Hiya Folks! Read in the Economic Times today (15/7/14) that happily, for the Modi Sarkar, the effect of the rainfall has not yet petered down into the prices of food and other commodities. WPI inflation, the article claims, is at a 4-month low of 5.43% and down from 6.01% in May. However, ET also suggests, rather ominously, that its a matter of time before it does enter the indices and hence it looks like a rate reduction in the 7th August credit policy announcement is unlikely. I agree. Inflation indices that seem benign today are the result of the RBI being consistently tough in keeping the rates high; a small slip, and we could well move into much more uncomfortable inflation figures, that would hurt the Aam Admi pretty badly.
But how long will it be before the indices start showing the effect of an exceptionally dry June? I thought that a quick look at the data would clear up matters significantly. The first place I went to was the Indian Meterorological Department website, where to my trauma, I found excellently maintained data sources on rainfall every month, every year, as per district, region, state….you name it and they have it. Trauma? You may well ask, but given my earlier brushes with badly maintained data sets at various Government levels, it has become something of a favourite past time to bash up Government departments for not maintaining proper data. So when my favourite punch bag suddenly became a Santa goodies bag, I was flummoxed and flustered, to say the least. But bravely, I surfed on and got my hands on data for average Indian rainfall for the past 10 years. Now, in what follows I give a very basic analysis of how I am viewing things. If one just takes the growth rate on rainfall over the last year and compares it to the y-o-y GDP growth rate, this is what one would get:
The X-axis is moving from 2001 to 2013. Now, while it is a MUCH better idea to look at rain deviations from the long term period average, I have done a level 1 analysis here and am just looking at y-o-y trends for a rough picture. You can immediately see that the rainfall performs very poorly in 2009, which indeed has been identified to be a pretty bad drought year for India in the recent past.
Now, its interesting to note that the GDP growth rate moves from around 8% in 2009 to 10% in 2010, despite the drought conditions. How, I ask myself, does this become possible? One of the answers obviously is that the agriculture sector contributes to less than a quarter of Indian GDP and so, any effect of the monsoons, on the growth rate could be pretty damp. So, while the agricultural GDP may have fallen, India still shows better growth on the basis of a service sector responding to a strong global recovery. This also perhaps points to the fact, that the bad monsoon spell in 2014 so far, may not have an immediate impact in terms of slowing down the GDP growth rate.
However, even though the effect of the monsoon on the GDP growth rates may have been damp in the 2009 episode, I believe that this effect will definitely show itself in terms of higher food prices and in terms of higher inflation in 2009 and 2010. I go back to the inflation statistics to confirm this. In what follows is an analysis of WPI because WPI being at a 4-month low was the trigger point for my thoughts today.
Interestingly, the WPI inflation for the year 2009 itself stands out in the crowd because it is so phenomenally low! Take a look at this:
Of course, while the WPI is low, it undoubtedly shows a hike (pl note that WPI inflation figures are to be read as y-o-y) all through the rains and from October onwards moves into an alarmingly high slope category. I also see, albeit just on visual inspection, that the inflation effect persists throughout the next two years: The top moving inflation series are in 2010 and 2011. The persistence makes sense. A bad monsoon means that the ensuing kharif and the rabi seasons would underperform, supply would get affected and the prices would rise. In a traders’ market with alarmingly low levels of legal compliance on hoarding, there would be immediate incentive to hoard the non-perishables and the not-so-perishable cereals/ pulses/ vegetables, thereby driving up the prices. In the meanwhile, food inflation would also contribute highly to the retail inflation, which in turn would get reflected in the dearness allowances, thereby sparking off a wage-price spiral. The Government would be tempted to raise the wages paid under schemes such as NREGA, to make sure that the rural labourers, the beneficiaries of the scheme, always have enough money to purchase sufficient amount of food. This would drive up the farm sector wages, further increasing agricultural costs and further putting pressure on the already high food prices. These factors have been well-documented and are a part of the agriculture-food-inflation economics that is India.
Visiting these facts today is important because it helps us to sense where the rogue fundamentals affect the system. I have a feeling that this bad monsoon may not immediately take off on bad growth figures, but it will play sharply on inflation. So reduction in the rates in the August policy would be premature and indeed, dangerous. Whilst the monetary policy holds on to tighter reins, the Modi Sarkar would really need to crack the whip on hoarding. Unless we get some action in that sphere, there is no way of avoiding high food inflation in the future. The other thing the Government needs to do is control the urge to increase MSPs/ NREGA wages in anticipation of higher food prices. It is very difficult to understand whether high NREGA wages are the cause or effect of food inflation. There seems to be some level of bilateral causality between the 2 variables, and I would like NOT to see any immediate action in that sphere.
That’s about it from my end. Food is going to get expensive, folks…food for thought, anyone?
*A disclaimer before I end. These are quick reactions to the piece in ET this morning, not well-crunched research results.
**I also want to thank my student Shrabana Mukherjee from Symbiosis School of Economics, who helped me to put together WPI data quickly for this piece.