Zemblanity- The Nemesis of Serendipity
William Boyd in his novel, Armadillo (1998), came up with an antonym for serendipity, called as zemblanity. Serendipity is the term coined by Horace Walpole in 1754 which means pleasant discoveries by chance. On the other hand, zemblanity is “the faculty of making unhappy, unlucky and expected discoveries by design.” The novel revolves around these twin poles of Serendipity and zemblanity, bringing out the contrast in our daily life that oscillates between utopian dreams and a dystopian reality.
While reviewing a book for Frontline magazine, Weapons of Math Destruction, by Cathy O’Neil, I was in a trap between serendipity and zemblanity. Dr. O’Neil started a Program in Data Journalism at Columbia. Her earlier book, in collaboration with Rachel Schutt, Doing Data Science, is one of the finest textbooks in big number-crunching. However, her latest book explains the congenital problems in dealing with big data. She, in her book, has shown an ironic relationship between the all the assumptions taken behind almost every mathematical model and the inequality they later create. The assumptions are that mathematical models would ensure greater fairness, eliminate bias, and judge by universal rules. But actually, the book shows that these assumptions based mathematic models become lethal paradigms, by being opaque and incontestable simultaneously. Reading Dr. O’Neil’s book was a moment of serendipity while the equivocating numbers were exposed.
India’s growth story
The zemblanity occurred when the Central Statistics Office (CSO) retained its January estimate for growth in gross domestic product (GDP) in 2016-17 at 7.1%. If these figures were correct, it implies that independent economic forecasters had got their estimates about the potential slowdown due to demonetisation completely wrong. This newspaper’s Editorial, “Resilience reaffirmed” (March 2, 2017), reveals the dilemma in accepting these figures at face value. It read: “The Survey asserted that the recorded GDP growth would ‘understate’ the overall impact of demonetisation as ‘the most affected parts of the economy — informal and cash based — are either not included in the national income accounts or their measurement is based on formal sector indicators.’ When dealing with statistics, it is safer to keep all the caveats in mind.”
Prime Minister Narendra Modi used these figures at an election rally at Maharajganj in Uttar Pradesh. “On the one hand are people [critics of note ban] who talk about what people at Harvard say, and on the other is a poor man’s son, who, through his sheer hard work try to improve the economy,” he said. Though it is a politically powerful rhetoric that may well resonate with the people, does it really address the problems relating to big data collection, its analysis, and the models?
More questions were raised than answered about our economic data, ever since the government decided to change the base year for GDP calculation from 2004-2005 to 2011-2012 under Prime Minister Modi’s regime. We are still not sure how this shift altered quantum of notional increase against the actual increase has in GDP. Secondly, to conduct a comparative analysis, we need data that are aligned to a set of rules and categories without introducing a new parameter. But this kind of analysis was not achievable even for the latest Budget figures.
The 2017 Budget for the first time eliminated the distinction between plan and non-plan categories. Also, the Railway Budget and the Union Budget were merged together. One has to first actively disaggregate the figures sector-wise and department-wise to compare the figures with earlier estimates and conclude some meaningful comparisons. In this context, there is no well-defined method to understand the overall impact of demonetisation on India’s growth story. According to Pronab Sen, former chief statistician of India, the informal sector in India accounts for about 45% of GDP and around 80% of employment. If this sector is not taken into account, then the metadata remains inadequate and is also observed as a deliberate move to mislead.
While we report the official figures and explanations, there is a gap in interpreting and critiquing big data. With policy decisions becoming a product of data and mathematical models, it is worth creating in-house expertise in this crucial area as the next step in public interest journalism.