Last week, Special Public Prosecutor B.V. Acharya said that a glaring arithmetical error by the Karnataka High Court in computing the loans taken from banks by All India Anna Dravida Munnetra Kazhagam general secretary Jayalalithaa, her aides and their firms resulted in their acquittal. While it is for the highest judiciary to decide on the exactness of computation of loans and the legitimate income in Ms. Jayalalithaa’s case, it raises an interesting question about the skills journalists need to possess to evaluate facts.
One of the early lessons I learnt about being a journalist was the importance of constant skill enhancement and the need for comprehensive revision of domain expertise. Every policy change brings in its wake new terminologies and parameters. With the macroeconomic models getting sophisticated and the intertwining between economy and politics becoming more intricate, it becomes incumbent on journalists to review their knowledge, and to have mid-career refresher courses. In 1992, when the first scandal of post-liberalised India — the Harshad Mehta scam — broke, most journalists, including yours truly, were clueless on the modus operandi of that scam, and many abbreviations sounded alien. The Bombay stockbroker used a mechanism called “Ready Forward” deal (RF deal) and a financial instrument called Bank Receipt (BR) to craft his avaricious schemes. Journalists understood the full import of the deal and the instrument only when the scandal unfolded.Comprehending data collection
In this era, when data journalism is seen as a way forward to deal with difficult issues in a balanced, unbiased manner, it is important for both the readers and journalists to understand the rules that govern statistical data collection both for the financial sector as well as for the macro economy. It is vital that we do not compare apples and oranges as the official bodies entrusted with the task of compiling national data have changed their computation method for a range of purposes. Last year, we had to withdraw a story from the magazine section because it did not take into account the new computing method followed in the Union Budget following the B.K. Chaturvedi Committee recommendations. The story wrongly implied that there was a 71.4 per cent decrease in the Budget allocation to the Ministry of Human Resource Development in Budget 2014-15. In the new method, Central assistance to States and Union Territories were listed separately, a major departure from past Budget estimates.
The problem is not restricted to Budget figures alone. The Central Statistics Office (CSO) is the principal data-collecting, processing and disseminating agency responsible for coordinating, monitoring and supervising the National Statistical System. The CSO released the new growth estimate for 2014-15 of over seven per cent that was not in line with other economic indicators such as the Index of Industrial Production (IIP), which showed weakness.
The turnaround in the economic figures was largely due to a change in the base year used for computing the gross domestic product (GDP). The CSO’s estimates were with a new base year 2011-12 replacing the old 2004-05 as base. Economist C.P. Chandrasekhar wrote: “The decision to revise the base year is, of course, routine and in keeping with accepted practice. To take account of structural changes, accommodate new and better datasets, use improved methodologies of estimation and harmonise statistical practices with those adopted internationally, statistical agencies the world over update their estimates. But the scepticism expressed above arises because of the results of the exercise, especially the revised growth rates.”
He alerts the readers to be aware of the changes in data sources and methods and draws our attention to the observation of the Chief Economic Advisor of the Government of India that “this kind of surge in growth has possibly not occurred, even though there has been a recovery in growth after 2011-12.” We need to keep in mind that if we were to use the earlier base year, India’s growth decelerated after 2010-11 and if we use the new base year, it has grown in a robust manner.
The other area where the figures seem to be suspect is the Purchasing Power Parity (PPP). Jayati Ghosh in her T.G. Narayanan Memorial lecture pointed out how dramatically PPP estimates can change. For instance, the 2005 PPP-adjusted per capita income for China in dollar terms showed a 40 per cent decline compared to the 2000 estimate. “This is because the new PPP for China is estimated to be around half the nominal rate, whereas the previous estimate (dating from 1993) had suggested it was only around one fourth of the nominal rate. This downward revision of per capita income in China also adds significantly to the estimate of poverty using the standard US dollar per day definition, more than doubling the estimated number of poor people in China. In the Indian case, the PPP-adjusted income declined by nearly 30 per cent,” she said.
If data journalism has to gain gravitas, then journalists must develop skills not only to understand the quantitative methods but also grasp the qualitative inputs that go into the process of data collection.