When data trumps rhetoric

Data journalism, as it evolved over the last decade, has in fact reinforced the grand old dictum of C.P. Scott: "Comment is free, but facts are sacred."

February 09, 2015 01:59 am | Updated November 16, 2021 05:18 pm IST

A.S. Panneerselvan. Photo: V.V. Krishnan

A.S. Panneerselvan. Photo: V.V. Krishnan

A recent report in this newspaper, >Quotas do not hurt efficiency, says study (February 5, 2015), generated two sets of strong responses from readers. While one section felt that the report validated the affirmative actions of the government, the other section felt that it reinforced the dominant political narrative and felt that the study should not have been reported. The arguments against the article were varied: from questioning the methodology, the duration of the study, the intent of the researchers, the intent of this newspaper, to what the term ‘efficiency’ means in a government-run operation like the Indian Railways. Rhetorical flourish is not an answer to empirical evidence.

There is a schism between rhetoric and good journalism. Rhetoric is a narrative that is certain of its worldview; it has no doubts, no dilemmas, no predicaments, no ambiguities, no ambivalences and no questions. On the other hand, journalism works on the principles of interrogation, inquiry and doubt; it seeks evidence, looks for proof for every hypothesis and a model for every paradigm. Academic research and data mining are two important avenues for journalism to see whether a particular policy has worked or not and to present a clear cost-benefit analysis to readers. Data journalism, as it evolved over the last decade, has in fact reinforced the grand old dictum of C.P. Scott: “Comment is free, but facts are sacred.”

Power of data journalism The power of data journalism is that it challenges rhetoric with hard empirical evidence. In The Hindu , there are broadly three directions >data journalism tends to take: one, when the newspaper does its own number-crunching, two, when it reports on other people’s analysis of the data, and three, combinations of the two. There are clearly defined methods for each of these trajectories. The checks and balances put in place for the first approach ensure that the data source is dependable and experts approve of the robustness of the methodology. In case of complicated sets of figures, there is a constant review to see that there is internal cohesion that meets the rigour of high academic standards. When the paper reports on other people’s analysis, the senior editors review it with a fine-tooth comb. The research should be done by reputed experts and, as far as possible, published in a recognised and peer-reviewed journal. It is an attempt to bridge the knowledge gap between academia and policymakers, between cutting-edge developments and popular notions, between the projections and across various disciplines, departments and institutions. The newspaper also carries reports on research in progress, but clearly states this.

For this story, the research paper had already been published in a reputed journal, World Development , by two noted economists. The lead author, Ashwini Deshpande, has decades of experience, is a respected expert in the field, and has been widely published. There has been no academic challenge to the paper.

What triggered this research in the first place? It was two sets of conflicting rhetoric. Dr. Deshpande, Professor at the Delhi School of Economics, and Thomas Weisskopf, Professor of Economics at the University of Michigan, acknowledge in their article, “Does affirmative action reduce productivity? The case of Indian Railways” that the divergence of opinions over affirmative action triggered their study. They wrote: “Some critics have even suggested that the failure to allocate key jobs on a strictly meritocratic basis has resulted in serious injuries, as well as gross inefficiency. For example, in “Job Reservation in Railways and Accidents,” ( Indian Express , September 1990), it is charged that the frequency of Indian railway accidents would likely increase because reservation policies result in a larger proportion of less competent railway officials and lower overall staff morale. But advocates of affirmative action — in India and elsewhere — argue that hiring is otherwise often far from truly meritocratic, and that workforce diversity may actually generate efficiency gains as individuals from diverse backgrounds bring complementary skills to an organisation. To shed light on this debate, we focus on the world’s largest employer subject to affirmative action —the Indian Railways (IR), with roughly a million and a half employees — in an effort to assess the effects of job reservations for SC/STs on productive efficiency.”

Media and academia always come together in investigating stereotypes, popular myths, claims and help to piece together a string of evidences to connect the dots and make sense of our reality. Empirical evidences, statistical tools, sample surveys, mathematical models, econometric models, data collected by diverse sources are all augmented to get a picture. This picture sometimes validates the original assumptions of policymakers and at other times repudiates them. Data journalism is an important public interest device that helps to cut through the emotive obfuscation generated by competitive rhetoric. The findings of data journalism can help to effect course correction in the event of a policy implementation based on flawed assumption and strengthen the implementation process if the assumption is right. It may not serve anyone to confront data journalism with rhetoric.


The article has been corrected for a factual error.

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