Just the other day, a political activist called me up from Patna and shared with me his views about what the ordinary people are going through while an economic slowdown looms on the near horizon. “Sadness and a sense of hopelessness has gripped ordinary people. And this mood is deepened by merciless inflation, power outages for 10 to 14 hours and inadequate supply of drinking water. Worse, there is no leader or a sensitive government to help them out from this crisis.”
Surely, Nitish Kumar is doing a better job if growth figures of the state are anything to go by, I asked him. “The misery of the people does not suggest this, at least.”
He is not the only one who rubbished the statistics that show Bihar’s growth figures nosing ahead of others. Informed articles in reputed journals puncture holes in the claims made by the Bihar government since Nitish came to power some years ago. Bihar is trying to copy Gujarat’s growth narrative under the muscular leadership of Narendra Modi to position Nitish as a serious contender for the nation’s top job when the country goes to the polls in 2014.
Nitish and Modi are not the only ones who are relying on dodgy figures to showcase their stellar performance as helmsmen. Congressmen leaders, all these years, have done their bit of playing around with numbers to suit their purpose.
After all these years, there are serious issues with all kinds of data. Census figures are dodgy, we don’t have a clue on the real extent of poverty and illiteracy. The same holds true for our exports. Trade data overstated our exports by a whopping $9 billion between April and November 2011. Sugar production, too, was shown to be 13 million tonnes. Later, it was corrected to 6 million tonnes.
In July 2011, a frustrated RBI Governor, V Subbarao, who heads an institution that thrives in an environment of opaqueness, was seen wringing his hands in frustration over inaccurate data when he said, “The data on inflation and industrial output is flawed and does not reflect the correct picture.” He was of the view that poor quality of data can mislead policy calculations.
Subbarao’s public expression of frustration suggests that there is no institutional design behind cooking up the data, but is it really the truth? While incompetence and corruption may have had a role to play in putting together growth statistics, there are some manifest manipulations that governments surreptitiously engage in for either populist reasons or to satisfy multilateral agencies.
Former International Monetary fund (IMF) adviser Davison Budhoo had blamed the lending agencies for engaging in statistical fraud by manipulating the figures of countries in the throes of economic crisis, and creating circumstances for imposing its harsh conditionalities in the name of stabilising these economies. Squeezing subsidies in the name of fiscal discipline, these conditionalities took a heavy toll of these poor societies. The Eurozone countries are also being subjected to this prescription. Budhoo provides graphic details of how this statistical fraud has been perpetrated.
Similar shades of sinister manipulation of numerical data are available in the way figures of loss in the oil sector are beefed up to bolster a clamour for doing away with ‘ghost’ subsidies. Late CPI(M) leader Dipankar Banerji strenuously tried to show the trickery engaged in by the government to palm off the notional concept of ‘under recovery’ from the sale of oil and its products as losses. Anyone who spends some time on the pricing of oil would discover how a cabal of bureaucrats and sarkari economists have been taking the aam admi for a ride.
Every time people question the rise in oil prices, the government appoints a committee to look into it. Despite that, the content of the government’s argument has cussedly remained the same. There is a fear that Manmohan Singh’s government, in its endeavour to stem the economic slide, will come out with a policy prescription based on flawed data.
So, again, we would see austerity and cuts in subsidies to lower our fiscal deficit. The moot question is: is the total quantum of subsidies calculated correctly, or is it that much of it is notional and may not have reached the ground?
The kind of havoc dodgy data and statistics can wreak in the lives of the poor has to be taken seriously. Nandan Nilekani’s UADAI is a good beginning, but a lot more needs to be done to ensure that our poor do not end up as collateral damage in the designs of ambitious politicians, or, worse, diabolical bankers.