent), employment (20 per cent), supplier’s delivery (15 per cent) and stock of purchases (10 per cent). This questionnaire is administered to 500 private sector companies and the comprehensive score is arrived at. The public sector is left out.
Intuitively, it can be seen that the purpose of the PMI is to indicate some degree of confidence level in manufacturing based on this representative sample of companies. While the reference is to the previous month, the methodology involves adjusting for seasonal influences. The answers are tabulated in terms of the proportion of respondents who say yes, no and no-change with weights of 1, 0 and 0.5 being attached to the three responses.
This is done for all the five parameters and a weighted average is taken to arrive at the score. Hence if all say no change then the score would be 50; and if all said yes, the score 100. If everybody says there is deterioration, then the score would be zero. Typically a score of above 50 is looked at positively while anything less than 50 would be a marked deterioration.
The IIP is measured as comprehensive production across the industrial sector and is a comparison over the previous year; hence in a way it takes care of seasonal factors. While the December PMI number of 54.7 can be interpreted as a case of the sample companies feeling they were better off than in November, the IIP growth rate for December would be reckoned over the same month in 2016.
Therefore, a comparison between the two is really not appropriate as the basis for comparison is different. However, as the PMI is released on the first of every month and the IIP is known on the 12th, the PMI score is assumed to be a precursor to the IIP. But, is there a strong relation between the two variables?
POOR CORRELATION WITH IIP
The coefficient of correlation between changes in PMI (month-on-month) and industrial growth rate (y-o-y) for the last five years was 0.25. The coefficient for the same when reckoned on m-o-m basis with growth in IIP was 0.15. (The coefficient of correlation states whether or not there is a strong association between two variables i.e. if PMI changes by a certain amount will IIP also move commensurately in the same direction).
Hence the relationship between the two variables is quite low and insignificant. If the same is calculated for the absolute value of PMI and IIP growth on either ‘y-o-y’ or ‘m-o-m’ basis, the results are less satisfactory at 0.18 and 0.06 respectively. Hence, statistically a high or low PMI number does not tell us anything about the IIP growth number, however compelling it may seem.
The reasons for this are the following. First, a sample of 500 companies is too small to be representative of what is happening at the aggregate level. Also as these companies would tend to be the bigger ones, the SMEs would be left out. The IIP is more comprehensive in coverage. Second, the responses are of an ‘either or’ variety and hence is not graded to any number. If the increase is of say 20 per cent, it would be given the same weightage as 2 per cent growth as the answer is only in the affirmative of the parameter being better. Therefore, there would be an inherent bias in the final numbers that are tabulated.
PMI NOT RELIABLE
Third, even if one were to force a comparison between the two indices, the PMI has only one component, output, which can be related with IIP and has a weight of just 25 per cent. Therefore, at the aggregate level the PMI could be scoring well on orders, stocks, employment or suppliers’ delivery but scoring low on output.
Fourth, even with the PMI new orders increasing, it would not necessarily mean that output would increase in a subsequent period. Fifth, the exclusion of the public sector is significant as there is a very high contribution by this segment, especially in capital goods and infra areas.
Last, since July when GST has been introduced, there have been significant disruptions in the production cycles of companies. As there was some ambiguity in the input tax treatment, companies had gone in for de-stocking prior to the introduction of GST. Subsequently there was the emergence of festival demand which pushed up production. Presently companies are getting back to their normal stock levels and are hence increasing their output. This has causes some degree of volatility in the production cycles.
To conclude it may be said that the PMI is not a leading indicator of the state of industry which is better represented by IIP growth. While the IIP growth calculation has its challenges, the fact is that this number is also used for calculating GDP when reckoning the contribution from the unorganised sector. The sample used by the PMI is not known, but a guess would be that it includes the larger companies, which are also the ones likely to report their conjectures on a monthly basis and could be biased. But nonetheless, th
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