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I began the introduction to my last episode by asking the question, how do we see the
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Well, I want to ask the same question today.
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How do we make sense of these complexities around us?
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One answer is that we can never truly see the world for what it is because it is so
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But the best way to get a good sense of it is through getting as much knowledge as we
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With the data we can, so we have more dots to join and hopefully we will emerge with
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a high definition picture.
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But sometimes dots aren't enough.
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How we join those dots also matters.
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Ideology can sometimes be an impediment.
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Every ideology presents simple answers to everything and simple answers are attractive.
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But when we see the world through these frames, we never quite take all the dots into account.
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That is why it is important always to be open minded, to question everything.
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The only ism that can take you to the truth is skepticism.
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Welcome to the Scene and the Unseen, our weekly podcast on economics, politics and behavioral
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Please welcome your host, Amit Barma.
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Welcome to the Scene and the Unseen.
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My guest today is Pramit Bhattacharya, a journalist who has been a pioneer of analytical journalism
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in this country or what would also be known as data journalism.
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Pramit joined Mint more than a decade ago, reported on markets, ran their edit page,
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did grassroots reporting, wrote opinion pieces and most importantly set up their data journalism
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He played an important role in the careers of two other journalists who have been on
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the show before, Rukmini S and Roshan Kishore.
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And his pioneering work will continue to have an unseen effect on a generation of journalists
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He is such a careful and meticulous thinker and also so principled in his approach to
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I enjoyed this conversation.
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I learned so much from this conversation.
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But before we begin listening to it, a quick message from the sponsor of this episode,
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which is me only, you can't get away from me.
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One of the things I've learned most over the last year and a half is sharing my insights
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on my two greatest passions, writing and podcasting.
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And I'd love to invite you to be a part of this journey.
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Registration is now open for the January cohort of the art of clear writing, where over four
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webinars on four Saturdays, I teach all I know about the art and craft of writing compelling
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prose, much interaction, many exercises.
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And at the end of it, you get to join the clear writing community, an online community
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formed by the 18 previous cohorts of this course.
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In that community, we have book clubs, workshops, writing prompts with feedback and much else.
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I am also doing a special cohort of my podcasting course, the art of podcasting, which I had
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conducted for three cohorts last year before I took a break.
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All my learnings from five years of the scene and the unseen in three webinars over three
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For more details and to sign up for my writing course, head on over to indiancut.com slash
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To sign up for my podcasting course, go to scene unseen dot i n slash learn.
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These links will also be at the bottom of the show notes.
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These are exciting times for the creator economy, and I'd love to help you be a part of it.
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Pramit, welcome to the scene and the unseen.
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I'm a fan of your work and it's really great to be on your show.
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And I'm a fan of your work as well.
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Not just, you know, reading meant and all the things that you've been doing, but I've
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had two other data journalists, Roshan Kishore and Rukmini on the show before, and they've
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So glad to finally have the OG as it were, or OP, is it this new internet lingo sometimes
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confuses me, but glad to have you on.
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You know, so before we sort of get down to talking about your field of work and all your
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insights over all these years in journalism, I'm curious to know a little bit more about
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your sort of background, like wherever you born, what were your early years life, like
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tell me a little bit about that.
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Yeah, so I was born in a small town called Tezpur, which is about 20 kilometers from
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And my father was a college teacher, my mother was a homemaker, she was a teacher earlier,
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but after marriage this thing.
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So early years were just like a typical small town existence and where everyone knew each
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other, also partly stifling because a son of a college teacher in a small town is supposed
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to behave in a certain way, right, you can't escape that.
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And so I think it was only when I went to Guwahati for my undergrad, there's this college
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called Cotton College, oldest college in the Northeast, and one of its most important culturally,
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politically, and so on.
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So for instance, the current chief minister started his political career, current chief
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minister of Assam, Hemant Bishwas Sharma, so he started his career as a student politician
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in Cotton College, Guwahati.
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And while I was there, even then he was the cabinet minister, there were at least five
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others who were ex-general secretaries of Cotton College.
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So we are all aware of the legacy and the history and so on.
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And by the second year I had moved into a hostel, the Cotton College hostel, earlier
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And when you are in a Cotton College hostel, there's no way you can avoid politics.
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So I also plunged into it.
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And like most other students of Assam, you first have your brush with student politics
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through the ASU, the All Assam Students Union, which led that famous six year long agitation
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during the time of Indira Gandhi, which finally led to the Assam Accord.
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So if you are a student in Assam, you are quite active and your voice is heard, and
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especially in a place like Guwahati and Cotton College.
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I think those years were sort of very influential in determining the sort of life I finally
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had and the career choices I made and so on in two ways.
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One was, of course, the brush with politics.
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And even at that level, you see the use of muscle power, money power, everything.
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And you see how hard it is to take an independent stand, which we did eventually.
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And the candidate we put up for election was threatened with a gun.
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And we went for a hunger strike, Gandhian sort of satyagraha, against unknown assailants.
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But we let it be known to everyone whom we knew that it was someone perhaps from the
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ASU because we had broken away from the ranks.
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And we also saw the impact of that, because after long years, ASU lost an election in
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And it was a big development.
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And the other thing that happened, I got involved in a lot of other college activities, a lot
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And I, along with a few other friends, started the first magazine of the economics department.
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I renewed a magazine that used to be there in our hostel 30 years ago.
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So we picked out a couple of pieces from the old magazine and then we started with that.
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We got a writer in Assam to sort of come and inaugurate that, a theatre, one of the leading
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theatre sort of artists in Guwahati, who was also a teacher in Cotton College to write
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for us for that, this thing.
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And we also went out to people to collect ads, like cat coaching centers and so on.
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So the entire process of preparing a magazine.
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And when that first copy comes out of your printing press, whose owner had been persuaded
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by one of our professors to produce these magazines for us at a very cheap rate.
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So we got all kinds of the entire infrastructure and everything and doing all of these things
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on our own, managing budgets, commissioning pieces, editing pieces and so on.
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So it was a great sort of learning experience.
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And then when finally it came out, the response that we got, and I had written a piece which
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was very critical of our hostel culture, you know, and the gundaism that was prevalent
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even at that time, although I didn't use the word gundaism, it was quite a harsh piece
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that we pick targets when we know that all hostel borders will go and, you know, fight
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We know what the outcome is.
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So it is not that we're doing anything brave by doing that.
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And I had expected backlash.
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Instead people from other hostels came up to me and say that, you know, what you wrote
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So I also noticed the power of words.
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And also during our political fights and so on, we realized that, for instance, during
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that hunger strike, we were not getting the kind of press support on the first day that
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And through our channels, we found out that some senior folks in ASAM had warned newspapermen
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And if you know how the system in ASAM works, a lot of journalists have also come up through
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Then we started making our own calls and used our students' family connections, their village
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connections and so on to counter and we got this thing.
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And we also got some independent interested journalists interested in our story.
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So I had been a sort of I had seen the other side of the news industry before, you know,
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I stepped into it and I knew both the weaknesses of the model of the news model as well as
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So I didn't join with any wide-eyed illusion that, you know, everything is hunky-dory and,
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you know, you get to say what you want to and so on always.
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But I also realized that, you know, this is this has some power and it can change minds.
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If you can put across your message in a convincing way, words do carry a lot of weight.
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And then I also felt that along with all that, you also need some amount of technical knowledge
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on a particular subject which you need to focus on.
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I was always interested in politics and economics.
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I had already decided to do my undergrad in economics.
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I got admission into IGIDR, which is one of the premier institutes for economics in the
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And after my IGIDR stint, I felt that I felt confident enough to, you know, I felt that
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I was good enough to even challenge, you know, what some of the economists were telling us
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about how the economy is moving, what things we should be worried about, what things we
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should not be worrying about.
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And I had also learned how economists are able to use data to game models and so on.
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So I didn't want to go into a data kind of thing.
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In fact, I walked out of, say, corporate jobs which involve data and partly because I always
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wanted to have something, some writing role kind of a thing.
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And also because of this sort of interest in to get into the real sort of this thing
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that pushed me, I think, towards journalism.
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And that's how I sort of first stepped into financial journalism.
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It's incredibly fascinating, many stands to pursue there.
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And you know, you made Cotton College Guwahati almost sound like, you know, they say that
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all British prime ministers do a PPE from Oxford or Cambridge and all of that.
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And it kind of sounds like that, that this is the whole ecosystem.
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This is where everybody comes from.
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So I'm actually going to, you know, talk about that phase in Guwahati a little bit more.
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But before I do a brief digression, I'm intrigued by what you said about how that experience
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taught you about the weaknesses and strengths of news and journalism and whatever.
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Can you elaborate a little bit on that?
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So one was this raw use of power by the dominant system.
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You know, Asu at that point in Assam politics was like what the Sangh is in India's politics
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In fact, more, because Asu had far more sort of influence and also credibility.
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Also because they led the agitation, remember, they were the original sort of revolutionaries.
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It was only those who joined the political party and became AGP who were seen discredited.
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Asu still had a lot of weight, even though stories about corruption and Asu had begun
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sort of leaking out and there were journalists who were writing about it.
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So one was that the other part was that, see, we grew up in a time of militancy.
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For the first few years of my, almost till cotton college, I've never celebrated an
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That day is a day of protest in Assam.
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I mean, when we were going for our generation.
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So for everything, there were two sides.
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There was an independence movement, sub nationalist, secessionist movement in Assam, which had
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their own versions, own history, own story.
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And there was the nationalist India mainstream story.
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And even if there's a conflict somewhere, some shots are fired, something happens, someone
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loses his life, the SMS newspaper will have, or some SMS newspapers will carry one version,
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which is most likely the alpha version.
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And the English newspapers will, on the same story, have a completely different version,
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which will most likely be the army version.
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We knew from experience and from, you know, things that happened locally, that neither
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So what was true would not necessarily come out in the newspaper in any form.
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And one had to live with it.
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You couldn't do anything.
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Even if you wrote a letter to the editor, it was very unlikely that that would get published
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When I was in cotton college, then of course we had the, you know, if you write as a group
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of cottonians to an editor, it is very unlikely to refuse.
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So if something happens to a student from an elite institution and so on, then it's
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But something happens in a remote village, but which is maybe not too far from Tehsipur.
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I know someone there and I know what the story was that will not get published in a newspaper
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So I saw what, how news, much of news, especially conflict reporting is sort of distorted news.
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I was very aware of that and which is why when people say that, you know, the future
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of news is local news and hyperlocal news and that will be great, there won't be pulse
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I have never bought that because I can see how parochial it is.
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And this came up even during my reporting in Maharashtra when I used to go from Bombay
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to a local place, say that was a constituency of a big cabinet minister.
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The local reporter would tell me everything that had gone wrong in the constituency.
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But when I asked him how much of it have you reported in your own writing, they would say
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Because the local cabinet minister is also the chief ad buyer of the local newspaper.
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So it's also the revenue model that works in a certain way.
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So at different places, different systems provide money for news agencies and newspapers
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So there were rumors that time never proved that even Indian intelligence agencies paid
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money to ensure that news about conflicts and so on was presented in a certain way.
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These things can be proved.
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At least I didn't have the resources or this thing to pursue this.
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But there were other incidents, secret killings for instance, there are books now on that
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dark sort of episode in Assam's history.
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So when you grow up in a place with such violence, such extreme events and such distorted representation
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of such events and when you see even the mainstream media, the so-called national media, completely
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oblivious or completely ignorant of those, you just feel that there's no one really interested
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The reality is far more complex.
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But as a young sort of student or child, you feel that no one is just bothered with it.
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Then as you grow up, you realize that what are the hurdles or barriers that prevent us
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from reaching the truth, even if we are sincere as journalists.
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So that makes you more aware of the nuances and complexities.
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But I didn't start out from a position where I respected all the great institutions of
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I had great respect for the army.
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I had great respect for the national TV channels or the national newspapers and so on.
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At the same time, I did not have great respect for the local sort of this thing also.
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So it was a position of deep skepticism and maybe that's why I became a journalist, you
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know, that here we can question everything and although we can't probably say everything
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that needs to be said and should be said, we can make an effort and gradually lower
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those hurdles and come to a bit closer to the truth.
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So you know, I'm also coming from like a position of deep skepticism on some things.
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And one of the things that kind of strikes me is that all of this would make me wonder
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and this is something I still wonder whether there is any point to the pursuit of the truth
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that journalists like you and me would want to believe in, because on the one hand, when
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you talk about the news media there, on one hand, it's a there's a narrative impulse.
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You know, you're with one side or the other and there is that impulse to push that kind
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And you don't really want to report anything else.
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And on the other hand, there is also the business impulse, you know, where, you know, you got
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to do what you got to do to stay profitable as a business.
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And if somebody is your big advertiser, you're obviously not going to piss them off.
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And these two things alone would make me skeptical.
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But what has made me more skeptical in recent times is the fact that the common person doesn't
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seem to care about the truth even.
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They are happy, you know, they pick a side and they'll go with whatever narrative chooses
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fits the vision of the world that they've chosen for themselves.
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And they'll just go with that.
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And you know, the question then comes as does the truth make a difference?
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Do people even really care?
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Which is an obviously it's an open question for myself, because, you know, what I would,
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you know, I would if I thought truth didn't matter, I would stop doing this podcast.
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Why not just choose your delusion as it were and kind of live with it?
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So did these processes cross your mind in the sense that what you have kind of done
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in journalism, the route that you've taken is not just in a broad sense, trying to figure
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the truth out, but trying to do it with data, trying to do it with granularity and trying
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to get as accurate as you can and, you know, peel away the layers of narrative and obfuscation
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that might otherwise lie behind events or processes.
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So you know, have you ever been struck by this sort of skepticism that what's the freaking
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You know, I can bring up so much data about how the economy has done this quarter, but
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people will believe whatever the hell they want to believe.
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So those kinds of doubts occur at least four to five times every month.
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But see, one always then reasons that maybe there's a male from someone, you know, who
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understands and who sort of pushes you.
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So if that kind of feedback also stopped, then yes, I would be completely disillusioned.
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But over the years, I have always, as I said, and from right from my student life, you know,
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even when I wrote things that I thought will provoke people, make them angry, I would lose
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friendships because of what I wrote.
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And later on, it was not just not about friendships, of course, I was talking about powerful people
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and, you know, they were not my friends in any way.
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But say when I was writing about economics and I was criticizing what economists were
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doing and many of my friends are economists, you know, now they're senior economists because
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they they're all batchmates or juniors or seniors from the same institution.
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But they take it in a very, I should say, sporting manner.
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And sometimes gently they will get back to you and say, hey, you know, you are stretching
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this point a bit too much and so on.
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But they engage and they're willing to acknowledge that, you know, some things are not right
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or could be better and some kind of models should be rethought and so on.
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So had those kind of reasonable, reasoned responses to my work not been there, I would
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definitely have been or maybe I would have left the business of writing altogether.
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But thankfully that has not happened.
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Maybe I'm lucky in that respect.
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And so that is what sort of helps me continue despite those doubts.
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Yeah, you know, I totally vibed with that because while, for example, you know, the
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scene on the on scene started as a labor of love, the engagement from people has been
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so deep that sometimes I feel I'm just doing it for that, that, you know, that people care
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so much and I can't let them down.
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So every Monday there's got to be a good episode and I have to do it with full intensity.
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Another question that arises from what you just said, you know, how all your economist
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friends will once in a while gently tell you, what is this you have written and all that.
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And one of the laments that one has about the media, which I guess is a universal lament
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that one can't help, is that too many journalists are generalists, especially so in these times
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where budgets and all are being cut.
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And of course you want one, but I want to sort of just ask a broader question that what
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happens when journalists are generalists and one day you're reporting on X subject, another
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day you're reporting on Y subject, is that invariably, you know, the experts of the subject
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will be disappointed because they will think, oh, you know, like whenever I read an article
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written on say podcasting or poker, you know, sometimes some journalists put in a lot of
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rigor and do a good job.
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But most of the time I'll be like, this person doesn't know what he or she is talking about.
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So even for a good journalist who is a semi-expert, who might have an economics degree like you,
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even when you are reporting on say the way that the RBI does a particular thing, someone
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from inside the RBI could easily say that, hey, you don't know the tugs and pressures
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on us and all the different things we have to consider.
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So how do you sort of weigh this?
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Because on the one hand, you want to do justice to whatever the subject is.
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You might even, I don't know if it happened to you, but you might even at some point suffer
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from the imposter syndrome and say, should I really be writing about this?
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And at the same time, you know, all these specialist friends of yours, economists or
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whatever will often make valid points, you didn't consider this.
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So you know, and yet you know that it is better that you do what you're doing than do nothing
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at all, or then let somebody else who is completely untrained come at it with a random narrative
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So it's, in a sense, God's work you're doing, though I'm an atheist, but in a sense, it's
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important work you're doing, it should be done.
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But at the same time, you know, you know that you can never, you'll never satisfy the experts.
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So how do you deal with this?
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As far as, so I, the way I approached it and thankfully I had good mentorship from people
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who were equally respected by experts and so on.
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So they always told me that a good story should be accessible to everyone.
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So a story on economy, even a 10th class student should be able to understand it, but it should
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not disappoint the experts.
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Wow, that's a high bar.
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Yeah, that's a high bar and I don't think in 100% of my stories I would have met it,
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but I try, I mean, that is the ideal.
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So and there are some genuine cases where there is a lot of uncertainty and in economic
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sort of reporting, it will always be there.
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And it's a question of which sides the majority of the consensus is.
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Sometimes there's no consensus.
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In those cases, it is always best to tell your readers so upfront that there is no consensus.
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This is what you think based on all the evidence you have been able to collect, all the data
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you've seen, all the experts you've spoken to, all the research you've read.
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And after that, you basically leave it to the intelligence of the reader to figure out.
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Even after all these processes, you must still make mistakes.
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The thing is to be humble and next time when you approach that subject or any other subject
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to be more careful and weave that in.
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So I have made mistakes and I've learned from them.
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And when someone tells me that, hey, you could have looked at this way, I engage with them.
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So and over a period of time, I have been able to handle those criticisms, I think more
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There were points when I had fights also with my professors and I said, no, you're wrong.
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I know this, this, this, this happened and this is how it worked out.
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And so this is why I'm correct.
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With hindsight, I would have dealt with those sort of arguments much more maturely.
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But the good thing is they did not feel that way that I was being too harsh.
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So they also were patient.
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So I think I've been sort of lucky that I got a lot of good feedback from good people.
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And especially in my early years, that helped me a lot.
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You know, it helped me improve and even now it helps me improve.
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So I want to kind of go back to Assam for a bit.
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And I was in any case very intrigued by your sort of growing up in Guwahati.
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Because you know, people who grow up in sort of the mainstream of the country, the big
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cities like Delhi and Bombay and whatever, India is one thing to them.
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And I realized that like even from the kind of privileged bubble that I grew up in, that
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it takes a while for you to really see the world and the layers peel off and over a period
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of time, you begin to realize that, you know, there are so many nuances and complexities
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about the country that you've missed.
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And one of those has also to do with the idea of India in the sense that not in the sense,
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you know, Sunil Khaldani may speak of it or as is the current debate today about inclusiveness
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and secularism and all that.
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Not in that sense, but just the idea of India as one geographical union as it is.
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Like one thing I realized while, you know, reading up on that period, I did an episode
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with Narayanini Basu on VP Menon who helped Siddharth Patel kind of integrate all the
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states is that that process of integrating the states and building this union and making
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sure the center held seemed to me at one level to be like a kind of fast track colonization
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what the British took 250 years to do.
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These guys sitting in Delhi kind of managed to do in a few months through threats, through
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coercion, through promises that were later broken and all of that.
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And that gives me unease.
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Now, I love this country.
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I call myself definitely not a nationalist, but definitely a patriot, a distinction Ram
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So I love this country.
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I'm glad that we are what we are.
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But I sometimes wonder about the road we took to get here.
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And that I imagine would especially be true for someone say in Kashmir, in the Northeast,
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even in Assam to some extent, because like so many Kashmiris, given what we have done
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there, would in a sense, it would be understandable for them to look at the Union of India as
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our freedom fighters must have looked at the British, you know, and it would be understandable.
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I'm not saying right or wrong.
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I don't want to go there, but it would be understandable for them to look at the Indian
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Army as an occupying force with all its oppressions and everything.
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And so what what was that sense like in Assam, like did you have a different sense of India?
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Did you have these competing sort of senses of India in your head, this sub nationalism
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You know, how much of a force was that?
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Because we we wear many caps, right, we'll wear an India cap, we'll wear an Assam cap,
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we'll wear our community cap, we'll wear our small town cap.
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And the world looks completely different if you just change one cap to another.
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So tell me a little bit about how your thinking evolved.
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Yeah, so just because you're talking about caps, I'll respond to that.
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So the sub nationalist cap is quite important, and if you notice the headgear Modi wears
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when he goes for his rallies in Assam is very specific to that place.
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And so he may refuse to wear whatever, a Muslim headgear or something, but he will not refuse
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to wear that Assamese headgear that people wear during farming, actually.
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But now it's a mark of respect and so on.
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So so yeah, so so that is very important.
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And even today it is important, but it is not as important today as it was when we were
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So now Assam is much more integrated in that sense with the rest of the country.
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Maybe Manipur isn't, maybe Nagaland is not to that extent.
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But the rest of the northern states are much more integrated than they were 20 years ago.
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And on balance, I think it's a good thing.
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So yes, they were competing visions, they were competing sort of ideologies about what
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it means to be independent, what it means to be in India.
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And they were competing visions even within Assam.
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So it was the dominant Assamese middle class that sort of provided intellectual material
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financial support for the secessionist movement.
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Let us not forget that the tribals had their own ideas.
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The linguistic minorities, the Bengalis, the community I belong to had their own ideas.
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We actually wanted to celebrate Independence Day because we were refugees from East Pakistan
#
or at least we are descendants of those.
#
So coming to India was an active choice.
#
And although unlike in the case of refugees from West Pakistan, there wasn't much done
#
to alleviate the distress of refugees from this side.
#
And we're left to the mercy of natural forces.
#
And Bengalis also had to suffer during the anti-outsider agitation.
#
But still, this was preferable to what was happening even before 1971 in East Pakistan
#
in the lead up to the creation of Bangladesh.
#
So when you look at the counterfactual A that is there, B, let us look at the Assamese position
#
because see, most of my friends are Assamese, so it's not that they are my class enemies
#
In fact, I did appreciate even then the Assamese position and they are right to be aggrieved
#
about the way the Indian state came into being, the way a sort of quasi-colonial mentality
#
still pervades in many things.
#
I don't know if you're aware of this, but so central government employees have some
#
kind of a hardship allowance if they go and serve in the North.
#
Wow, that's so convincing.
#
In certain institutions, if you serve for five years, then for the rest of your life,
#
you don't have to go there.
#
So people do it early in their career and then before they get married or whatever,
#
So they actually see it as a punishment kind of a thing before they go to more established
#
So maybe the initial policy was to encourage people to go.
#
I don't know what are the origins of that.
#
But right now, it is perceived in a very different way.
#
And so there are many of these things, which is why there was a recent controversy where
#
I think Himanta also stepped in where a contestant from Assam in a reality show, she was speaking
#
in Chinese and someone said something, a judge.
#
But the judge, the full conversation was not shown.
#
Only that part was shown.
#
And it immediately created controversy because, you know, people didn't give the benefit of
#
And there are historical reasons why this is true.
#
And even current, I mean, even few years back, the parts of Delhi were very unsafe for people
#
So all these things are also theirs.
#
But if you extend the logic of the cessationists and the earlier subnationalists, there's no
#
way Assam would have remained independent for long, even if they had managed to get
#
it, suppose, or say Nagaland or eventually it would have been cobbled up by China.
#
So then you would not even be able to voice whatever grievances you have, you know, the
#
kind of things I'm saying now in your podcast and which will be published, you know, I can
#
I can't do this in China or much of my journalism.
#
So while I do have a lot of, while I criticize the way Indian state policy function in various
#
ways, including its policy in the North East and how wrong it was and how it wronged people,
#
I also feel that this was the least worst thing that could have happened.
#
All the other choices were much worse.
#
So in this period of time, what I was also very intrigued by was, you know, when you
#
think of a data journalist, right, you think of a numbers nerd, Joe, you know, the kind
#
of guy who might get bullied in his hostel or the kind of introverted person and always,
#
you know, studying algebra and all of those things, and generally a numbers nerd kind
#
But when I chatted with, you know, Roshan Kishore, who's worked with you before, and
#
he had a rich history of politics in the JNU student hostel.
#
And now you also talk about your deep interest in politics, quite obviously, you know, during
#
your college years, that really intrigues me.
#
So what, what was your notion, you know, growing up at that time, and I guess this would have
#
been early 2000s when you were in college.
#
Yes, 2005 to 2008, I was in my undergrad.
#
You were in your undergrad.
#
So in those days, what was your perception of yourself in the sense, what was your idea
#
Like what, what did you want to be?
#
Did you have a path stretched out?
#
What were the things that you were passionate about at that age?
#
So I didn't have very clear ideas about that this is what, but I knew what my interests
#
So even before I went to cotton college, I knew I was clearly interested in economics.
#
And as I began studying economics, I got interested in statistics.
#
I mean, I had a statistics as a subject and in Guwahati University, the other courses
#
are not that well developed, but statistics historically has been a prominent subject.
#
And so I really enjoyed my classes, stats classes.
#
I performed well in my stats courses as well, although it was a minor subject.
#
My major was in economics and economics I was always interested in.
#
I was interested in writing and I saw, you know, the impact it had.
#
And I was also interested in politics.
#
But towards the end of my student cotton life, I realized that I couldn't make a career out
#
I mean, in an idealized sort of student campus, even there, we struggle to sort of get away
#
The dirtier sort of aspects of politics will definitely have the upper hand.
#
So A, that was one, B, I also felt that, you know, there's a broader politics that we all
#
take part in, whether we are academics or journalists, or, you know, even even as bureaucrats
#
or any anyone in any way connected with public policy.
#
And maybe the influence would be less, but over a period of time, maybe one could move
#
the needle a bit and maybe help others do better along that path and so on.
#
So I had very sort of vague ideas around that time.
#
And I also had a sort of notion that I was very happy with my statistics training, but
#
I was not very happy with my economics training.
#
I had great teachers and they said that you should go to a place like IJDR and so on.
#
Without them, I would not have come here.
#
So no complaints on that front.
#
And they were very clear that we can't give you, you know, all the things that you need.
#
So for instance, if you want to do economic modeling, well, you need to handle software,
#
you know, and IJDR, we got that our very first econometrics teacher taught us coding.
#
So those are things that I picked up here and I'm grateful for both.
#
I think all the years in cotton helped me sort of figure out which are the possible
#
And IJDR gave me that solid footing, you know, where I could specialize and pick one aspect.
#
And as I said, I knew I couldn't succeed in politics anyway.
#
The other part was I wanted to write, so I wanted something around that.
#
And the third was I had a sort of self-doubt and I wanted to see whether I could succeed
#
as a corporate economist or an analyst or something of that.
#
So I did an internship with Citi in my first year.
#
Much of it was just coding and I realized I didn't enjoy it.
#
So it was the final proof that, okay, fine, I can go for an unconventional career.
#
My brother had an established corporate career by that point.
#
So I didn't have any family pressure to sort of pursue necessarily the most lucrative option,
#
which in Nigeria is basically that you sit for placement.
#
So I didn't sit for placement and I searched for a job.
#
I mean, today it seems that I'm a successful journalist, but it was a bit difficult to
#
get a break when I first started out.
#
Mint tried me out and they said, fine, you can come along.
#
So that's how it happened.
#
I don't think everything was planned in advance or I had a concrete plan through which I just
#
knew discovered my interests and what I was good at, what I was bad at, my strengths and
#
limitations in a very real way during those cotton college experiences, because dealing
#
with so many people, so many tumultuous events in your hostel, in your college, it gives
#
you a sense of, okay, this is what you're good at, this is what you're not.
#
So that I think helped me decide.
#
And I was also struck by something you said during lunch where you mentioned that had
#
your brother not had a corporate career, you wouldn't have been a journalist.
#
And reason number one is, of course, you would have been under pressure to take up a more
#
lucrative career where you could look after your parents, but also your first job in Bombay,
#
he helped you pay the deposit because obviously he earned a corporate salary as it were, which
#
is again, just a role of happenstance.
#
Like somewhere in a parallel universe, you are doing something else entirely because
#
your brother did something else entirely, which is so fascinating.
#
I want to double click on a couple of thoughts that struck me while you were talking about
#
your journey in college.
#
And one of the things you said was that you realized at one point that even though you
#
were very interested in politics at the college level, you realized at one point that it becomes
#
a different beast when you're out in the wider world and it's not for you.
#
And there are other interests you have, so you go on to do them.
#
And I wonder then that if in our politics, there is a process of self-selection that
#
weeds out the best and keeps the worst, because then the guys who remain are the people who
#
have the kind of skills at playing those dirty games which you are required to play to rise
#
up in actual real world politics outside the college campus on one hand.
#
And the other way this effect would work is that if you are good at something, like in
#
your case it would be you have a deep passion for economics and writing and all of that
#
and you decide to pursue that.
#
Somebody else could be, you know, could say, hey, I'm smart enough to do an MBA and become
#
a banker, whatever it is.
#
If you're good at something, that is a much safer option and you won't go the political
#
So in a sense, is that selection process guaranteed to bring a particular kind of person into
#
politics and does that become a problem?
#
And I'm not understating the skills involved in actual politics.
#
You require tremendous skills, a certain kind of street smartness, a certain kind of project
#
So all of that, I totally have huge respect for politicians.
#
But otherwise, do you think in terms of, you know, the things we just mentioned that do
#
you think there's a selection process that selects a particular kind of person?
#
Unless you're born into a political family, unless you're really privileged or unless
#
India's per capita income moves to that level where the average person or the average middle
#
class person can also afford to take such risks.
#
At the end of the day, it's a huge risk.
#
So we at least were not courageous enough to pursue those risks.
#
So I think I don't know if that answers your question, but it kind of does.
#
I mean, it would indicate that there's a selection process of circumstance also there, that if
#
you are, you know, if you have the privilege that you don't need to have a regular income
#
and you can follow your interest in politics, as it were, then you go in for it.
#
But if you're not from that background, you are automatically, you're not going to do
#
You know, unlike, you know, earlier, we brought up the whole thing of British prime ministers
#
doing PPEs from Oxford and Cambridge and all.
#
And there is a completely different scenario where there is a perfectly respectable thing
#
for someone who's done his PPE and, you know, could get a job in any bank in the country,
#
for example, to say, no, I'll be a politician instead and it's not what it is.
#
And I think India's political system probably has a lot to do with that as well.
#
The other thing that I'm struck by is you mentioned how, you know, you edited your hostel
#
magazine Jowar and you were into writing as well and all of that.
#
And you also mentioned earlier that your dad was a teacher and your mom had also been a
#
teacher before she chose to become a homemaker.
#
So how much of an influence did books have in your growing up?
#
Like what kind of books were you reading when you were growing up?
#
Was there a sense of wanting to be a writer even outside of the context of economics and
#
journalism and all of that?
#
You know, tell me about this sort of relationship with words, as it were.
#
So I definitely read a lot.
#
And as I said, when you are in Tezpur, then you can't do kind of things which I did in
#
You have to be, you know, you have to behave in a certain very straight jacketed way.
#
So then you spend more time at home and then your home is filled with books because your
#
father is a teacher, mother is an ex-teacher and they both read, they still read a lot.
#
And you absorb, I mean, and it was not any planned reading.
#
You pick up whatever it is.
#
Sometimes my father would say that you won't understand this is not for your age.
#
And in fact, I would pick up those books.
#
And the other thing was newspapers.
#
So we always had more than one newspaper in the house.
#
We had different magazines.
#
And in fact, before my high school leaving exam, someone asked my mother, how is Pramit
#
And I was just thinking, my mother can be very sarcastic if she wants to.
#
So she said that, see, if they ask questions about what India today is writing, what frontline
#
is writing and so on, you will get 100 on 100.
#
But the problem is that the examiners don't know that they should ask this question.
#
So I'm not sure what Pramit will do with the real questions he has to face in the exam.
#
So yeah, so both those kinds of nonfiction writing and in books, it was largely fiction.
#
I don't think I read that much of nonfiction at that age.
#
And maybe I read many books that usually people of that age don't read.
#
And yes, there was a, this is very strange.
#
There's a phase where I thought I was a communist because I had read Maxim Gorky's mother.
#
And then the next year I decided I had become a capitalist because I think I read Ayn Rand
#
And later on thinking about this, it seems that I was just silly and stupid, you know.
#
I'm going off in one direction and weaving this thing of how I'll bring a revolution
#
and the next year I'm going in completely different.
#
But in general, I think it just shows that I have always been very skeptical of any one
#
Or even if I flirt with an idea or toy with a sort of ideology for some time, it has not
#
stuck really for a long time.
#
And the Cornwall College thing, we were an independent group.
#
That was the reason we existed and that was the reason others were attracted to us.
#
We were not part of any political grouping, neither ASO nor NSUI.
#
So I think, yeah, books were there and also books led to certain thoughts.
#
And books, politics, culture, all of this sort of combined.
#
Economics, I think, came a bit later, maybe when I was around class nine.
#
It is interest in politics, actually, that led to interest when I realized that many
#
of the questions, conflicts that I was seeing around myself were related to land, were related
#
to jobs, the lack of industrialization of the state.
#
That's when I started sort of thinking about this a bit more harder and feeling that I
#
need to know and understand this economic aspects a bit more.
#
And even while reading newspapers, I felt that I didn't understand the economic op-eds
#
So that was also an urge that I should be able to understand everything in a newspaper.
#
I didn't feel that the economists were writing in a very jagan field or this thing.
#
I thought that was the standard.
#
Now I know that the writing could also have been better.
#
But at that point, I felt that I need to crack this.
#
So that also, I think, led me to economics.
#
That's very fascinating because I went through a similar process.
#
There was a period in time where I would carry marks in my college rucksack and I thought
#
I was a communist and then you go in different directions.
#
And this is something that kind of interests me about how one forms one's views of the
#
I kind of grew up in the 80s and early 90s.
#
So a significant chunk of my life was, in fact, pre-liberalization.
#
And you don't have access to the internet or to so many different kinds of views from
#
across the world and all of that.
#
And I was probably lucky that I didn't study economics in college because I couldn't be
#
brainwashed into one way of thinking.
#
So I was kind of completely open.
#
But by the time I began to form my views of the world and began to figure things out for
#
myself, I was well into adulthood.
#
So what was that formation like?
#
I don't just mean in terms of ideology, because as you've just mentioned, you could be skeptical
#
about all ideologies and not fall into the sway of either of them, alluring as they might
#
be because they seem to offer simple explanations for the way the world works.
#
But even if you're questioning them, everyone has a lens through which they view the world,
#
which could be a complex lens with many factors to it.
#
And it evolves over time where you build your judgment and which help you see what the world
#
So what was it like for you to sort of build that lens through which you could look at
#
And this must be, I suppose, a process doubly complicated because there are all these old
#
dogmas that we've kind of which are there in the environment, including, I suppose,
#
if you're studying economics from your colleges or whatever, the received wisdom, the notion,
#
the things you take for granted till you learn to question them.
#
So what was this process of forming your view of the world and the way that you view things
#
In the early years, it was largely questions around politics that dominated.
#
So I didn't think that hard about economics, economic ideologies, apart from the brief
#
phase from class eight to class nine, which some people might feel that it's a very early
#
phase to think about these things.
#
But it happened with me.
#
So after that, it was just trying to understand and studying it as a subject and so on.
#
And I got also very interested in statistics.
#
In Assam, you can take statistics as a subject in class 11 also.
#
At that time, it was there.
#
And as I said, there's a good tradition of that.
#
And in my college, it was also, again, questions on politics that dominated.
#
And I didn't form a particular worldview other than being anti-ASU in my final years.
#
When I moved to IJDR, I also had a sort of distance from Assam in that sense.
#
And IJDR is exactly the opposite of quantum college.
#
So I did get admission into JNU also.
#
I didn't want to go there because I didn't want an extension of the same life I was having.
#
So it was conscious choice as well.
#
And my parents were also, of course, very happy because they were tired of all this
#
So there's my picture in some protest.
#
Or someone would call them and say that, you know, they've seen me holding a placard.
#
The rabble browser data scientist, who would have thunk, man?
#
So in IJDR, I began sort of questioning myself and what do I really believe in.
#
And even then, there were sort of people who were left-leaning Canadians.
#
And there were people who were quite market-leaning, if not market fundamentalists.
#
In fact, we had two macro courses in the first year.
#
Macro one was by a Canadian.
#
Macro two was by an anti-Canadian.
#
And this happened throughout.
#
In my final semester, in the morning, we had a lecture by someone who told us, in the first
#
class, you have to unlearn a lot of what you have learned from your other professors.
#
And he made us do surveys.
#
He was very interested in this ground level.
#
Even now, a lot of my understanding of survey comes because I actually did a fairly intensive
#
survey, explained from pilots, redoing questionnaires and everything.
#
So that was in the morning.
#
In the evening, we were in a finance class where we were studying stock markets, how
#
to value assets, bond pricing methods, and so on.
#
So it was just a contrast.
#
And I grappled with these questions.
#
I had debates with my professors.
#
The good thing about IGI there is professors gave you a lot of time.
#
The student quality teacher was very favorable.
#
And we were a batch of 20 or something.
#
And there were 30, I mean, including junior and senior, there must have been more than
#
20 professors for sure.
#
And so we got a lot of time.
#
You could just mail someone that I want to drop in.
#
Like later when I want to study abroad, you know, you had to take appointments for fixed
#
That was not the case there.
#
And you could discuss random things, including these things, cold war, economic ideology,
#
liberalization, how that has helped or harmed and so on.
#
So a lot of my ideas got sort of...
#
And in that sense, I feel IGI there was a very heterodox place.
#
And this I'm saying with the benefit of hindsight.
#
At that point, it didn't feel that way.
#
I felt at that point that IGI there was perhaps a bit too right leaning, right leaning not
#
in the social or political sense, but in the economic sense.
#
And it should offer more space to left leaning thought.
#
So I did graduate out with that view.
#
And the second thing that shaped my worldview was the Lehman crash, because I entered the
#
And I remember when Lehman crashed that day, one of the professors came and said in class
#
that this is a crazy day, but this is also a great day for economists, because now we'll
#
So it's a natural experiment.
#
And that, if you remember, it led to a sort of student movement, at least in the West,
#
where they demanded changes in the economics curriculum.
#
People walked out of the classes of Mankiw, who was seen as a conservative economist in
#
So nothing of that scale happened here.
#
But within the student community, there was a lot of discontentment about the models we
#
are taught, about some of the neoclassical assumptions, about not being taught very more
#
So I did feel that we need exposure to more different and diverse views.
#
But somehow I never transitioned into the left camp, if I may put it that way.
#
On balance, I still felt that the market approach worked best, at least as far as production
#
When it came to distribution, I did favor a heavy role of the state, especially in education
#
and health care, especially in a country like India, and seeing what I had seen.
#
But other than that, I did think there was a big role for market forces.
#
Maybe I myself was a beneficiary of liberalization in many ways, and I couldn't deny that.
#
And gradually, I have sort of rebalanced.
#
I think I have over the years begun to lean a bit more on the right.
#
That also happened after my King's education.
#
Because at that point, I was contemplating giving up economics writing and the pursuit
#
of this sort of tracking the economy or tracking what was happening there, and do something
#
And I wanted this interdisciplinary course that they offered.
#
It was like an academic sabbatical.
#
And I got a fellowship, and I did a thesis which was on political economy, political
#
And nutrition I was already interested in, I had got a previous fellowship from Save
#
the Children and Hindustan Times to cover nutrition.
#
So I wanted to understand the right to food movement and its reception in Delhi, how bureaucrats,
#
planning commission, etc.
#
That time planning commission was still there.
#
I mean, not while I was doing it, but when the right to food movement went strong.
#
And there were conflicts between them, between finance ministry and them.
#
So I tried to sort of make sense of that.
#
And during our coursework and everything, I was exposed to readings in sociology, in
#
political science, across the social sciences.
#
And I had come with a very wide-eyed sort of admiration for other social sciences.
#
I thought that, you know, economists were this, you know, strange guys who just relied
#
on modeling, didn't actually know what was happening in the real world, whereas other
#
social scientists were more closer to reality.
#
And I discovered it was not the case.
#
And I discovered that it was partly the methodological sort of imperatives of economists, including
#
the attention to data, that actually helped economics stay close to reality.
#
And a left-leaning economist and a right-leaning economist can still argue and still agree
#
on the method, even if they disagree with the conclusion.
#
That is very hard to achieve in sociology or political science.
#
The level of debate and engagement is far lesser.
#
There are these schools of thought around a particular individual and his work.
#
And it is very hard to come out of that unless that person dies or retires or holds no considerable
#
So I came out of that economics distrust.
#
And I had much more sympathy now for my former professors and for my friends who are still
#
And that's when I decided to return to sort of data journalism, pursue the study of the
#
economy in a much more rigorous fashion.
#
And at the same time, I won't say that I didn't learn anything from social, there is nothing
#
that economists have to learn.
#
Economists do have a lot to learn from other social sciences, and so do journalists.
#
But just that I felt, A, my comparative advantage lay here.
#
I couldn't possibly do much there.
#
And it is easier for you to establish yourself there.
#
And it is also even generally it is easier for economics and economists to accept new
#
ideas to absorb new ideas, then perhaps it is the case for most other.
#
So just to give a simple example of this, I don't know if you remember this famous
#
Reinhart-Rugoff paper on public debt.
#
And it was a conservative sort of view that if public debt to GDP crosses 90%, that is
#
the threshold they identified, the country is in trouble.
#
Now, a few years back, a graduate student in I think Amherst University or somewhere
#
asked them for the data, found a big mistake in their Excel calculations and also some
#
of their modeling work.
#
The Excel of course got much more media attention because it was like these two great economists
#
And the whole debate shifted after that.
#
People accepted that there may not be such an arbitrary threshold.
#
Although public debt to GDP ratio is something that is worth tracking.
#
And beyond a point, it may cause trouble for an economy.
#
There may not be a 90% kind of an arbitrary threshold for all economies.
#
It depends on the context, it depends on various other forces, including geopolitical forces
#
that determines whether it will land into a debt trap or not.
#
And that told me, it was around the time I was contemplating all these things.
#
And that told me that, look, it is unimaginable that a graduate student in any other discipline,
#
at least in social sciences, will be able to challenge one of the leading experts in
#
No, that's not going to happen.
#
So there is more openness here than I was willing to previously admit.
#
So that sort of brought me back into the fold, as you may say.
#
You mentioned sort of Keynesians and anti-Keynesians.
#
And that reminds me of this great rap video, Keynes versus Hayek.
#
Have you seen it on YouTube?
#
I'll put a link to it on the show notes.
#
It's basically Keynes and Hayek battling in rap against each other.
#
And the lyrics are written by someone who's been a guest on the show, Russell Roberts,
#
So Russ has done the lyrics for that.
#
I'll link it from the show notes.
#
And you also mentioned about how the influence of someone may be so strong that until he
#
dies, the three words you used, it may remain that way.
#
And this reminded me of the quote about how paradigms change one funeral at a time, which
#
You can't change people's minds, but you can educate a new generation of people, hopefully.
#
Something else you said reminded me of a quote that seems completely unconnected from all
#
And I'm going to read it out is by the great writer Italo Calvino.
#
And he's got this book called The Literature Machine.
#
And he has an essay in that called Philosophy and Literature.
#
So I just want to read out a little bit of it.
#
It may seem to have no relevance to what you do, but I think it actually has a lot of relevance.
#
And Calvino writes, quote, philosophy and literature are embattled adversaries.
#
The eyes of philosophers see through the opaqueness of the world, eliminate the flesh of it, reduce
#
a variety of existing things to a spider's web of relationships between general ideas,
#
and fix the rules according to which a finite number of pawns moving on a chessboard exhaust
#
a number of combinations that may even be infinite.
#
Along come the writers and replace the abstract chessmen with kings and queens, knights and
#
castles, all with a name, a particular shape, and a series of attributes, royal equine or
#
ecclesiastical, I don't know how to pronounce it.
#
Instead of a chessboard, they roll out great dusty battlefields or stormy seas.
#
So at this point, the rules of the game are turned topsy turvy, revealing an order of
#
things quite different from that of the philosophers, or rather, the people who discover these new
#
rules of the game are once again the philosophers, who dash back to demonstrate that this operation
#
wrought by the writers can be reduced to the terms of one of their own operations, and
#
that the particular castles and bishops were nothing but general ideas in disguise.
#
And this seems to be two interesting approaches of the world, which are not contradictory,
#
which can come together, but the philosopher's approach that he describes seems to be to
#
get meta, to look at a particular subject and figure out general rules, like philosopher
#
economists may talk about, say, supply and demand and how they behave in particular ways,
#
or they might talk about how monetary supply you increase that and you might have inflation
#
or whatever, you have these broad general meta rules.
#
And at the same time, the literature people, as it were, would be people who are looking
#
more closely at concrete immediate things that are happening, perhaps people working
#
in data journalism like you, who are actually looking at all the data at minute level, at
#
a local level, fleshing out the general picture that the philosophers, so to say, may come
#
So here I find when one thinks of economics, when one writes about economics, you have
#
these two frames that are operating and one frame is a meta frame where you have rules
#
of the game and you can disagree about rules of the game depending on whether Keynes or
#
Hayek or whatever, but you have a meta frame.
#
And the other frame is this complex, messy world where nothing seems to fit and where
#
all kinds of mad things happen.
#
So as someone who is an economic journalist who looks into data, what is your mindset?
#
How do you stop one kind of thinking from impinging on the other?
#
Because there must be times where the correct thing is to sit back and look at the broad
#
picture and try to figure out the way the whole machine works from first principles.
#
But there must also be times where you want to ignore that frame and just dive deep into
#
the complexity of the problem and look at people as people and not as numbers and try
#
to get more particular.
#
So how's that sort of journey been for you?
#
Yeah, that's an interesting question.
#
So I'll answer it in two parts before I get into my journey, what I think about this question
#
in general, which is that at least as students of economics, we are fortunate that there
#
have been great economists of the likes of Milton Friedman, Samuelson, of different ideologies,
#
but who have been excellent communicators, right?
#
So if you read some of their writings, I mean, they would put an economics journalist to
#
They are very clear about what they're saying and it is not filled with jargon and so on.
#
In India, maybe we have less of such examples, but in the West there is, I mean, and if you
#
follow English, you can follow their work.
#
So one is that, and Samuelson has, for instance, has also published very technical papers,
#
Where the writing is completely different.
#
So they were making conscious choices and they felt that both were important, their
#
role as public intellectual and their role as someone who was advancing the discipline.
#
Milton Friedman did not have that big an impact on economic theory, to be honest.
#
I know a lot of Friedman fans may be disappointed by hearing this, but Samuelson definitely
#
had much greater impact on the economics profession as such, in that sense, not as a public.
#
As a public, both were equals, I guess.
#
And I think both of them around the same time, for the same set of years had columns, I think,
#
on opposite pages of the Time magazine or something.
#
So one is that, that you, so even theorists like Samuelson who work on this meta-narrative
#
and this general sort of framework, they relax those theoretical assumptions when they deal
#
with concrete problems, right?
#
Because they also know the limitations of the theory.
#
In fact, good theorists do know the limitations.
#
They do not expect the real world to behave that way.
#
It is only bad and mediocre theorists who think that this is how it will work out.
#
The second part is my own journey has been much more ad hoc and this thing, as I said,
#
I reacted to my IJD course in a very sort of different way.
#
I felt that, you know, this was inadequate and to some extent it was a let down.
#
And so I need to figure out for myself how things work.
#
And so first I felt that I should understand financial markets.
#
That was a primary imperative.
#
And although we had a very good finance teacher, you can't understand markets unless you speak
#
to people who are participating in it.
#
So I became a market reporter and it was fun for a while.
#
But after one year or one and a half years, I felt that I was doing the same kind of stories.
#
And I do tend to get bored after a point.
#
In fact, this data journalism thing is the longest thing I've done.
#
And that is also because within data journalism, you can do a lot.
#
And this was a new thing.
#
So you're given a lot of freedom and you could work out different things.
#
But after that market stint and because I had got that malnutrition fellowship, I had
#
got the taste of long form reporting, I decided that I need to move out and do things on my
#
And it was that period when I was not doing too many stories, but you know, maybe I was
#
doing two stories a month or three stories in two months, and I was given that freedom.
#
And occasionally I was contributing to this online view section which had come up with
#
And Nayanjan was my boss and he was a very sort of open kind of a boss.
#
So he allowed me to travel to some rural place in Maharashtra for five, six days and then
#
come back and write one story.
#
So I had those kind of, I mean Mint allowed me to do all that after a couple of years
#
And that phase also, I think was critical because I got to see for myself many of the
#
things that was there on the ground.
#
And I also then got to appreciate the role of data and that was the turning point actually.
#
Because even in market reporting, I did use numbers, but I didn't look at it as you know,
#
as a very key component or this was something one had to do.
#
And so I did, I was like any other reporter that you put a chart along with your story
#
and then you figure out what is the right chart and do it.
#
So, and I knew I was good with numbers.
#
So it was something if I didn't have a very good, you know, story that came from my sources,
#
I knew I could always pull out a trend story.
#
So it was something like a backup.
#
It was never something primary.
#
And even when I went into the field to do long form writing, it was with that, that
#
I don't want to do this numbers thing.
#
But when I encountered questions in the field, which I could not answer with primary observations
#
with my field work, by just talking to people, then I realized the importance of data, especially
#
when I had to leap from my observations in the field to something generic or general
#
about the economy or the country.
#
And that is when I realized that, you know, I had perhaps exaggerated what we generally
#
tend to dismiss as just numbers or, you know, just people modeling and coming up with anything.
#
So I realized that there is good modeling and bad modeling, there is good empirical
#
work versus bad, there's good research versus bad.
#
And I became more sensitive.
#
I had a nuanced sort of view of that.
#
And that is when I decided to sort of come back to analytical writing, I started writing
#
editorials and op-eds and so on.
#
And then started this data journalism sort of began from that.
#
In particular, there's one story, I think, which I just thought I will mention, because
#
while thinking, you know, it sort of struck me.
#
So there was this controversy around BT cotton, I don't know if you remember, around 2011-12.
#
So this was 10 years of BT in India, and there was also a documentary being made, I think
#
it was not fully made that time, about farmer suicides, which laid the blame on basically
#
And there was a lot of writing around that.
#
And I felt that this needed investigation.
#
And I was already traveling to the countryside that time, done some stories on various farm
#
I was among the first to write on farmer producer companies, which now have become commonplace,
#
but at that time was not.
#
And some of my contacts and sources had told me that, you know, this is one big thing,
#
and this is destabilizing agriculture.
#
And this GM lobby is pushing ahead, although GM crops are a failure.
#
And I began with that as the starting premise, which got strengthened after my first sort
#
of meeting, which was in Monsanto, because they were based in Bombay.
#
So even before I traveled, I met them, Monsanto, of course, is the most famous GM company.
#
And I met them with a certain amount of skepticism.
#
But when they offered to sponsor my visit to Vidarbha and said that we'll pay for it,
#
I just lost my, you know, I just lost it.
#
And I was polite to them, but I was really startled.
#
And I said that I went with the aim of sort of exposing them, if I may put it that way.
#
When I landed in Vidarbha, I spent quite a few days there.
#
First in a village called Dorli, that in 2005 had declared that the entire village was up
#
So it was a stunt to attract attention.
#
And a lot of people, a lot of reporters have gone there.
#
I found that the villagers there and the farmers there did not have very negative, strong negative
#
opinion against BT cotton as such.
#
In fact, the profits had gone up since they used it.
#
In some other villages, they had problems, but it was not BT per se, there were various
#
They had to take loan from moneylenders, the water, you know, Vidarbha has erratic rainfall,
#
so often the rains fail and then the crop fails.
#
And in some cases, yes, they felt that the price of the seeds was too high.
#
But that did not come out of the primary factor, it was at best a secondary factor.
#
So I thought what was happening?
#
And then I began to look closely at people who had led me to that story, to the NGOs
#
And I know I'm saying that a moment when NGOs are facing a lot of heat and not necessarily
#
So I want to qualify that not all NGOs are like that.
#
I have worked, I've come across in my reporting, I've come across many NGOs who are not like
#
In this particular case, let me be clear, I did feel that they were most likely getting
#
funds from the organic lobby to prevent GM crops from finding a foothold in India, although
#
that was not what the farmers wanted.
#
So building on that, I then went, came back to Mumbai, looked at the numbers and I saw
#
that yield had actually gone up.
#
The instances of farmer suicides in Vidarbha had actually peaked in the late 90s, much
#
before BT cotton was introduced.
#
When I looked at the data for five years after BT, five years pre-BT, just the exact five
#
years, there's not much difference, statistically, it was almost insignificant.
#
And in fact, the post BT was slightly lower.
#
That lower may not be this thing, but at least it did not cause a spike.
#
The spike happened much earlier.
#
When I looked at numbers for Gujarat, which is another cotton producing state, there the
#
yields had grown phenomenally.
#
So there were two BT stories, not one.
#
And I was getting to hear only one.
#
So then I researched more and then I planned a trip to Gujarat, I met people there.
#
Even before going to Gujarat, I realized that, by the way, this story has a Narendra Modi
#
So it's a bit long, but go ahead, we have time.
#
So genetically modified crops came to India in the late 90s, they began applying for approvals
#
And there was a lot of skepticism even then.
#
The way the BT cotton seeds are developed is it is through a hybrid.
#
So two crops have to be cross pollinated and that cross pollination has to happen under
#
local conditions, otherwise it will not work under local conditions.
#
So those happened in Gujarat, not Gujarat.
#
And some of the companies involved there either through that or through some other sources
#
got hold of those trial crops and seeds.
#
And suddenly Gujarat was flooded with illegal BT seeds, which were not certified, which
#
had not got the approval, but which farmers bought.
#
And in 2001, I think around the time Narendra Modi became Chief Minister of Gujarat, there
#
was a huge ball-worm attack and BT is supposed to protect against.
#
I actually wrote a column about this, but go ahead.
#
This is so long back and I did the story in 2012.
#
So some of my numbers and dates may get mixed up.
#
And they realized that this was working because the illegal crops succeeded whereas everything
#
And Monsanto got wind of it and they complained to the Genetic Engineering Approval Committee.
#
They also took it up with the Agriculture Ministry, they took it up with the state government
#
in Gujarat and they demanded that those crops be burnt.
#
The farmers protested and given Mr. Modi's understanding of politics, he realized that
#
if he burnt, allowed crops to be burnt, what would be his fate?
#
So he said that, okay, we'll put a ban on illegal crops and we'll not allow them to
#
be sold, but we're not going to burn the existing crops.
#
And Monsanto had to accept it.
#
But that didn't happen.
#
Illegal seeds got a life of their own.
#
And what that meant was that the terms of sale were also different.
#
So because you're not buying on a receipt, farmers did not need to pay anything upfront.
#
If the crops failed, they did not need to pay anything.
#
So under that premise, at one point it was like 50-60% of the market.
#
Monsanto had a lower share than this Desi sort of this thing.
#
The other was the soil condition.
#
Gujarat was much better and conducive.
#
Water supply was better and water supply had improved over time that helped the boom in
#
cotton in Gujarat, partly because of Mr. Modi's efforts and partly because of non-governmental
#
organizations which had organized water harvesting schemes and so on.
#
So it was a combination of various things, but BT was certainly not the culprit.
#
In fact, the seed market structure was such that it actually favored the farmers, whereas
#
in Vidarbha, because it was one single monopoly and they could dictate the price and the conditions
#
under which it was sold and farmers had to pay upfront, the situation was quite different.
#
And coupled with weather conditions, soil conditions, credit conditions, credit also
#
was much more easily available in Gujarat than in Vidarbha.
#
So a combination of all these institutional factors, various other factors, produced this
#
And so when I came back and wrote the story, it was not an anti-Monsanto piece at all.
#
It was a story of what was actually happening and how in fact the NGOs were not giving
#
up or showing us the truth.
#
Not just NGOs, even a parliamentary standing committee on GM crops at that time had decided
#
to completely ignore this question.
#
I remember I had a conversation with Basudev Acharya, who was a CPM MP and the chairman
#
of the standing committee on agriculture.
#
And I asked him this question that BT is just one part of the seed.
#
It is just one protein that prevents against bulbworm.
#
The rest of the performance of the crop depends on the cultivar, the variety that is being
#
used or where you're putting in that gene or changing that gene.
#
So did you consider the role of cultivars?
#
Because the agriculture scientists in Vidarbha told me that had they not used those kinds
#
of hybrids, the situation would have been different.
#
So it is not just about BT, the kind of cultivar in which the BT thing is being put in.
#
And he was just blank for some time and he said that we have listened to so many testimonies,
#
no one raised this question, which I found hard to believe.
#
So that either means that they were talking to the wrong people or that they chose to
#
bypass scientific evidence.
#
And then I realized as a professor in Cornell University, Ronald Herring, who has done work
#
on this, on the politics of organic farming and anti-GM crops.
#
And I wrote to him and he said that what the parliamentary committee in your country has
#
done is nothing unique or exceptional, this has happened in many places.
#
So I ended up in a place very different from where I started with.
#
And I could do that because of a mixture of things.
#
It was not just data, but data played a critical part and also, you know, primary observations,
#
speaking to people, looking at research, looking at wider research and so on.
#
So it really made me question a lot of things.
#
It also taught me how to do things better.
#
That's a fantastic illustration because it also shows you the importance of the skeptical
#
mindset that too many journalists I see will often go into a story with a narrative already
#
Like a Monsanto is evil narrative could easily have been your narrative.
#
And they're not open to that changing.
#
And that becomes a problem because they're only looking for things that confirm that
#
narrative and not looking for things that go against it.
#
I wrote a column a while back, which I'll link from the show notes called Farmers Technology
#
and Freedom of Choice, a tale of two satyagrahas.
#
And I find this story really inspiring because what really happened is, as you were pointing
#
out just to sort of recap for my listeners, as the nineties came to an end, cotton farmers
#
in India were having a tough time because bollworms were averaging crops across the
#
They had to use increasing amount of pesticides to keep them at bay.
#
The cost of the pesticide went up, the labor involved was also costly, and the crops would
#
And then this illegal BT cotton seeds kind of came about.
#
And in 2002, all cotton crops in Gujarat failed except 10,000 acres that had BT cotton.
#
And like you just pointed out, the government did not say, hey, this is a model that has
#
They instead said destroy all of these, which was crazy.
#
And at around that time, Sharad Joshi, the great farmer leader, leader of the Shedkari
#
Sangatana in Maharashtra, he took about 10,000 farmers from Maharashtra to Gujarat, and they
#
did their satyagraha thing there.
#
And eventually, the ban on BT cotton was lifted by the center.
#
And the thing here is that GMO crops-
#
Yeah, I forgot to add that.
#
The ban on GMO was lifted because of the illegal seeds, because of the efficacy of the illegal
#
And the thing to note here is number one, and I'll give a link to my column, but number
#
one, GMO crops are completely safe, GMO foods are completely safe, don't kind of believe
#
the lobbies enough science has been done on this.
#
And they're not only completely safe, they've helped humanity in terms of in just increasing
#
agricultural productivity, stopping people from starving, just incredible.
#
The other point you mentioned about the NGOs is absolutely correct.
#
We should have the hashtag not all NGOs, that there are tons of NGOs which do incredible
#
work and which I admire greatly.
#
But whenever we are sold a particular narrative, we should follow the money.
#
It is true that a lot of NGOs are incentivized by where they get their funding from to pick
#
the most alarmist narrative so that their funding continues or increases.
#
So we should be wary of alarmism in absolutely any sphere and we should examine everything
#
with the kind of skeptical eye that you sort of brought to this.
#
So now, you know, you mentioned that in college, the first thing your econometrics teacher
#
taught you at IGIDR, I think you said, was coding, right?
#
A part of it is understanding statistics, a part of it is having the tools to work with.
#
And now you're in journalism and you take data seriously because they shed light on
#
things that personal reporting can't.
#
So how did you go about building your data skills?
#
And earlier, like at one point, you said that people talk about, you know, you can torture
#
statistics to tell them any story you want and models are such a big problem.
#
And you pointed out, look, that's not true.
#
There are good models and there are bad models.
#
There are good ways to use stats and bad ways to use stats.
#
So elaborate a little bit on that, on your sort of education and understanding that what
#
are the things you should do with numbers and what are the things you should not do
#
Like, where do you get a sense of best practices from, especially given that this particular
#
field, the kind of data journalism that in a sense you pioneered at Mint was so new.
#
So it's not like there are role models and there are teachers, like internationally, of
#
course, I'm sure there are, but how does this process work where you figure out your own
#
best practices that these are the things we shall do.
#
These are the tools we shall use and these are the pitfalls to avoid.
#
Yeah, that's a good question.
#
So this was exactly the question that troubled me when this push came to start this.
#
And I think I've mentioned this earlier also somewhere.
#
I was skeptical when they said that you start this data journalism thing, something dedicated
#
I was open by then to the use, increasing use of data in newsrooms and in stories and
#
But this dedicated data journalism business, I had strong doubts.
#
And even now I don't very much like the term data journalism.
#
I prefer either analytical or statistical journalism, but this is what people understand.
#
So now I have accepted it.
#
And so they said, like they meaning basically Niranjan and Ravi Krishnan.
#
Niranjan was my boss and the executive editor now is with IDFC.
#
Ravi Krishnan is now the deputy executive editor at Money Control.
#
Earlier he was the Bomb Bureau chief and before that he was in a team called Mark2Market,
#
which was the markets group, market analysis group.
#
I was in the views team at that time, writing edits and op-eds and so on.
#
So the two of us were basically tasked with starting this and we had constant debates
#
and discussions and so on, mostly at Niranjan's house with lunch sponsored by him.
#
And this was something that I constantly raised that, you know, there's a great chance that
#
A lot of data is completely untrustworthy.
#
So we should not make strong inferences from them, they'll be wrong and they will be proved
#
wrong in the course of time.
#
So because we all of us were keen that what we said was not just true that day, we don't
#
want to prove them wrong the next day.
#
We want it to have a certain shelf life beyond the typical newspaper story.
#
So we all agreed on this.
#
So thankfully there was agreement on the what we were setting out to do.
#
We're just trying to figure out the mechanics.
#
So we came up with some broad guiding principles.
#
We did not have a fully fleshed out and listed sort of this and this came later.
#
Based on our collective experiences, our work with data and Niranjan himself has used data
#
in his work and at the same time is also very skeptical of, you know, much of the modeling
#
So there was a lot of agreement in some of the basic guardrails.
#
One of which was that it has nothing to do with data is basic journalism, but which we
#
practice religiously in plain facts.
#
Maybe others do it to some extent, maybe not, I don't know.
#
But which we did is that we will never trust a report on a report, right?
#
If someone has said that this number is coming from a World Bank report or this thing, we'll
#
go and check whether the World Bank report actually says this.
#
This is a very basic thing.
#
It has nothing to do with statistics.
#
But that is one mistake that people make and, you know, editorials get written about stuff
#
which is actually fake news or zombie statistics as they say, which doesn't exist.
#
And there are Nithya Yogi reports which have quoted World Bank studies, which are not there.
#
So I think in that respect, Planning Commission had better checks.
#
So we can't trust Nithya Yogi documents to that extent as maybe things will change once
#
The second thing was that whatever statistical tools or indices or calculations we do should
#
be accessible to a class 10 student.
#
So we'll use mean, median, mode, percentage, percentage difference, scatterplots, etc.
#
Everything that a class 10 student of that time understood because the math syllabus
#
and even some basic statistics is now taught even at school level.
#
And a lot of things are now available at school level.
#
So we won't go beyond that.
#
For anything else that requires those kind of modeling or this thing, we felt that that's
#
not suitable for newsroom because it requires some amount of peer review and which we can't
#
So for those cases, we will rely on other economists who have already done some work,
#
maybe on past data, historical data, and we'll look at research precisely for that to inform
#
And third, we'll stick, therefore we'll just have to stick to largely descriptive statistics.
#
And this came also again from our collective experience that when any of us spoke to policymakers,
#
whether they be politicians or bureaucrats or senior people influencing policy, none
#
of them ever said that I was impressed by that regression coefficient 3.35 and not 0.25
#
in that previous model and hence changed my policy.
#
So most people look at trends, most policymakers also look at descriptive numbers.
#
If the broad trends point in various directions, then they go for a certain policy choice.
#
And the fourth thing which policymakers also do and which we try to incorporate in our
#
work is to always look at multiple data sources.
#
If multiple data sources are pointing towards the same trend, then we can be reasonably
#
So I always used to say when I hired people, got them into a team, that there's a cousin
#
who's a doctor who once told me that among doctors, there's this thing that they say
#
that if a doctor has made an extraordinary diagnosis, it is most likely that he's extraordinarily
#
If the previous test says something and his test or whatever checkup said something else,
#
then he should actually be careful.
#
So I said the same thing to a reporter.
#
If a new claim, whether it be in a research report, whether it be from a source or whether
#
you have come up with your own analysis of the data, is saying something extraordinary,
#
it is most likely that is extraordinarily wrong.
#
So please re-investigate, which is in a way is not told in as many ways either to science
#
generalists or to data generalists always.
#
Sometimes they go with, suppose a new paper has come up which is challenging the entire
#
It is more newsy, right?
#
It is very hard then to resist the temptation of playing it up, whereas we are sort of inculcated
#
So we put in such guardrails and of course we refine our time and some of these things
#
we sort of communicated and conveyed in a much more clearer with examples because by
#
the time when we started doing our work, we came up with these examples and these examples
#
So for instance, the Nityayog report was a real report.
#
I don't remember what it was about, but there was this report which I think one reporter
#
from Washington Post had pointed out.
#
She had done a whole thread on how it was actually a zombie statistic.
#
So similarly, when I did my work on nutrition, a lot of data that people tended to first
#
look at was ICDS data which is collected by the administrative machinery and which is
#
wrong because the ICDS official has always an incentive to underreport and that is why
#
you rely on an independent survey which is conducted not by the WCD women and child ministry
#
but by the health ministry which does not have an incentive to.
#
So we made it a sort of generic rule that whenever you encounter a data set which is
#
collected by the same agency which is supposed to be responsible for changing policy or making
#
recommendations, be very careful because it has an incentive to doctor those numbers or
#
even if not doctor, they come in as a sort of an administrative process where the incentive
#
is not to report truly.
#
We saw that with COVID statistics as well.
#
It was much later that the real numbers or even the registered numbers came up and even
#
with COVID infections, we didn't know and there were huge state level variations.
#
So those are things in other examples from other sectors we already had learned and we
#
put in place so that when the time comes, we do not make those mistakes.
#
So those are things, a combination of reading, engaging with other people, speaking to people
#
who work with data and speaking also with people who use data and have sort of made
#
such mistakes in the past and can caution us not to make those mistakes.
#
So combination of all of those things I think helped us sort of frame certain things and
#
despite all that I said, we also made some mistakes but we learned from them and we corrected
#
ourselves and those also came up in our sort of workbook that we should avoid.
#
So personally, how was this process for you?
#
Because what's happening here is in the span of just a few years, you've joined as a reporter
#
covering the capital markets and then you move on to doing this other kind of journalism
#
and then you're looking after the edit page and you're commissioning op-eds and writing
#
opinion pieces and then you're into data and then you're asked to set up this division.
#
So in a sense, you've kind of not exactly, it's not a great metaphor but it's the first
#
one which came to me, from a hunter, you've now become a gamekeeper.
#
In a sense that you become a manager of people now, you're not just a journalist going out
#
in the field using all these tools but you're also a manager of journalists who is formulating
#
almost in real time as mistakes are happening, as one is learning, who is formulating the
#
code of conduct by which they work, who is formulating best practices, who is giving
#
shape to the processes which they must follow and you must also follow and during this time
#
you are also still a journalist.
#
So what was this process like for you?
#
Because what I've often found is that these are almost two entirely different skill sets
#
that doing the job and then actually managing people are completely different and it's very
#
hard to find an intersection between someone who can kind of excel at both.
#
So what was this journey like for you?
#
Yes, so there were, as you said, it was a mixture of various things.
#
One was, as you said, forming our own rules, there was no one to do that for us.
#
So we were sort of chatting our own path and creating the rules ourselves, it was crazy.
#
But it was fun, that part.
#
Then there was this part that you are either writing new kind of stories or helping shape
#
those stories, that was also very, you know, it was a fun experience and it was not that
#
my previous experiences were not coming into play.
#
So everything that I had done as a market reporter, as a sort of development writer,
#
all of that was shaping the stories on the economy, on public policies, on politics that
#
I was editing or commissioning or getting others to write or myself writing.
#
So all of that flowed in.
#
The hard part, I think, was becoming a sort of, at that time I was an assistant editor,
#
but practically a sort of co-editor of the team.
#
And then eventually I became the data editor.
#
So that also meant managing people.
#
I mean, I was myself a very young journalist at that time.
#
In fact, I had once complained, went up to Devan Jain and told him that, look, nothing
#
much of my own writing is happening.
#
You have put me into this thing where, which I'm no longer enjoying.
#
And just because you forced me, I'm doing it and I want to quit.
#
So he said, don't be silly.
#
You don't have to quit or anything.
#
You can still stay in Mint and continue doing what you were doing before.
#
We'll make a departmental this thing.
#
I'll speak to Sukumar, who was the editor and this thing, and we'll figure, you don't
#
have to do anything because I'm forcing you or if you feel that I'm forcing you.
#
But that conversation helped me cool down, calm down.
#
And Devan Jain said gently, you know, in his usual this thing that, see Pramit, this is
#
something you'll have to do at some point of your life or the other.
#
So maybe you feel it is a bit too early, but this is also skill, as you said earlier.
#
Initially, I did not look at it as a skill.
#
And that was my mistake.
#
Then I began to sort of view it more seriously.
#
So I feel I have been a better mentor to some of my colleagues who joined later than to
#
some of the colleagues in my early sort of years.
#
So that is one regret I do have.
#
And had I had greater experience or had I been prepared for a transition to that kind
#
of a role, I would have approached things differently or with the benefit of hindsight,
#
I would not have done several things or communicated in a way that I did or been more clearer in
#
exactly how to do things.
#
So that again, it was a self-taught, self-driven sort of process.
#
And so in the later years, it was much more easier.
#
And also, as brainfacts brand got established, we got fantastic talent coming in.
#
So that also made a huge difference.
#
You didn't have to work at a very basic level with them.
#
They would already do certain work and then you push it up to an even higher level.
#
So we got fantastic people, people like Roshan, for instance.
#
And the other thing we always insisted, which was not a guardrail for journalism, but generally,
#
because of my own experience in getting into journalism, I always wanted the gates for
#
data journalism to be open.
#
So if you notice, whenever we have vacancies, we make a public announcement, which is not
#
so common in journalism, but which is quite common among data journalists.
#
And I think it is because of us.
#
In the early years, we put out ads in places like EPW with disastrous consequences.
#
But later on, Twitter and all these things picked up LinkedIn.
#
So we could put out ads there.
#
And we got a very diverse kind of variety of candidates who applied.
#
And in fact, the selection process took up a huge amount because if 100 people have applied,
#
you need, first of all, some criteria to filter out, then you need to do tests.
#
And I am a strong believer in the written test over an interview for various reasons,
#
including the fact that a written test balances the playing field more than an interview,
#
especially if someone is coming from a background where he's not so comfortable in spoken English
#
We did change all that.
#
And some of those practices also seeped into other teams and other newsrooms and so on.
#
People ask me, how do you approach hiring and so on in other institutions, even in think
#
So it was very good to see that, you know, and people saw the kind of people who came
#
and joined us, worked as there was attrition also.
#
So people left and they got fantastic offers as well, because journalistic work is very
#
People see your byline.
#
You are doing great work.
#
And especially data work can be applied in a number of fields.
#
And people have gone on to join investment banks, think tanks, someone like Roshan has
#
set up his own team, Vishnu, who had come in from Jaipal, he went on to another think
#
Now he's in the economist data team.
#
Harsha, who had a very good visual sense and did many of our maps, redid many of our
#
He joined a startup and now he's back to Colombia to study data journalism.
#
So different people pursue different paths.
#
But the point is all of them came with and they all came from different backgrounds,
#
different training and so on.
#
But the common point was they were all interested in numbers.
#
They wanted to tell a story as rigorously, as accurately as possible.
#
They're all united in the common vision that will make this as simple as possible for our
#
readers and offer as compelling an experience as we could through the use of words, numbers
#
I should point out to our listeners that the Niranjan you keep referring to is Niranjan
#
Rajadhaksh, you know, who was a managing editor at Mint for the longest time.
#
In fact, he gave me my column in Mint, which later won the Basia prize circa 2007 when
#
So wonderful editor, wonderful human being.
#
I should have him on the show sometime.
#
He keeps making polite noises every time I ask him.
#
But maybe if he's listening to this Niranjan, your time is coming soon.
#
And also what you said about learning about management, looking at it as a skill.
#
And I actually went through that because the first time I found myself with managerial
#
responsibility was in the mid-autees where I was a managing editor of Cricinfo.
#
And so suddenly, you know, one day you're a star writer and the next day you're whatever
#
you you're managing people.
#
And I simply wasn't good at it.
#
But I didn't think of it specifically.
#
Like I sort of, you know, in Calvino's words, to go back to dad, I took the literature approach
#
to it and it was all messy and complex, but I didn't take the philosopher approach to
#
it where I should have sat back, got meta, formulated certain rules on how to behave,
#
how to deal with people, how to whatever, I didn't do all of that.
#
But when I did this brief stint as which lasted, I think, a couple of years as editor of Pragati,
#
the policy magazine, I think I was the best boss in the world because I had really thought
#
it through how I should kind of handle those kind of responsibilities.
#
I'm also struck by what you said about many of the people who worked with you going on
#
to, you know, economist data team or corporate job or, you know, a think tank, Roshan set
#
Do you sometimes think of roads not taken by you because you could also have done any
#
I'm sure corporates would have if not made you offer certainly been glad to have you,
#
you know, look after their whole analytical team and all of that.
#
Your skills could be incredibly more lucrative in other professions, but instead you are
#
sort of here in journalism.
#
So do you think of roads not taken?
#
And if it is the case that you've made a conscious choice to stick with this because you love
#
it, what is it that you think makes you do that?
#
Like I just did an episode which, you know, on the day we are recording it, my episode
#
with Abhinandan Sekri released.
#
And one of the things I was struck by was that a lot of the things he did in media,
#
he doesn't like to call himself a journalist, but a lot of the things that he did in media
#
and as a media entrepreneur was driven not so much by a desire for profit or the bottom
#
line, as would be the case with many entrepreneurs, but because he was like, shit, this has to
#
And my question to him therefore was that is your journalism in a certain sense an extension
#
Is it a form of activism?
#
And I feel like asking you the same thing because at one level, you were interested
#
in politics in your college years, you were engaged with the world and trying to bring
#
Is this an extension of that?
#
And so to be completely honest, in the early years, it was an extension of activism.
#
I wanted to first, of course, figure out how things run and how to make a mark as a journalist,
#
how to write and so on.
#
But I was pretty sure when I joined that once I did that, I would do my own propaganda and
#
change the world and that sort of early sort of expectation is not unusual.
#
I've hired people who had similar, expressed similar beliefs and thoughts and it should
#
I think in an early age, if it is not there, then there is something missing in you.
#
But that, that illusion, disillusionment all happens within a span of a few years.
#
So within two, three, four years, I think all that got over.
#
And so while that may have been the early urge and there was definitely an urge to make
#
the world better and a mix of things that, you know, as someone who has come from, say,
#
the periphery of the country, you do feel that wasn't the periphery are not heard.
#
So you do want to make those voices a bit more audible.
#
Similarly, you do feel that as an outsider, kind of outsider to the mainstream, you can
#
say stories that others are not able to.
#
Then you also feel that when you go out there, when you report, when you come back to write
#
because of your training, because of the amount of time you have thought about these questions,
#
the amount of time you studied, not just in formal courses and so on, but generally you'll
#
be able to offer a more comprehensive and coherent narrative compared to someone who
#
And therefore, in some ways, you will be able to teach others to do better.
#
Like I remember people used to joke about me when I first joined Mint that the reason
#
Ramit has come from IGID, they used to make fun of that even then, that why did he come
#
It was because his primary lesson, primary aim is to teach economics to journalists.
#
So they used to make jokes about that.
#
Maybe there was some element of truth.
#
But over a period of time, all that dissolved and in fact, one of my colleagues recently
#
asked me this, exactly the same question, because I'm now taking a break, I have quit
#
what was a very nice and fulfilling sort of job.
#
And I'm still in writing.
#
So the point was, I still continue, I've already done a lot and you can easily do something
#
So there are two parts to it.
#
One is the original choice I made.
#
I walked out of the placements from IGID, knowing that I would always get a salary which
#
would be lower than any of my peers would be getting.
#
So that gap would never be bridged.
#
And that was a very much a conscious choice.
#
And this was the life I wanted.
#
I also had the counterfactual of life as a corporate worker for two months at City in
#
its analytics division.
#
So it was not that I was living some, I was unaware of what the other life would be and
#
so would be tempted by it.
#
I think I did that precisely because of that so that I did not have any temptation later
#
I knew journalism was hard.
#
I had no illusions about that part at least.
#
And the second part of it is that the way I think of it, I think it was Akar Patel or
#
someone who said that, you know, journalism is a craft.
#
It is not a skill in that sense.
#
So if I think of myself as a carpenter, for instance.
#
So initially I was making very rough chairs and I was making different kind of chairs
#
and for different people, different purposes.
#
Over a period of time, I started making more smoother chairs.
#
And then I started making chairs which were unique and where I could bring in different
#
kind of skills to produce a certain kind of chair and which was not there in the market.
#
So that I felt is a value.
#
And this process has taken me long years.
#
You know, it was only after eight, nine years that I felt that, okay, fine.
#
I have understood a bit about, you know, how to make a mark as a writer, as a journalist.
#
I did get Ramnath Goenka fairly early on.
#
That was never sort of a mark where I felt that, okay, I have arrived or I have this
#
Nor was it any particular story.
#
It was just, you know, reading my own stuff, you know.
#
So for instance, when I go back and read some of my older stories, I feel, why did I write
#
So that I think, if I'm not very much mistaken, I won't say about some of my recent stories,
#
So I'm more satisfied with them.
#
So it is that the same way the carpenter is satisfied with the work and therefore wants
#
to go on because I feel that he has acquired some level of mastery.
#
I'm not saying I'm a master yet, but some level of mastery over that craft.
#
Now, one could well argue that this is something like a sunk cost fallacy.
#
And it might be true, but a sunk cost fallacy is known only post facto.
#
So it is only later that I will get to know.
#
Right now, I have the freedom, I have the privilege to do this and, you know, there
#
are people who are happy to give me freelance work and I can continue writing, even if I
#
am not associated full time with any organization.
#
So I feel I should make use of this privilege, continue to write till I can and then see
#
what life has in store for me.
#
I don't think it's a sunk cost fallacy.
#
And I love the metaphor of the carpenter in the chair, you know, and it's something I'm
#
going to use with my writing students because I keep telling them about how iteration leads
#
And one of my points is that whenever you start doing something, you suck at it, right?
#
I start writing, I'll suck at it.
#
I start podcasting, I suck at it.
#
Carpenter starts, makes his first chair, he sucks at it.
#
You do it again and again.
#
And then if things work out, you find that unique voice, as it were, where you can make
#
a sort of the perfect kind of chair.
#
And that's what happens to writers.
#
I see that in my own podcasting journey where, you know, my early episodes are clumsy chairs
#
and now I think I've got a particular style and a particular voice.
#
So I'm intrigued in double clicking on what is the kind of chair that is true to you?
#
What is that authentic chair that you're making?
#
And obviously, you know, it's a metaphor.
#
So just in terms of the kind of stories you do, the kind of writings you do, like where
#
does that individuality, that authenticity come in?
#
Like another key thing I tell all my writing students is that forget everything else.
#
The one thing you cannot compromise on is being authentic to yourself.
#
Don't try to second guess what the reader might want or where there is a hole in the
#
Just be authentic to yourself.
#
And even in your work, like from the outside to many people, journalism may seem everything
#
You're going out and reporting stories and it's just a matter of efficiency.
#
But it's not that, right?
#
You have a particular way of looking at the world.
#
You have particular interests and passions and areas you want to dive into, like you
#
dived into nutrition, for example.
#
And when all of this comes together, there is something that you can look at and say,
#
oh, this is a Pramipatacharya story or this is a Rukmini story or, you know, all these
#
unique voices are sort of developing.
#
So how would you sort of define your chair as it were?
#
Or tell me a little, I can't ask you to define a chair, but tell me a little bit more about
#
I don't know how to answer that, but the way I see it, I see it as a sort of a few sets
#
of comparative advantage that I have.
#
One is unlike many other data generalists, I started out as a sort of field reporter,
#
did beat reporting, then long form writing.
#
And even before that, I had a sort of very diverse sort of experiences and because of
#
my political student activism and so on, which are not easily replicable.
#
So that is one, the background and everything.
#
The second part is, of course, my economics training.
#
Not a lot of people with that kind of training tend to get into journalism.
#
And even if they were to, they may not be willing to sacrifice for so many years to
#
And I don't blame them.
#
So I'm just saying, stating it as a fact.
#
So that means that there are very few people who understand that subject topic in depth
#
and then are able to come up with a sort of informed view and a narrative around it.
#
And the final thing, as I said, because of my life experiences, the only ism I have been
#
left with is skepticism.
#
And it is not very useful in any other profession.
#
But in journalism, it is.
#
It makes a huge difference to your work.
#
If I were to, you know, be a propagandist for a particular school of thought or for
#
a particular agenda or a particular this, there's nothing wrong in it.
#
Many journalists are whether they admit it or not.
#
They can still do very good work, can even do good data work, building, you know, your
#
arguments brick by brick using numbers rather than mere words, you can combine the two and
#
build powerful arguments.
#
And I don't have temptations for doing that.
#
And it is true that many people will prefer that kind of loaded narrative, which is already
#
I don't have a fascination for that while it is true that the audience for that may
#
I have always engaged with that kind of an audience which values that kind of independent
#
So in my own growth, it is these people who have played a role, not the trolls or not
#
So it's a combination of all this that comes into my writing.
#
The final thing is the curiosity to dig deeper and also the ability to pursue that depth
#
and then finally break it down by simplifying but not oversimplifying.
#
So that is a very delicate balance and I can't give a sort of rule or a database this thing
#
But I think that is very important.
#
And I think that I would attribute to my training in IGADL because when you have to write these
#
long term papers, you didn't work hard.
#
And in the last three, four days, you have to go through fifty, sixty research papers
#
to come up with one term paper.
#
That very experience of digesting all that information and in technical language, turgid
#
prose and then coming up with something that the professor can understand.
#
Of course, the professor can still understand turgid prose, but I also paid attention to
#
my writing and some professors commented also on that.
#
So the final thing is that that experience teaches you.
#
So what you do in journalism then becomes child's play basically because you deal with
#
a lesser sort of less technically challenging sort of set of circumstances and that gives
#
you the confidence that you can crack it.
#
Even if it is a complicated economic subject, you spend some time on it, you'll be able
#
In nutritionism, you can take the help of someone you know.
#
And that is the final thing that over the years, I've spoken to experts across various
#
I've always had very diverse interests.
#
So there's some nutrition expert who still remembers me.
#
There's a market guy who at that time was not known, but today is widely respected and
#
so on, who will still answer my call because I spoke to him at a time when no one knew
#
So there are these kinds spread across various fields and disconnected fields.
#
And I can bring all of that also into my work.
#
So it's a combination of several things.
#
In fact, you know, to sum it up, I would, if possible, I'll dig out the link and send
#
It's an interview by Elena Ferrante, of Elena Ferrante rather, the author of the Neapolitan
#
novels, which my wife had read several years ago and was very impressed by.
#
Constantly urged me to read, which till I left my job, I didn't have the time to read.
#
Now I finished it, all the four parts.
#
And I was just reading about her.
#
So I went through an interview with her where she says that, you know, this writing, this
#
process is like weaving.
#
And you basically learn that you have to weave different threads in.
#
You're not even aware of the existence of the other threads when you start.
#
It is only when you start doing it that you know that this thread is missing, the other
#
And then you bring all this together and your work becomes better.
#
So I quite relate to that.
#
I don't think I've seen all the threads yet, but I have a fair idea now.
#
And I also know what threads I'm missing.
#
But I know how to sort of address them or sort of fill them in.
#
I'm not completely new to this.
#
And so, which is why I would like to sort of pursue this a bit more, become a bit more
#
That's a beautiful metaphor of the threads and weaving.
#
And I'm reminded of this quote by Joan Didion where she said, I don't know what I think
#
until I write it down, which is so lovely.
#
And that's another thing I kind of tell my writing students that, you know, sometimes
#
people will get paralyzed because they'll start writing something and one, there is
#
the anxiety and the bigness of the task and all of that.
#
But two also, sometimes they will feel that they don't know exactly where it's going.
#
So they don't write at all.
#
And the point is that sometimes writing is a process of discovering your material.
#
Sometimes you can find the gaps in your own knowledge and know what else you need to do.
#
Sometimes it clarifies your thinking about something, especially if you force yourself
#
to write in clear language, you have to force yourself to think clearly.
#
And there's another quote by the novelist E.L Doctorow, where he said that even if you
#
can see only the light of your headlights, you can make the whole journey that way, which
#
is kind of like driving in the Delhi fog in winter, I guess.
#
But which is another way that you can sort of approach writing.
#
You don't need to know necessarily everything that you're writing about.
#
You can go by the headlights and, you know, for a lot of fiction, certainly, I think it's
#
also a process of self-discovery.
#
So what I want to do now after a quick commercial break, which we shall take now, after a quick
#
commercial break, I want to talk about writing to you, because that is also a related craft
#
to journalism and data and all of that.
#
Fascinating to know how you're thinking on that evolved over time, but let's take a quick
#
commercial break first.
#
Long before I was a podcaster, I was a writer.
#
In fact, chances are that many of you first heard of me because of my blog India Uncut,
#
which was active between 2003 and 2009 and became somewhat popular at the time.
#
I love the freedom the form gave me and I feel I was shaped by it in many ways.
#
I exercise my writing muscle every day and was forced to think about many different things
#
because I wrote about many different things.
#
Well, that phase in my life ended for various reasons.
#
And now it is time to revive it.
#
Only now I'm doing it through a newsletter.
#
I have started the India Uncut newsletter at indiancut.substack.com, where I will write
#
regularly about whatever catches my fancy.
#
I'll write about some of the themes I cover in this podcast and about much else.
#
So please do head on over to indiancut.substack.com and subscribe.
#
Once you sign up, each new installment that I write will land up in your email inbox.
#
You don't need to go anywhere.
#
So subscribe now for free.
#
The India Uncut newsletter at indiancut.substack.com.
#
Welcome back to The Scene in the Unseen.
#
I'm chatting with Pramit Bhattacharya, who, you know, in the break when I said we'll talk
#
about writing after this was saying, I don't think I'm a great writer.
#
But you know, anyone who says I don't think I'm a great writer, I don't take that seriously
#
had somebody said to me that, listen, I will tell you everything about the craft.
#
I would have been skeptical as which is also the default ism of my new friend Pramit.
#
So skepticism always a good thing.
#
But I want to talk about writing because one of the things that I've noticed while you've
#
been talking about your journey in journalism is the importance of realising that there
#
is a reader at the end of it and the reader matters.
#
Like too often what happens is when we write, we imagine the act of writing is the act of
#
transferring thoughts from our head onto our laptop.
#
But especially for journalistic writing, that is not the process.
#
Your writing only becomes meaningful and comes alive and it has the intended effect on the
#
And in the case of journalism, where you manage to convey the story that you're saying.
#
And you know, one of the things I tell my writer, in fact, for those who want to write
#
op-eds, I give them what I call the Naniji test, which is that every time you write an
#
op-ed, show it to your Naniji or to a 16 year old sibling.
#
And if they don't read the full thing, it's your fault.
#
The idea being not something specific to do with Nanijis or siblings, but just any intelligent
#
lay person should be able to understand it.
#
And that's exactly what you said when speaking both about writing and about the kind of data
#
that you use when you said that, you know, we are not going to use extremely complex
#
terms and just keep it 10 standard level.
#
So tell me a little bit about how your approach towards writing evolved.
#
Because when I look at my own journey, what I see in myself as a young person is that
#
I was a bad writer as all new beginning writers are, but my judgment was also bad.
#
You know, my judgment and ability were equally bad.
#
So I didn't know I was a bad writer.
#
I thought I'm damn good.
#
And I persevered and basically I faked it till I made it.
#
You know, fake it till you make it in the present tense sounds much better.
#
Faked it till I made it sounds bad.
#
So what was that process for you like?
#
Like at what point, I imagine early on in your journalism, you have to get mindful about
#
You have to look at the craft, consider the craft, tell me a little bit about that process
#
of values that you then had to imbibe.
#
And also, were there any writing models for you?
#
People you could look at and say that, wow, this person does it really well, I want to
#
So it's a combination of all that.
#
And in fact, before journalism, I before I entered the newsroom, I thought I was a great
#
We all do, welcome to the club.
#
So and that was partly because, you know, in Cotton College, then during IGIDR, I edited
#
Everyone said, wow, what writing and so on.
#
I realized that was only because the rest was so poor.
#
When I wrote my first copy after joining Mint, Ravi, with whom I started the data, he was
#
So it's only later that we started it together and so on.
#
So Ravi was the senior market writer, and he gave me some IPOs, DRHP, and asked me to
#
So I think I must have rewritten that first copy six times before Ravi said that, okay,
#
fine, I can send it for subbing.
#
Okay, so, but in the second copy, I had to read it only once.
#
And from then on, it was just one round of rewriting and so on.
#
And after two, three months, there wasn't much rewriting.
#
So Ravi claims that I was a reasonably fast learner.
#
I think there is only half truth in that because the thing is, Ravi wanted to move out of market
#
So he had to convince the bosses that there was going to be a replacement in the next
#
And so I was still, I think, half done by the time he left.
#
But then I was on my own and I had to face the resident editor or the deputy, whatever,
#
managing editor, Tamal, on my own.
#
And that was an arm above and he was the overall bureau chief, so you couldn't just push whatever
#
So I had to be very careful on my own.
#
Sometimes I would ask Ravi to still take a look at what I had written.
#
And then for front page stories, Niranjan would take a look.
#
And who would have a completely different set of questions and so on.
#
And whenever he picked it, I mean, anyone, not just me, even a very senior writer would
#
really be afraid, you know, what is he going to ask?
#
Like, give me an example.
#
Can you remember something where...
#
Yeah, so there's this, I don't think I should name that person, a very senior journalist
#
now, a very senior editor in another newspaper, Economic Times.
#
I think I can name the newspaper, who was admitted at that point.
#
And he and Ravi had written some piece and they put it on the system, we had a system
#
called IDOS, the Content Management System, where you can see who is editing and all the
#
So it was a very transparent system also.
#
So you can see the changes, you can review it.
#
And each writer can see what each, whether the sub editor or the...
#
So each copy also went through three rounds of checks before it was passed.
#
So Mint had this system.
#
And the third check could often be either the managing editor or the editor himself.
#
So Niranjan or Sukumar.
#
And whenever either of these two picked up, there was complete chaos in the newsroom.
#
When Sukumar, people used to shout, Oh God, Sukumar has picked up.
#
When Niranjan, they couldn't shout because they were sitting there.
#
I remember Niranjan, the moment Niranjan picked up the copy, that person came up to Ravi and
#
said that, Ravi, let's go down.
#
So Ravi said, Niranjan has picked up.
#
And then both of them vanished and Niranjan wanted to, had a query or they couldn't find
#
them for, you know, like half an hour or something.
#
And then he said, what happened?
#
He said, no, no, we just fled seeing that we knew there were problems in the copy.
#
So tell us now what are the problems.
#
But the thing is Niranjan would never shout or, you know, make you feel bad.
#
He would just raise questions in a very pointed manner.
#
And he would sometimes kill copies.
#
That was a real fear that ultimately it would not go.
#
And I think in a couple of instances, he has killed my copies also, one or two copies.
#
And though they deserve to be killed.
#
And in one case, I actually reworked it and then he cleared it also.
#
So it was very, very rational sort of dialogue.
#
I sort of, I benefited from the mentorship and the guidance and the feedback, sometimes
#
very critical feedback from all of them.
#
And one feedback I consistently got in my early months was that I had an academic overhang,
#
which was understandable because I'd come from a research institute and finish my masters
#
So I worked hard to sort of get over it.
#
I don't think I managed it till many years later when I fully realized.
#
But one thing that always struck me was suppose I came back from a field from somewhere else,
#
wrote a report and submitted to Niranjan.
#
Niranjan would take his time in sort of getting back on it.
#
But then he would come up with very specific questions or comments that this part is problematic
#
It was not a vague thing that you don't know how to write or this thing.
#
So he would always tell me you're a decent writer, but sometimes in some places it's
#
So I think it was easier to improve with that kind of specific, precise feedback and which
#
I have tried to emulate in my own work as an editor.
#
I usually don't say that the entire copy is rubbish.
#
Sometimes I do when the entire premise is wrong, but then I say that the premise is
#
I mean, this is not a story.
#
And that copy is not actually written because in plain facts, you actually have to submit
#
a pitch with what you're going to do, what are your methods and so on.
#
Only after that is approved, can you start reporting and then writing.
#
So it's a multi-step process.
#
So at the pitch stage itself, I kill ideas because I don't want to waste the reporter's
#
time also, and usually data stories take a fair amount of time.
#
And a fair amount of data work goes in, many hours of work goes in after I approve the
#
So from a practical point of view also, it makes sense.
#
So those kinds of feedback, I think, helps improve one's writing.
#
And the final thing is initially, you have these people whom everyone acknowledges as
#
very good writers like Niranjan, Manas Chakravorty.
#
Then stuff they wrote would make people sit up and go for a meeting next day, someone
#
And you know it has made an impact, you know, people are talking about it.
#
And so naturally, you want to emulate, right?
#
And instead of emulating, you end up imitating.
#
So initially, for a while, I used to imitate how Niranjan approached.
#
Then I used to imitate how Manasa approached.
#
And it took me a while to realize that this is not working.
#
And both have their own kind of chairs, isn't it?
#
You can't make those chairs again.
#
So then I began talking to them about what it is, how they approached it.
#
You know, what is it that they read before they started writing?
#
What are the changes they made after they had decided the topic and the flow?
#
Whether there were any last minute changes?
#
So that writing, and I didn't do it in a very sort of note taking kind of a way.
#
It was just over coffee or this thing.
#
In January, I said that I like this, and then they will tell the story.
#
You know, it was just exchanging.
#
It felt like we're just gossiping about writing.
#
It was, it never felt, and I also didn't consciously sort of this thing.
#
I looked up to them, of course, and I wanted to learn from them.
#
But it happened very casually, or at least they made me feel that, you know, we're talking
#
So I think the credit lies with them, nothing to do with me.
#
Those conversations helped a lot.
#
If in my early years I didn't have those conversations, I don't think I could have survived till now.
#
But despite all that, it is, much of it is also self sort of this thing.
#
You go back and look at some of your old copies and you go, yeah, and you realize that, you
#
know, some bad habits you have left.
#
Sometimes while you're, while reading your own work, even recent work, you feel that,
#
no, this could have been, you could have done better.
#
And then you frame a rule internally in your mind that next time you should avoid this.
#
So it's a mixture of, I think, all of this, but this early environment and what kind of
#
feedback you get, how precise that feedback is, makes a huge amount of difference.
#
And you know, earlier you spoke about how you became a better manager when you started
#
thinking of managing as a skill.
#
Now when you became an editor and you were, you know, guiding other journalists and approving
#
copy or suggesting changes, did that make you a better writer?
#
Because what I have found is that the act of like, I've been teaching an online writing
#
course for a few months now, and I feel that I'm just looking at my own writing differently
#
now because it's just so much sharper.
#
And I keep telling my students that the main thing is mindfulness, that you might have
#
a different view of a particular piece of copy than I do, and that is fine.
#
But the important thing is that you're mindful of all the choices you make and you know why
#
And similarly, I feel that that's made me more mindful of my own copy, why am I doing
#
And you know, or just reading the pieces to give feedback, I kind of realized that that
#
kind of gaze helps me with my own work also.
#
So has that been the case with you?
#
Like, do you feel that that added an extra layer of awareness to your writing?
#
So that is one part of it.
#
I became more mindful and I started then avoiding the same things that I asked.
#
Because one has to practice what one preaches.
#
But even beyond that, around that time, because I became an editor, and of course, Niranjan
#
wanted me to sort of make my own judgments and choices.
#
So very often he would leave it to me to shape the final this thing and he would take a look
#
much later or after it had been published, he would give me feedback.
#
So it was also a sense of responsibility that the buck now stops with me and especially
#
So that I think was a huge sort of this thing that there is no one now in the system.
#
I still sometimes request him to take a look at my work and he does.
#
But that that sort of forces you to be much more careful, both in your choice of words
#
and the approach you take when you write something.
#
So that definitely so mindfulness and the sense of responsibility both sort of shaped
#
my later period writing definitely.
#
I think it made me more precise or more sort of made what the messages I wanted to convey.
#
I think I was able to do it with a greater amount of precision compared to early.
#
So let's talk about data now, like I've done an episode with Dinyar Patel on Dadabhai
#
Navroji and Dadabhai Navroji, of course, was in a sense a pioneer.
#
I don't know if he was the first, but he was certainly a pioneer in terms of using data
#
to figure out what is the state of the economy.
#
And he did it with a political purpose, where the purpose being to sort of push back against
#
British claims of how how the governance was the first poverty line in India, so to speak.
#
And you've pointed out that, you know, T N Srinivasan coined that.
#
But Dadabhai Navroji essentially came up with that.
#
And that's just like pioneering work.
#
So it's it's, you know, using data not in a driveway, but number one, to make a political
#
point to counter a false narrative.
#
And you know, you can't argue against data and to to also sort of have more clarity on
#
the state of the world, what it is actually like.
#
Tell me a little bit about sort of the evolution of data science in India and its relationship
#
with governance, because it's very interesting.
#
And one of the things I didn't realize, like I was listening to your interview by Milan
#
Vaishnav on Grand Tamasha, where you spoke a bit about it, where I was sort of startled
#
by how India, in a sense, actually led the rest of the world on certain things, which
#
I found so fascinating.
#
And so tell me a little bit more about this history of data science, as it were, as this
#
had gone through the years.
#
Yeah, I would rather use the term statistics.
#
Yeah, that's what I prefer.
#
And India was definitely one of the early pioneers.
#
The Indian Statistical Institute was set up much before statistics departments were set
#
And in fact, many American universities hired graduates from ISI to set up those programs
#
So we had a lead over any other developing country by far, and we had a lead over most
#
The only two countries which I would say were more advanced in statistics than India.
#
One was the UK, where much of the early work on probability sampling and so on occurred.
#
And the other was USSR.
#
And partly because of Soviet planning and so on, they had to rely a lot on statistics.
#
And both this English and Russian experiences shaped statistical thinking globally, including
#
But India had its own sort of experiments and surveys and so on.
#
And you're right, the other analogy, that his work gave the sort of initial impetus
#
to many nationalists to start looking at the economic questions, the kind of exploitation
#
that had happened and to what extent it had happened.
#
And a lot of it in the early work were based on very provisional, preliminary, rough data.
#
Much of it was to advance the nationalist line of argument without the rigor that was
#
And the first to sort of make a difference and to produce really rigorous estimates of
#
India's national income as such was VKRV Rao.
#
This was in 1931, if I'm not mistaken.
#
I'm slightly bad with dates, so unless I've read the paper yesterday, I may not be able
#
to sort of exactly, but early 1930s, let's say.
#
And so that was one effort.
#
The other side, parallelly, was this young physicist called Prasanth Chandra Mohalanubis
#
from a very aristocratic family in Bengal, a Brahmo family, very close to Rabindranath
#
Tagore and that social milieu, who suddenly decides in the middle of his physics education
#
that he has to do statistics.
#
And in fact, he comes back and joins the physics department and sets up a small unit.
#
That is the Indian Statistical Institute.
#
That's where it started.
#
If I'm not mistaken, this is also in the late 1920s, very late, it would be early 1930s.
#
But if I'm not mistaken, it is late 1920s.
#
And the first sort of surveys are initiated there.
#
This is remarkable because it is only in the 1920s that probability sampling has first
#
So within a few years, this has traveled to India.
#
This is a remarkable speed at which India picked this up, thanks to Mohalanubis.
#
And also thanks to another economist in Gokhale, B. R. Gadgil, who had also started doing some
#
And among these, Mohalanubis had the widest range of connections and intersections both
#
with a global sort of statistical establishment, including the Royal Statistical Society and
#
other statisticians at UK and USSR, and also with India's own political leadership and
#
administrators in the Reserve Bank of India and so on.
#
And Mohalanubis' contribution to sampling theory, he made significant contributions,
#
including for instance, this idea that you should do a pilot survey before doing a full
#
No other institute did it.
#
And then it has become now a standard everywhere, before you do a full scale survey.
#
So that originated in India.
#
There were other such contributions.
#
But the bigger thing was to do this household level surveys at a large scale.
#
That was done for the first time in India, and Mohalanubis was the pioneer.
#
And on the one hand, he convinced the nationalist leadership in the Congress, thanks to people
#
like Deshmukh, who introduced him to Nehru and others.
#
C. D. Deshmukh was in RBI at that time, was the RBI governor, later became finance minister.
#
And Deshmukh convinced Nehru that basically this man is really talented and you need to
#
And Nehru understood that without filling these data gaps, we couldn't plan properly.
#
So it was also, we had already sort of decided that we would take the planned approach.
#
The political leadership had decided.
#
The industrial lobby in Bombay had also backed that plan, 1944 famous Bombay plan.
#
So the entire elite class was agreed on planning, and you cannot have planning without data.
#
So that was the big impetus that drove much of these efforts.
#
But beyond that, Mohalanubis was also interested in influencing the trajectory of data collection
#
and statistics globally.
#
So when the first United Nations Commission on Statistics meeting took place in New York
#
in 1946, Mohalanubis suggested that we need a proper standardized manual for conducting
#
surveys, otherwise the results will not be comparable across the world.
#
And he was asked to chair that subcommittee on sampling, which produced that first set
#
of manuals that were used globally by all countries.
#
So he practically wrote the handbook of surveys, basically, you know, and later on when ISI
#
got government recognition, it scaled up.
#
Most developing countries would send their people's official statistics to India to
#
learn statistics and so on.
#
In fact, the vice premier of China in 1956 had come to ISI because China was very backward
#
Their line of communism had apparently rejected a lot of the Marxist approach, including probability
#
sampling, which was very strange.
#
But they realized their mistake and they were missing out on something.
#
And he came to ISI and the meeting ran much longer than and his entire schedule got disrupted
#
And he insisted on knowing exactly what it is that Mohalanubis did, his team did, and
#
insisted that there would be a Chinese delegation which he would send and Mohalanubis should
#
entertain and educate them before sending them back.
#
So there was this global interest in what India was doing, India was leading the charge.
#
And there were a whole new raft of innovations, you know, setting up this national sample
#
survey, central statistical organization, annual survey of industries, agricultural
#
surveys, RBI started surveys on rural credit.
#
So across institutions and through interconnected individuals, Mohalanubis, VKRV Rao, Gadgil,
#
there was this whole nation building and statistics building sort of approach at the same time
#
that could inform the planning process and make India a stronger economy.
#
So they may have different approaches to statistics and so on, but all of them were agreed on
#
the final goal that you need better data, you need to fill the data gaps, and hence
#
you need an independent sort of survey mechanism.
#
And it was not an easy sort of process.
#
In many cases, you needed to handle this large scale household surveys.
#
You didn't know how to handle it because you were doing it for the first time and there
#
were no global examples.
#
India had to import second hand IBM machines from the US and Mohalanubis somehow managed
#
A lot of people thought that Mohalanubis was too close to the USSR, you know, because he
#
was also the originator of the second plan, which followed the industrialization led model
#
of economic development.
#
But what is not known is that he had equal range of interactions with scholars from all
#
And it was through American help that those IBM machines came to ISI and helped in the
#
tabulation of NSS data, otherwise those survey would have been useless or it would have taken
#
much longer for them to arrive by which the timeliness of the data would have been lost.
#
So Mohalanubis was quite agnostic and whoever provided help, he was ready to take.
#
And in fact, there are now declassified Soviet archival work, which other historians have
#
looked at, where they say that KGB and other Soviet agencies are actually suspecting that
#
Mohalanubis had moved to the other side or something of that kind.
#
So he was viewed with some amount of skepticism on both sides.
#
But in a way, what he was basically pursuing was national interest.
#
And he helped generate this awe and respect for statistics and Indian statistics globally.
#
He helped establish his reputation and he also helped establish its acceptance in India.
#
He got the leading statisticians of the world to come and review the first NSS reports.
#
And their conclusion was that there were many areas of improvement.
#
But they said, and this includes people like Fisher, who is considered one of the fathers
#
And they said that as far as the sample surveys are concerned, the world has much more to
#
learn than to teach from India.
#
And this is what built up India's official statistical reputation.
#
And even Angus Deaton, the recent Nobel Prize winner, he says that where India and
#
Malanubis led, the rest of the world followed.
#
So we often hark back to some ancient golden era, where we had done these fantastic achievements
#
in mathematics and sciences, some of it real, some of it imagined.
#
There are real contributions also.
#
But we don't need to look that far back as well.
#
Our achievements in statistical science, in applied statistics, is phenomenal.
#
Yet we do not give it the kind of recognition it deserves.
#
And this is particularly true now because there is an aversion to anything associated
#
But this was also true earlier.
#
It is not the case that this aspect of India's intellectual contribution was highlighted
#
in a significant manner by people outside the statistical establishment.
#
People within that do know and do talk about it.
#
But otherwise not so much.
#
Even economists take our data for granted or till recently have done.
#
They too do not engage with the data systems to the extent that is perhaps required.
#
And one reason why there has been a decay over the years, especially in the post Malanubis
#
era, is partly because of that.
#
The amount of external involvement, the amount of involvement of independent academics who
#
could review, who could assist, who could engage with the official machinery and improve
#
their work has steadily come down.
#
It has not happened in a day.
#
There has been a long period of bureaucratization.
#
NSS governing councils used to be headed by academics and dominated by independent experts.
#
Only one or two bureaucrats would be there.
#
This process got reversed.
#
A lot of powers of the governing council were taken over by the Ministry of Statistics,
#
which was early in the same ministry which handled the Planning Commission.
#
So a lot of these changes started happening from the 80s onwards.
#
And then 1991 liberalization unfortunately led to a cut of funds for all departments,
#
including the statistical machinery.
#
And because of India's liberalization and its globalization and standardization of data
#
and all this became a priority, IMF came up with data standards based largely on the
#
requirements of developed countries and created a uniform set that every country had to follow,
#
And so the data demands of certain kinds went up, which may or may not have matched with
#
India's interest, but which India had to follow because it was a signatory to that.
#
And in the early 90s, India was also getting IMF funds.
#
So from the 50s, when India was setting the standards for the world and India was setting
#
standards in the world based on its own experience as a developing country, as a third world
#
country, the wheel turned full circle.
#
Now we have come to a stage where we just accept standards given by others.
#
We no longer have leadership as far as official statistics is concerned.
#
There are still many brilliant statisticians in the country, but their engagement also
#
with the statistical establishment is far more limited today than it was in the past.
#
And many people, even from issues like the Indian Statistical Institute who graduated,
#
they do not get into the survey work or statistical establishment work and so on because there
#
are many other lucrative options, including in the private sector as well as abroad.
#
So the influx of talent into the official statistical system has also suffered.
#
And a combination of this has resulted in the fact that today, statisticians find it
#
very difficult to defend their work, which was not the case earlier.
#
And hence it is much more easier to attack their work.
#
Even when those people were attacking, often do not know the basics of what they're talking
#
So a couple of observations around a couple of broad themes and the second one kind of
#
leads to a question of sorts.
#
Like my first observation is to tell my listeners and to remind myself that the level of statistics
#
that we have today is actually really recent, like you pointed out India's contribution,
#
Mahalan Abyss' contribution, but just to go a little back in history, and I explored
#
this in an episode on the GDP with Rajeshwari Sengupta, but just looking at the GDP as one
#
kind of interesting statistic that has come up.
#
After the First World War, you know, the winners met around a table with the loser Germany
#
and they were deciding on what reparation should Germany pay.
#
And they had no way of knowing what Germany could actually afford to pay because there
#
was no GDP, there was no measure of the economy.
#
So they set a large number that proved to be too large, and too large in what sense
#
Germany had no way of paying that kind of money as reparations.
#
So they ended up just printing money so wildly that there was hyperinflation.
#
And that hyperinflation, you know, had consequences on their economy and their society, which
#
indirectly led to the rise of Hitler and Nazism and even the Second World War, the seen and
#
the unseen going that deep.
#
And around 1920, Keynes wrote this book called The Economic Consequences of the Peace, where
#
he warned against this happening.
#
And some of the impetus for creating a measure like a GDP came out of that and then the Great
#
Depression in America that we first need to measure this before we can talk about it.
#
And that's the importance of statistics.
#
And Simon Kuznets, who is one of the pioneers at, you know, creating the GDP and getting
#
that measure about, however, did protest to one aspect of the GDP as it was adopted, which
#
is that government spending was counted in the GDP.
#
And Kuznets' point was this, that a government can therefore game the GDP by just digging
#
ditches and filling them up.
#
It's all government spending.
#
It's kind of easy to game.
#
And therefore you game that statistic, you can use it in the service of politics, whatever
#
Now what I find also interesting about India, and you mentioned the Soviet Union also being
#
so advanced in statistics, is that there was an ideology which I feel was flawed, which
#
is this whole central planning planned economy mindset.
#
That this flawed ideology is leading to the birth of a methodology and a practice that's
#
actually incredibly useful.
#
Like you know, the Soviet Union, one of the things that they did to push their kind of
#
nationalism was the focus on chess, where chess was taught in kindergartens in all schools
#
And through their dominance of chess, they wanted to send a message to the rest of the
#
world about their intellectual superiority and so on and so forth.
#
So that's a good consequence of a bad way of thinking.
#
But in a sense, when I look at all the sort of the pioneering work by Mahalo Nabeus and
#
all of that, the work that comes, it also seems to me that a lot of the statistics feel
#
so necessary, because the more you are in the planning mindset, the more you need the
#
statistics because you are like, you know, what Adam Smith would have called a man of
#
system, you know, everything is a chess board and you're trying to just get everything
#
And of course, we know that economies and societies don't work like that, and they're
#
more complex, but the good part of it is that you have the statistical systems coming up
#
And you'd imagine that now that all of it has come up, now that India has been a pioneer
#
in so much of this, that the same standards will continue.
#
And as you point out, there's been a decline.
#
And I wonder at some level, whether that decline in a sense is inevitable, that your initial
#
thrust towards building these systems and being a pioneer is, you know, whether it's
#
an ideological impetus or whether it's a particular kind of passion or whether it is a great man
#
theory of history, Mahalanobis himself being a visionary and doing all of this, eventually
#
the inertia of the system takes over.
#
And the Indian state is an inertia machine.
#
And eventually that takes over and that leads to whatever declines have sort of happened.
#
So, you know, the rest was an observation.
#
But in the last part of what I just kind of said, the inertia of the system and our statistical
#
systems, you know, just kind of rumbling along without having people with drive and passion
#
in them, without having that sense of importance that bloody hell these statistics matter.
#
You know, what is your sense of all of this because you are so much closer to it than
#
Yes, so that is certainly the case.
#
You do not have that kind of dedication and the sense of mission that was there in the
#
And it is true of many fields and many public policy institutions.
#
So in that sense, it is not an exception.
#
Having said that, the rank and file statistician today in India's statistical system is still
#
very proud of that legacy and still strives to protect it.
#
And even many senior statisticians are aware of that legacy and try to their level best
#
to achieve what it was set up for.
#
And it is thanks to them that we get many of our stories when reports are attempted
#
to be suppressed, they fight back.
#
And we should not take this for granted.
#
You know, had these efforts not been there, we would have been in far worse condition.
#
Not just because I wouldn't have got stories, but we would not have understood why certain
#
things are happening, why certain data sets are coming and others are not coming out.
#
So the point is that some of it is not that everything is lost or nothing of it remains.
#
It is just that you don't have that kind of statistical leadership.
#
So I would rather think that it is that gap which is missing and which can be institutionalized.
#
So in fact, if you look at the Rangarajan committee report, they said precisely this.
#
Mahalanubis was an ordinary statistical advisor to the Nehru cabinet and he was of course
#
an extraordinary person, whether you believe in the great man theory or not.
#
Now you can't expect the same now in 2000 or whatever, when the report was being written.
#
So you institutionalize a system where a group of people will sort of oversee and will stand
#
a bit distant from the overall system.
#
There were attempts to create such a system, there was this legal luminary called Madhav
#
Menon, who chaired a committee to prepare such a structure, something like the election
#
commission for statistics, for instance, which will ensure fair play rules, investigate if
#
there are these things, hold departments to account if they produce bad data and so on.
#
And the UK has such a system or has transitioned to such a system.
#
Now there are other countries which are moving.
#
Almost all major economies have a system of statistical audit.
#
India doesn't have one.
#
The only one that was attempted was in 2011 for IIP and it was just an experimental sort
#
I don't think even that committee's recommendations have been implemented.
#
So the idea is that yes, a lot of things have declined, but there are a lot of things that
#
A lot of things can be improved, maybe not in the same way as it happened in the past,
#
maybe not using the same approach, but through different means, different institutional mechanisms
#
and we should try them out.
#
Similarly, regarding your point about planning and statistics, yes, it was a strength, but
#
in some ways it was also a weakness because the moment you started opening up your economy,
#
you dismantled the license permit Raj.
#
Producers of goods and services are no longer, they no longer feel it necessary to tell you
#
how much exactly they're producing.
#
You're opening up the system earlier for every extra production you have to apply, for every
#
extra unit you have to apply.
#
So the state had, was it the commanding house of the economy and it had the information
#
set required for everything.
#
And you just need to call up someone from another department, you'll get the data if
#
And so the whole that came up was in the, and the scale of the private sector in the
#
economy grew because the public sector shrunk.
#
So you had this growing private sector and you had this opening up of this license permit
#
Raj, which is required economically, but statistically it is a disaster.
#
You lose all that information.
#
And since you didn't plan for it statistically, you are left with a gaping hole and which
#
till this day we are struggling to fill.
#
In fact, the controversy of the latest GDP series, 2014-15 series, which continues to
#
till this day, and which I'm sure Rajashree Sengupta must have touched upon.
#
She did work on deflators around that.
#
It's precisely because of this, it is the private corporate sector component of the
#
GDP that is, that has proved to be most troublesome and most hard to pin now.
#
So, it is not just, you know, that one government came in and happened and so on.
#
This is a long story of, you know, various things coming together and a constellation
#
of forces sort of determining the trajectory of a statistical system and you finally arriving
#
But as I said, this is some parts of it are broken, but these are things that can be fixed.
#
So couple of provocative questions, a two-part provocative question rather.
#
And part one is that if the planning mindset is no longer valid, like we both agreed at
#
liberalization, you know, got so many hundreds of millions of people out of poverty, it was
#
a great thing, it was necessary.
#
But as you pointed out, it affected the quality of our statistics.
#
So the first part of my question is that, and this is meant to be purely provocative,
#
so people on Twitter, please don't abuse me, which always happens when I ask provocative
#
But it's worth thinking about.
#
Instead of the planning mindset is invalid, which I believe it is, then why are measures
#
like the GDP even necessary?
#
You know, let people live their lives and do whatever.
#
Why does a government need to know, for example, like I understand in the interest of truth
#
in the interest of all of us as citizens, we want to know how's the economy doing.
#
But why is it even necessary?
#
And the second part of the question is whatever your answer to the first part.
#
The point is that I just feel that all statistics in a way are deeply inadequate in ways that
#
the common public doesn't take into account because they're using it a shorthand, which
#
You use it a shorthand because what else do you do?
#
But the GDP, for example, by sitting together and having this conversation, like even if
#
I wasn't recording it and even if this wasn't going out as a podcast, I think both of us
#
would agree that if we have a three hour conversation with each other, both of us benefited.
#
It's a positive sum game.
#
The value in both our lives went up.
#
That's not measured by GDP.
#
GDP would not measure, for example, the unpaid labor that women do everywhere.
#
GDP doesn't measure so many things.
#
You know, GDP can measure that I bought a computer today and I had bought a computer
#
seven years ago, but it cannot possibly measure how the computer today gives me so much more
#
productivity and power than a computer seven years ago would have.
#
I don't mean to kind of focus on the GDP in particular and make it like a whipping boy
#
for this hypothetical question, and we'll discuss the importance of getting good data
#
and understanding the world better right after this.
#
But just in terms of I see a lot of narratives constructed around this data that, oh, the
#
GDP went up by so much or went down by this much or, oh, the formal sector has grown by
#
so much or, you know, and you did a great piece about how that isn't necessarily the
#
And my friend Mohit Satyanand had that in his latest newsletter, which came out today.
#
And the data there is incredibly important.
#
But what would be your response to these questions?
#
These are rather difficult questions, and I'll try and answer them as best I can.
#
I mean, they're not the perfect answers, perhaps.
#
So on the first thing about planning and if you don't need planning, then do we need data
#
Maybe the answer is probably no.
#
But then is that a practical thing?
#
You know, I've always believed in practical things.
#
And even if there is some theoretical argument, it has to keep in mind what the reality of
#
The reality of the world is there's no country that does not practice planning, whether they
#
call it planning or not.
#
Even a lot of the American programs, all their funding abroad, their aid agencies, even defense
#
planning for strategic planning is necessary.
#
I mean, today you have a threat like China, which in this particular moment may not come
#
all the way down to Tezpur, my hometown, as they did in 1962, but it is definitely a threat.
#
And especially for that part of the country, for Arunachal Pradesh and so on.
#
And the answer to that threat may not lie in Arunachal.
#
It may lie somewhere else.
#
It may lie in South China Sea, it may lie in commerce, it may lie in some other kind
#
of, but the state has to plan, right?
#
And this is something even Adam Smith would say that, you know, the first duty of the
#
sovereign and continuing with Smith, he would also say that the state needs to take care
#
When a pandemic happens, you need to step in and so on.
#
So all of these things are there even in the work of classical liberal sort of economists,
#
although none of them came up with this GDP concept, as you said in 1930, so it's not
#
Nonetheless, the world had become, the economy had become much more complex.
#
So while you were measuring all this coal production, power production, et cetera, separately,
#
there was no single metric or no uniform metric.
#
So despite planning, do you need that kind of a single metric or do you need a dashboard
#
of measures which captures the reality in a more richer form is, I think, a more interesting
#
and more practical question in that sense, and which I have often thought about.
#
And at the moment, I don't have a yes or no answer.
#
I think at the moment, I would want to have both kinds of measures and I would want policymakers
#
to pay importance to both these things.
#
Because for some things like figuring out whether your public debt is too high or not,
#
you cannot rely on a dashboard, various others, 20 other metrics to figure.
#
You need one single metric with which you will normalize.
#
So this is just a mathematical necessity.
#
Be a sort of common sort of measuring rod with which you can measure economies, with which
#
you can measure the overall economic progress of the country will always be required.
#
So if the government doesn't produce GDP, there will be many other people in the private
#
market who will produce alternate estimates and it will be, they may be even worse than
#
what we have in the form of GDP.
#
So to some extent, it is a bit of an inevitable sort of this thing that everyone relies on
#
So how do you compute the GDP and how to make it better?
#
That is definitely a question worth asking.
#
At the same time, can you supplement the GDP with a broader dashboard of measures is something
#
that a lot of people have thought about and a lot of people have suggested alternatives
#
or metrics that should be used.
#
GDP itself is undergoing sort of revisions and I had written about this, how climate
#
change has forced this transition a bit faster than was earlier planned by bringing in natural
#
resource accounting that you just don't look at the flows of goods and services, you look
#
at the stock of different kinds of capital, including human capital, natural capital and
#
so on, so that you can measure the rate of depletion.
#
So it was all fine if you're building a lot of buildings, highways, flyovers, et cetera,
#
but if you're devastating your natural capital at a very fast rate, soon you will run out
#
of those resources required, at least some amount of those resources are required.
#
And currently you don't have any indicator, which is part of your conventional economic
#
So you need to integrate that and that work is going on and it will take some time for
#
us to arrive at a sort of reliable this thing, but that's fine.
#
So those are some sort of reforms or this thing which can take us forward.
#
At the same time, I think this dashboard idea also should be pursued.
#
So at Mint, for instance, we had done a very simple sort of thing.
#
We used to look at 16 high-frequency monthly indicators, used to call it the Mint macro
#
Look at four producer economy indicators, four consumer economy, four ease of living
#
and four external sector indicators.
#
And just tell the readers whether out of the 16, 8 are doing better than the past, 12 are
#
doing better than the past or not, and use some standard, easily explainable metrics
#
on how we are doing the normalization and so on.
#
And in some ways, this is not a very original idea.
#
If you read this book, GDP, a brief but affectionate history by Diane Coyle, she also mentioned
#
that eventually I think we should consider this dashboard approach and so on.
#
We just thought we'll put it into practice in this when a lot of financial market analysts
#
also look at these kinds of dashboard metrics.
#
So it is not that everyone relies blindly on GDP data before putting in their money
#
Even sections within the government, within the RBI look at broader range of indicators.
#
But among all of them, GDP will still remain important.
#
As you said rightly, we should use them knowing that they are shorthand, that they are not
#
the whole story, they are metrics.
#
But that is true of almost any statistic.
#
If we extend that logic that it is incomplete and therefore we should not use it, then we
#
might as well stop using averages or the arithmetic mean.
#
And in fact, if you look at the history of statistics and the history of the world before
#
mean median mode started to be used, it was far poorer.
#
And this was a tremendous discovery that using mean median mode, you could make sense of
#
the world and convey it to a simple audience.
#
So I think the production of statistics, the evolution of statistics, it happened, co-evolved
#
with the evolution of the economy and the polity.
#
So because different democracies sprung up in different parts of the world and because
#
the economy became more complex, it is a combination of these two processes that has created the
#
need for these measures to get a better sense of ourselves and to plan for a better future,
#
which is a very sort of realistic sort of goal, even if it is not through Soviet style
#
Diane Call's book is wonderful and I'm also sort of reminded of that old saying that, you
#
know, all models are wrong.
#
Some models are useful.
#
So I think that's something that you can look at certain kinds of data and say that fine,
#
you know, the model may not be exactly right if you're going to interrogate it to the finest
#
detail, but is it useful or not?
#
And if I may just add to that in the same vein, this is also the saying, I forget who
#
who says that it is easier to lie without statistics than with statistics.
#
So you can still lie, but you can eliminate some possibilities of lying when you have
#
That's a fantastic saying.
#
The other thing that sort of strikes me is that like number one, my provocative question
#
wasn't designed to suggest that we don't need data.
#
In fact, I think we need data more than ever, except that now I think the demand for data
#
is a bottom up demand, like statistical systems originated from this top down impulse where
#
the state is saying that we need to design this chess board and run it properly.
#
Now, I think the reason data is important from where I stand is because people, citizens
#
like us need to be empowered with information about what is the state of the economy, to
#
be able to judge governance, like in a democracy, if we are going to vote for people who govern
#
us on what basis are we going to vote?
#
Data is absolutely critical to that.
#
Now, what strikes me is that data, like even though I understand that within the state,
#
there are institutions which have been running independently, well set up, stocked with good
#
Over a period of time, it seems inevitable to me that as more and more institutions of
#
the state are co-opted and used in the service of politics, that a lot of that data becomes
#
suspect or as has happened in recent times, just stops coming out altogether.
#
You have less and less data to work with.
#
And therefore it strikes me that just as the impetus came from the state and all these
#
institutions collecting data were from the state, today, if the impetus is bottom up
#
from the citizen, then I would imagine that there would more and more be a movement to
#
get this data collection and have these institutions replaced by private institutions, at least
#
supplemented by private institutions.
#
Now in your experience, how much of that has been happening?
#
Like you told of your dashboard with 16 monthly indicators, how many of them were data collected
#
How many were independent?
#
Like, did you feel that you have to do Jugaar in certain ways of trying to figure out what
#
Like just the estimation of the size of the economy in a sense involves some Jugaar because
#
you don't know the informal economy, for example.
#
So you're using proxies and you're doing all of that and you're coming to the best
#
estimate that you can in good faith.
#
How has that landscape kind of evolved for you?
#
Because I would imagine that even a Jugaaru way of doing things could lead to a new way
#
of looking at the world also, because just in finding these proxies, you might discover
#
interesting ways of seeing the economy as it were.
#
So tell me your experience of all of this over the last few years, because I imagine
#
it must kind of be a mix of both great frustration and great innovation, you know, always trying
#
to build the best picture you can, but not having the tools necessarily to work with
#
Yeah, absolutely right.
#
So I would like to say that the overall data availability has gone up and it has gone up
#
hugely and it is largely public data.
#
There's some amount of private data also, but much of the volume has come up and it
#
Just to give a simple example, you didn't have vehicle registration data on a daily
#
It was unthinkable 10 years ago.
#
So now you have that kind of data, so you can track at a state level, even beyond and
#
different kinds of vehicle getting sold, whether this is a commercial vehicle, whether it is
#
a personal vehicle, two wheeler, four wheeler.
#
And that also tells you a lot about the state of the economy, four wheeler sales are going
#
If used cars are getting sold, but two wheelers are not getting sold, then certain parts of
#
the economy are not doing well.
#
If commercial vehicles are getting sold at a higher rate, that means there is probably
#
a chance that the capacity utilization of factories are going up.
#
So these have economic significance and they help us and we have used them as thoroughly
#
as we can and they have been of great help in recent years.
#
Apart from that, we have used some amount of private data from CMI.
#
Now CMI's job survey, I do have some reservations about those are methodological in nature.
#
It is not because it is a private body.
#
It is just that the survey method is something that has raised a lot of questions.
#
And I think those questions have not yet been satisfactorily answered.
#
Maybe they will revise their methodology and future rounds will be different.
#
But there is still a problem, especially when it comes to survey data, because think of
#
When you do a survey, you want it to be representative of the population, right?
#
What that means is every individual in the country should have an equal probability of
#
being selected, equal chance of being selected.
#
They will not be selected, but they should have an equal chance.
#
There should not be a bias in that.
#
So if you think of it as a machine which is spitting out someone's name, Amit Varma,
#
Pramit Bhattacharya, the number of times if it is an infinite sort of game, then the number
#
of times Amit Varma is uttered should be the same as Pramit Bhattacharya, if there are
#
only one Amit Varma and one Pramit Bhattacharya.
#
There are many more Amit Varmas.
#
So then it will depend on the number of Amit Varmas there are in the country.
#
And if there are more Amit Varmas than Pramit Bhattacharya, then numbers should be called
#
So that is the logic of probability sampling.
#
To execute that logic in a flawless way, you then need a list of people of the country.
#
And further, when you do it in the real life, because you are segregating by region, you
#
need an adequate representation of gender, region, caste and so on.
#
You need much more finer details at the ground level, local level, what they call the primary
#
sampling unit or the first stage sampling unit as NSS calls or used to call.
#
I don't know what they call them now.
#
Now for a survey organization like the National Sample Survey, they come with the authority
#
They can ask the election commission for data.
#
They can use electoral rules.
#
They have other administrative.
#
They can even look at the census raw data and from that frame, they can select a representative
#
So there's a legal sort of limitation in how far they can do.
#
Although private researchers and surveyors have also used electoral rules.
#
But to the best of my knowledge, CMI has not, or it uses something else, I'm not sure.
#
But I mean, this is what from what I could understood that at least in certain states
#
in certain parts of the country, there are problems in trying to get fully representative
#
The other issue is to consider that at the end of the day, all of, much of these statistics
#
are also public goods, right?
#
They're non-rivalrous and non-excludable, right?
#
So you can use it for a different purpose.
#
I can use it and we can both use it without diminishing in the value of that statistics.
#
And the origin of the statistics is not able to fully capture the value generated by that
#
survey or the data set.
#
And public goods, if you just, if some particular good is by definition public good, similarly
#
think of something like pandemic management, right?
#
However rich a man is, wealthy a man is, or a firm is, whether it be Mr Ambani or Mr Adani
#
or so, they do not have that kind of incentive to prevent a pandemic in India.
#
Mr. Narendra Modi does because he heads the state.
#
So as the representative or the chief representative of the state, he has that responsibility.
#
So all kinds of public goods have that kind of a feature that if we don't have that statistics,
#
something will go wrong.
#
You know, they have that negative kind of connotation also that if we don't have, we
#
can't plan or if we don't have, then we can't do this.
#
So there is that element, which then means that when they're collected by official state
#
agencies, they're collected as a measure of public good, not specifically because some
#
companies which are going to buy that data are going to benefit from it.
#
In CMI's case, some of their sales will obviously happen to think tanks and academics and so
#
And it is in the nature of quasi-public good, but they want private returns for it and reasonably
#
so they are not funded by taxpayers.
#
At the same time, much of their funds will also come from private marketers and so on
#
who are actually interested in the consumer economy.
#
They are not interested in the entirety of the Indian economy.
#
And there is a dissonance between the two and it is going to remain for the foreseeable
#
So if 90% of your sales are coming from 40% or 50% of the consumers, you can ignore the
#
You are interested in as much data as you can about that.
#
If you look at at least as far as I could make it from CMI's sampling strategy, it sort
#
of reflects that pattern.
#
There is much more deeper sampling of urban units, heterogeneous small towns, big cities,
#
etc. etc., which even NSS doesn't do.
#
And the rural parts are all clubbed together.
#
And for rural states like Assam or Gihar, you will have two or three such aggregate
#
Whereas for a state like Tamil Nadu, you will have many more blocks from which random samples
#
So the entire approach, the entire methodology, therefore everything is shaped up by what
#
a survey or a data set is designed for.
#
And hence it is important to, so the role of the state and the role in production of
#
statistics and creation of statistics will remain, which leads to the conflict that you
#
That if the state is producing statistics and when the state, we mean that it is not
#
just the state, it is a government, the Modi government, the Manmohan Singh government.
#
And they have a vested interest in not showing us statistics that throw a dark light on them.
#
In recent years, these have come up in the forefront.
#
Earlier years also, there were attempts.
#
In many cases, Sena was prevailed and the data was finally released or the survey was
#
commissioned despite some opposition.
#
But in some cases, they were not.
#
And then whether that decision to discontinue a survey or hold back is because of public
#
interest or because of the interest of the survival of the government becomes the key
#
So then what do you do?
#
One is to say that, let's forget about government statistics and they just rely on alternative
#
metrics or let's look at third party surveys or let's look at surveys which are designed
#
for a certain purpose, but which are also throwing up ancillary data, which then can
#
be used to cross-tangulate and figure out what is happening in the country.
#
So to some extent, that approach can be used.
#
So for instance, when the NFHS 2015-16 numbers came up, we used that to look at how affluence
#
had shifted from 2005-6 to 2015-16.
#
That is not what NFHS is designed for.
#
This is a primarily demographic and health survey.
#
But to assess how demographic and health behavior and outcomes vary across different groups,
#
they were collecting data on a range of household assets and amenities, from which we could
#
also design an assets index and amenities index, which we did.
#
And therefore we showed that the divide between the western half of the country and the eastern
#
half of the country, which the 2011 census showed, still exists in 2015-16.
#
So that geographical inequality has not changed.
#
And similarly, education in 2015-16, if I remember correctly, played a much larger role
#
in shaping which wealth quintile you are in, which wealth group you are in, which class
#
you are in compared to earlier.
#
So that again showed that this formalization and everything that is happening is surely
#
But if you are not able to educate your kids or whatever, then you are at a greater risk
#
And of course, for the pandemic, it means that then those dropouts which we are seeing
#
and who will continue to drop out if the middle action is not taken are going to suffer income
#
losses throughout their lifetime.
#
So these are these are very real consequences.
#
And so this is one way of dealing with this issue.
#
But the problem is this is a one step thing.
#
If more and more people start doing this, then later on in the next round, the government
#
may say that, okay, let's shorten this wealth questionnaire.
#
Why do we need so long list of household amenities, so let's wait for the census and
#
once in 10 years, we'll know why in between you want to tell people what is happening
#
to assets and how many toilets are getting built and all that, you know, let's not have
#
Let's wait for the census.
#
So it's convenient to then sort of discontinue that if there is no strong public pressure
#
and if this does not come out in the public openly and not enough people shout against
#
So that danger is there and it is real.
#
I'm not denying for a moment.
#
And the second part is that this use of proxies or cross-tangulation works only to some extent.
#
Sometimes you need exactly that survey that so you can calculate multi-dimensional poverty
#
from NFHS, but consumption poverty, you need a consumption survey.
#
You cannot calculate from NFHS and it will be wrong to do so.
#
And it won't be comparable with your poverty estimates of the past because they were done
#
using a different survey.
#
So then you lose that connection with the past.
#
You are not able to measure your progress as a society, which is what you want as a voter
#
That is where the role of fresh institutions, new institutions come in.
#
So I don't think that you can do away with institutions completely.
#
All our institutions are problematic, whether it be the media, the army, the courts.
#
And over the past decade or so, there has been a backlash against all of the institutions,
#
But I think we can't take this too far.
#
Institutions will always remain imperfect.
#
We have to make the best out of it.
#
We can't give up and say that, look, this is not working and let's throw everything
#
The point is how do you improve and how do you make it work for more and more people
#
The same with statistics.
#
You need a way to ensure that there is independent regulation, right?
#
Because same with the election commission, right?
#
You can say that a government will become so powerful that it will appoint whoever it
#
wants as the chief election commissioner and through that mechanism manipulate.
#
And probably to some extent it happens.
#
But because it is ordained in the constitution that it has to function under certain norms
#
and those norms can't be changed unless the parliament recommends.
#
And there is so much glare and media attention on how and why certain decisions are taken.
#
And if at least one election commissioner is honest and wants to reveal what has happened,
#
he or she can easily do so in today's day and world.
#
There is certain amount of check and balance to ensure free and fair elections, right?
#
We may need to put in additional measures in the coming years.
#
And I do think some part of election funding, the way elections are conducted need to be
#
revisited and we need much more stronger teeth than we have.
#
But it doesn't mean that we throw away the election commission or we assume that let's
#
give the power to the state and anyway it is the state, everything is the state.
#
So why have a different commission?
#
Let's have an election ministry which will conduct election, right?
#
You have a statistics ministry, but that is not the regulator, that is the producer.
#
So you need a separate arm, which is the regulator and auditor of statistics.
#
And as you said, given the public demand for credible data, which has been growing and
#
I myself have seen this, a lot of readers, a lot of people who use data come up and ask,
#
why is this happening this way?
#
A lot of my questions, my stories happen because of that, otherwise I would not have been forced
#
to pursue many stories which I otherwise did.
#
And to answer those questions and to come up with honest answers, you definitely need
#
You need a proper independent statutory national statistical commission, not the kind of lame
#
duck body that we have been reduced to.
#
And the plan for that was already there in the Ranganathan Commission, as I said, that
#
you need an institutional approach and there have been many attempts to do it.
#
The bureaucracy and the political system as a whole has never been interested.
#
So right from UPA to current NDA government, no one has pursued it seriously.
#
But I do hope this growing interest in data, this growing interest in understanding statistical
#
systems will make more and more people question how this data is coming, what are the checks
#
and balances to ensure that it is independent and will eventually prod our representatives
#
to consider ways to ring fence and safeguard it.
#
So I am partly optimistic on that.
#
And given that most of this data is produced by the state, has there been an impact on
#
the quality and quantity of the data coming through in the last few years?
#
And if so, is it part of a continuing trend that was there anyway?
#
Or would you say it's something specific to perhaps this dispensation that as the economy
#
is gone and the economy honestly has been going down from UPA too, it's not something
#
to do just with this government, but as far as data purely is concerned, is there a trend
#
Is there something we need to worry about, about both the release of data and the quality
#
of data being sort of dependent on whatever the politics of the time may be?
#
Yes, so one part is to a large part, it is structural.
#
It is not because of this dispensation.
#
So I'm very clear on that.
#
Did this government take remedial steps to make it better or did it make it worse?
#
I think it made it worse.
#
There is no doubt about that as well.
#
In what concrete ways, if you can expand?
#
So for instance, when this entire GDP controversy erupted, initially they allowed people like
#
Arvind Subramaniam, Raghuram Rajan, who were then respectively chief economic advisor and
#
the IBA governor to express their criticism of the new numbers.
#
If you remember, they express very publicly, even the economic survey had a small box item
#
questioning or raising questions about the GDP puzzle and so on.
#
But gradually these people left and others came in and overall government spokesperson
#
started behaving as if this GDP numbers represented the gospel truth.
#
And instead of appointing an independent review, which was required and which has happened
#
with other data sets at other points in time, I remember when this IP controversy first
#
erupted around mid-2001, Mohan Singh had actually asked for a review of that.
#
And that report was not made public, but the National Statistical Commission examined that
#
and had recommended certain changes, some of which were done.
#
The person who authored that report, R. B. Berman, he was an ex-RBI statistician, he
#
later became the NSC chairman also.
#
So there were precedents in the Indian data system itself, where you appoint people from
#
outside who are not involved in producing the statistics to examine it.
#
And there were mechanisms to do it, you could have used the NSC to appoint such experts
#
and review it, they did not do so.
#
Maybe you could have accepted that, okay, fine, let us continue with this, it may not
#
be that problematic, but let us also look at other metrics and to measure the health
#
of the economy and maybe go for a revision quickly before the five-year cycle and then
#
ensure that the data gaps that were not filled are filled in time so that the next revision
#
incorporates all those methodological changes and we build a more robust system.
#
That effort also did not happen.
#
Much later in 2019, when I broke that story about an NSS survey finding that part of the
#
database of companies was wrong and companies were missing from the list when they went
#
Around that time, they said that we will set up this committee to examine the role of deflators
#
because that also was a bone of contention and then I do not recall anything happening
#
So when certain controversies did do come up in the knee-jerk reaction, then they also
#
said at that point that there would be this new NSS bill, which they again did not publish
#
until much later and then that too in a very diluted form from what it was originally intended
#
So there were these knee-jerk reactions to various things, but there was no systematic
#
effort to improve or reform the statistical system or give it some autonomy, if not independence
#
and make it answerable to an independent people.
#
Later the National Statistical Commission's powers were gradually and informally eroded
#
and survey data was unnecessarily politicized during the 2019 elections.
#
They questioned the employment and jobs data.
#
They said it is not fit for use and just after the elections, they released it.
#
Not just they released it, now almost every other publication, whether it be NITI or PME,
#
AC or anyone, they continue using the PLFS data.
#
They compare it with pass-around without any question, showing that this entire drama and
#
tamasha that they created during 2019 was political in nature.
#
And that harms the credibility of the statistical, that undermines the moral of statistical workers.
#
And you unnecessarily sort of drag honest statisticians through a process in which they are made to
#
feel that their efforts at preserving the integrity of the system has come to no avail.
#
And beyond that, when this National Statistical Commission was reconvened and so on, many
#
of its recommendations were either ignored or informally a lot of things happened where
#
the ministry took the lead decision such as not releasing the consumption survey results
#
The survey was in 2017-18, the decision was taken in 2019.
#
The NSC was not even consulted and later when the NSC, which is legally the body that is
#
supposed to take these decisions, recommended that at least the unit level data be released
#
and that recommendation was rejected or it was not acted upon.
#
So there were many of these sort of separate, then there was this back series controversy.
#
So that entire controversy was entirely political.
#
A lot of the GDP changes and division, a lot of people say it is fudged, I want to make
#
it clear, it's not fudged.
#
There are gaps in the data systems.
#
We wanted to move to a newer way of evaluating GDP, which was certainly more cutting edge
#
in the sense that the world had moved to that standard or was moving to that standard, the
#
UN had recommended, but we were not prepared for it.
#
The homework in terms of producing those data sets, which can then feed into that model
#
So we were not prepared for it and we moved into it with not great results as expected.
#
You have to do that homework before you move to that and we were not ready and this corporate
#
database was untested, it was new.
#
Even the statisticians were not sure whether it was a stable database, but they anyway
#
So because of those reasons, that GDP first controversy arose.
#
The second controversy of back series was entirely political in nature, where two different
#
back series was produced and the one that showed the NDA1 and NDA2 in better light was
#
And there were a lot of problems with that also and they were not ready to discuss that.
#
The chief statistician refused to answer media queries on that.
#
So there was a whole host of things that happened and even the sanitation survey that was sought
#
Thankfully, they released the data with the caveats saying that that part of...
#
But what was most unfortunate was the chief statistician wrote an op-ed in collaboration
#
with the sanitation secretary, questioning his own ministry's survey data and that was
#
Till that point, at least officially, publicly, no one from the statistical establishment,
#
the leadership had questioned their own numbers.
#
And from what I am told, he was very keen to gain an extension and perhaps it is those...
#
So public choice theory says that every person acts because of his or her own incentives
#
and perhaps that applied more than...
#
I don't think anyone told him to write that op-ed.
#
The good part of the end to that story is that that person did not get an extension
#
despite his minister recommending it.
#
So I am told PMO shot it down.
#
So one negative side of the story is that this is evidence that there is over-centralization.
#
PMO is getting involved in a lot of things.
#
But the other side of the story is that despite all that and despite all these attempts to
#
Despite trying to be more loyal than the king, that person in particular did not get an extension.
#
So maybe there are saner versus still in the government who do feel that beyond a point,
#
all this mudslinging on their own data will come back to haunt them.
#
Because if you are questioning the employment data today, if you are questioning the sanitation
#
data today, tomorrow people will question your Swajh Bharat numbers, people will question
#
other numbers that you throw out and everything will go for a toss.
#
So then even your genuine achievements will not be recognized as being true.
#
And I think to some extent this realization may have seeped in.
#
That's my provisional and optimistic reading.
#
I may be wrong on this.
#
As we come close to winding up and you've been so patient with me, a couple of final
#
questions and the first one begins with the statement of a dilemma, which is that human
#
beings are not designed to deal with scale.
#
Like our brains evolved in prehistoric times, there is something called the Dunbar number
#
and the Dunbar number basically is around 147 or 150 somewhere in that range.
#
What it basically demonstrates and study after study has shown it is true, is that when it
#
comes to faces and names that we can remember, that we feel comfortable with, that's a number
#
Beyond that, the brain cannot process it.
#
And part of the reason is that our brains evolved in prehistoric times where we lived
#
in tribes that were maximum that many people.
#
And we don't understand scale.
#
If you tell me something is 10 million, this and some other thing is 1 billion, that at
#
an intellectual level, I know the difference between 10 million and 1 billion, but my eyes
#
I can't kind of comprehend it.
#
And by me, I don't mean me specifically because I'm superhuman, I can comprehend anything,
#
but I mean the general person, that our brains are not equipped to deal with scale, our brains
#
are not equipped to deal with large numbers.
#
The other aspect of how humans are built is that we have limited bandwidth, limited energy,
#
and for rational reasons, we have to focus on what has an immediate impact on our lives,
#
which is the things that we do in our everyday lives, right?
#
Like my concern right now after this is making sure the files got recorded okay, and thinking
#
about the edit and thinking about the release and I don't have time to look at government
#
data and say ki governance kaisa hai, where is the poverty line and all of this.
#
And this is what public choice is called rational ignorance, that voters are not incentivized
#
to actually find out enough about governance because of all these reasons, they have bigger
#
priorities and that they realize one vote will not make a difference, which is most
#
Maybe there was an outlier case in some local election in UP where a guy lost by one vote
#
and his wife and the wife's sister hadn't come to vote, so he got upset with them.
#
But regardless of that, and that might even be apocryphal, but the point being that, so
#
we have these dilemmas where one, our brain can't handle numbers, two, it is actually
#
not in our interest to look too deeply into the functioning of these things because we'll
#
spend too much time trying to process it.
#
So we develop shortcuts and heuristics.
#
Now one common shortcut and heuristic is a tribal one, that you choose your tribe, and
#
by tribe I mean like it could be a political party, which is a Congress or whatever, or
#
it could be your caste or whatever grouping you choose or whatever vote bank you choose
#
And you say, okay, I'll just go with them.
#
That's where my loyalty is.
#
Two, again, our brains are wired in a sense to prefer strong leaders because if you're
#
living in tribes and tribes are always fighting with each other, you want your leader to be
#
the biggest, manliest, most macho guy out there.
#
So you're always going to pick a Modi over someone else, I won't take names.
#
So that machoness kind of helps and we gravitate towards strong man leaders and ignoring other
#
All of these are a dilemma because as we agreed, data is crucial to the functioning of a democracy
#
because data is what tells citizens are their governments doing a good job apart from the
#
anecdotal evidence of their eyes and therefore it's important.
#
Now as a data person, you obviously know the importance of this, you were speaking earlier
#
about how you try so hard to make data accessible so everybody gets it.
#
How big a challenge do you think that is?
#
What are the learnings you have learned while trying to figure out ways to make data accessible,
#
to make it interesting for everybody?
#
Like if other data journalists or people who are suddenly curious and say that, hey, this
#
seems worth my time, I want to do this.
#
What advice would you give?
#
What are the different ways in which we can get past this huge gap and make people care
#
about data and understand data without necessarily having to have them spend too much time on
#
So you mean advice as citizens?
#
No, both as citizens, what kind of data do I look at?
#
But typically there'll be shortcuts and heuristics.
#
One is you could pick a tribe, two is you can pick somebody you trust.
#
Like when it came to COVID, there was such a fog of war, who do you trust?
#
So I picked scientists like Eric Topol or journalists like Zainab Tufekci or epidemiologist,
#
however you pronounce that, like Gautam Menon and Gagandeep Kang and Brahman Mukherjee.
#
And so after a certain point of time, I realized I can trust these people and Gautam was on
#
And you can use heuristics like that.
#
But in general, I don't think a common person because of rational ignorance is going to
#
go to the source of the data and read World Bank reports and look at MIE data and all
#
But at some level, that gap of comprehension of building those narratives that cannot tell
#
the truth have to be filled by people like you, which you've been.
#
So what's your approach?
#
How difficult a problem do you think it is?
#
Because sometimes it boggles me.
#
I think it's almost insurmountable, especially in these modern times where people want to
#
believe what narrative they want to believe, facts be damned.
#
So how do you look at that?
#
So it is difficult, but it is also rewarding.
#
And there are two aspects to this.
#
One is that, yes, people do want to live in their own eco chambers.
#
These are, as you said, modern tribes and you want to do virtue signaling within your
#
own network and you don't want to engage with the outside.
#
Luckily, because of my own personal history and various other things, I have always had
#
interactions with different groups and different ideologies and so on.
#
So I've always cared for what people who may not agree with me on most things think
#
about my writing and my work and my data.
#
And one way I have ensured this is that I've always cared for whether they believe in my
#
charts, even if they don't believe in the text I wrote.
#
Plus the other thing we always made sure was we published all charts online in data wrapper.
#
So anyone can in a data wrapper chart, download the data in a CSV format and anyone who knows
#
Excel can recalculate and figure out and at least for a financial audience, which Mint
#
serves, people have that basic Excel skill.
#
So if I'm distorting the access or doing some very elementary statistical manipulation to
#
distort the data or tell half the story, everyone can catch me.
#
So by giving out that data in that form, it is a signal to an intelligent reader that
#
I'm confident of my numbers and my analysis.
#
And then if you still disagree, you can go ahead and use it the way you want to.
#
Or if you want to spin a different tale, you can use that as well.
#
And people have done it.
#
They've used parts of our story to highlight narratives that they want to project that
#
But broadly for a generator who is not into those misinformation campaigns, that is what
#
And so if I were to think back as a reader, I think those things do make a difference.
#
And you're right to expect every reader to go through every source for every report and
#
all this is not possible.
#
But the very basic thing that a reader can do is to see whether there is any source at
#
So sometimes friends and others send me maps and charts which go viral on social media.
#
And the first thing I check is whether there is a source.
#
If it is not there, I tell them that I can't proceed and you should also not trust it.
#
So there's a map of which book is translated most in which part of the world and so on.
#
And incidentally, Tyler Cowen had linked to it in Marginal Revolution and someone sent
#
it to me, an economics professor.
#
So I told him that I can't trust it and not should you.
#
Maybe someone just made it as a joke.
#
That person was very upset.
#
Alice in Wonderland was the most translated work in Britain or it was the most popular
#
Something of that sort.
#
So that is just a trivial example, but there are serious examples where people spread misinformation
#
And this is one easy way.
#
The other is, has anyone else written off about it?
#
If it is a prominent data source, if it is a prominent this thing, then someone else
#
would definitely in today's world, it cannot go completely unnoticed or uncommented.
#
So that, at least that much effort, I think we should expect engaged sort of citizenry
#
to do and that they should do.
#
And the third part is that we should follow the stories about what is happening to these
#
Some of them, the schedules are known.
#
When is the data set supposed to be released?
#
Why is it not being released?
#
So journalists should follow those up.
#
And as readers, we should engage with those stories and we should do our best to sort
#
of share them and engage with them.
#
And I do find more and more readers are interested in.
#
So the way I think of it is that compared to say 15 years ago, there is a much more
#
larger audience today for these kinds of stories.
#
And if you think of it from a long-term historical point of view, that is quite natural.
#
Our educational attainments have gone up, various other metrics have gone up.
#
The use of math in the workplace has gone up and it is set to grow up more.
#
Data itself is becoming, data science itself is becoming more and more important in a wide
#
So you'll have many, many, many, many more professionals who will be using data in their
#
Now, not all of them will be statistical experts.
#
Not all of them will be experts in economic statistics, but they will have a broad sense.
#
And if they want to contribute more, you want to dig out data and do tell stories, please
#
But I would encourage people to first pick a subfield, maybe it is cricket, maybe it
#
is movies, whatever interests you, maybe it is the economy.
#
And then look at all the public data sources that is there.
#
Today, if you know a little bit of coding, understand basic statistics, you can come
#
up with intelligent charts and narratives and you can start doing that work on your
#
own and you can at least cross-check certain numbers.
#
There are a lot of community efforts.
#
There's an open data community in India.
#
There's this group called Data Meet, which I'm also a part of, Rukmini is part of.
#
Most data analysts I think are part of, where we share resources, we help each other out
#
And I have also benefited from them.
#
Apart from that, there is a broader sort of open data community which pushes various organizations
#
to release their data in a CSV open access format or a machine readable format so that
#
they can be used by the wider public.
#
And you can never predict which data set will finally be used widely, which will throw new
#
light on the functioning of the state or the functioning of the economy or tell us something
#
new and who will do it.
#
But eventually it has a way of, it's like the sort of, in some way, the invisible hand
#
of the system that leads people to these things and whoever is interested is able to come
#
up with things that others have not been yet able to say or throw new light on this thing.
#
And that's how we advance our understanding of ourselves and our world.
#
Going to your advice to data journalists part of the question, would you say that it's important
#
if you want to be a data journalist today to not just learn data and statistics, but
#
also to learn storytelling?
#
Yes, definitely, very much so.
#
And what are the important sort of hacks or broad principles that you might have learned
#
over time of turning data into compelling stories?
#
One is, and which again came from my former boss Niranjan, is to learn what to give up,
#
So after you've done all your calculations, you have done all your work, you have to leave
#
out maybe 90 to 95 percent of what you've done.
#
So that elimination is often tricky and often we get attached to some, you know, special
#
effort we have made in a certain direction, but that may not be the most relevant.
#
So you have to keep the reader in mind that sympathy for the reader is most important
#
and that guides every other decision that you make.
#
So that is at the core of things and that leads to that elimination.
#
B, as I said, if you have some specialization, maybe it is the economy, maybe it is politics,
#
maybe it is sports, maybe it is something else.
#
That also helps because then you do by iteration, you keep on doing stories around particular
#
database, you get a better hang of it and you also start engaging with the producers
#
So I think that is very important.
#
In fact, sometimes when I'm asked to lecture at J schools, journalism schools, I tell them
#
that there's a lot of attention on the GDP, but you guys should pay more attention on
#
the DGP, the data generating process.
#
And that helps, whether it is corporate data, whether it is coming from SEBI, whether it
#
is financial data coming from RBI, whether it is GDP data coming from CSO, look behind
#
who is collecting the data, what is their incentives, who is providing the data, what
#
is their incentive, what are the level of checks, is it by an external auditor, are
#
market analysts tracking it?
#
Why is the data of a startup less reliable than that of a listed company?
#
Because there are more people who are tracking the checks and balances in a market, in a
#
stock market and much more.
#
So these are just some basic sort of rules of thumb that you apply.
#
And finally, I think when you come to charts, there is this misconception that it should
#
dazzle the readers and therefore it can.
#
Sometimes a simple line chart with one or two annotations can be much more effective
#
than any grand visualization.
#
And we have often seen that that is more effective in storytelling.
#
So think about that as well carefully.
#
And always remember that as one of my favorite writers, Tim Harford says in his latest book,
#
how to make the world add up, misinformation can also be very beautiful.
#
So just because there is a very stunning piece of graphic, it doesn't mean it is true.
#
Not just because it has gone viral on social media without a source, but also because the
#
producer of that listing may not have done enough work to say normalize the data.
#
Suppose you put India, China and various other countries.
#
In many metrics, India or China will come up as number three, number four in the world.
#
That does not mean anything.
#
Yet almost on a daily basis, you will see headlines not just in India, but across globally
#
that India is number two in this, number four in this, China is number three, number one
#
It's just the population effect.
#
So at least normalize the population.
#
India is number one in terms of podcast length, and it's all because of me.
#
So three final questions, see, I'm so precise, three final questions.
#
And the third last one has a thought experiment.
#
Let's say that one day you're sitting at a cafe and suddenly the cafe empties out and
#
Mukesh Ambani walks in and he walks up to you and you realize the cafe has been emptied
#
out so he can have a private conversation with you.
#
And he sits down and he says, young man, I heard your podcast with Amit.
#
Contrary to impressions in some quarters, I am deeply concerned about this nation.
#
I want to help our democracy and especially I want to help it in the context of data,
#
of helping people understand data, of building institutions that can produce worthwhile data.
#
Just this whole cause, everything that we've been speaking about, I will give you a thousand
#
crores or I will, I have a thousand crores to spend.
#
Send one crore each to all the MPs, along with my draft of a national statistical commission
#
bill, ask them to vote on it as yes.
#
Okay, I don't understand that, break it up for me.
#
So there are already attempts to reform the national statistical commission.
#
See, I don't think I have the power to change the statistical system.
#
At best I can point people in certain directions.
#
But you don't, but Ambani ji hit us, Mukesh Bhai.
#
So that's why I'm saying with this wealth, the best thing you can do is to bribe the
#
MPs to vote on a bill which is required for the country.
#
So tell me more about this bill.
#
What does it propose to do?
#
Like I'm trying to imagine a future landscape in which this problem is solved or at least
#
in which this problem is not such a big problem.
#
Yes, so it won't be solved fully, but to a large extent, there will be an independent
#
regulatory body with an initial corpus of 100 crores of its own to conduct statistical
#
So on the one hand, it will innovate and produce new kinds of statistics.
#
On the other, it will be a new sort of regulator of the statistical system.
#
So think of it as the CAG of the statistical system.
#
So it will audit all the government departments, it will examine whether the processes that
#
exist today to collect data, whether it be for Anganwadi, whether it be for COVID deaths,
#
whether it be for IIP or whether it be for GDP.
#
So it will look at all this, there will be different departments or different members
#
looking into each of this and they will evaluate whether certain core principles of statistics
#
are being followed, whether certain transparency principles are being followed and they will
#
publish all their findings and this thing in a transparent, they will be accessible,
#
they will be answerable to the parliament of course, but to the wider public at large.
#
See one of the basic recommendations is that even the current national statistical commission
#
should publish the minutes of meetings in a separate website.
#
When the domain name was registered, nothing happened.
#
So even basic things like this can make a difference.
#
I have always believed that it is this interaction with public and state authorities that will
#
One person unknown or one institution just left on its own is not going to produce change.
#
There has to be a system of checks and balances and there has to be a system of accountability
#
I strongly believe in that.
#
I love that you gave me such a concrete answer and my further message to Mukesh Bhai if he
#
is listening to this is one kindly listen to Pramit and two, you can send one crore
#
to each of the MPs we have, but we don't have a thousand MPs, you'll have some money
#
What do you do with it?
#
Remember where you heard this support independent podcasting.
#
Now my second last question is getting back to the personal, which is that a lot of your
#
work is sort of indiscreet articles which are just kind of coming in Mint and Mint also
#
So the person who doesn't subscribe to Mint may not get to read it.
#
Are you planning to write a book anytime?
#
Are you sort of, you know, it's that, I mean, taking inspiration from Rukmini, who's just
#
come out with a book I'm dying to read and that you had also some input into, I believe.
#
Is that part of your plans?
#
Because I'm sure everybody would like to see you do that.
#
You know, you very patiently answered all my questions during this talk, but you obviously
#
have way more insights and there are way more questions to answers and so on and so forth.
#
So like I really believe that good data journalists also have a duty as public intellectuals.
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And I think our democracy needs that as well.
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And in a sense that helps inspire data journalists of the future as well.
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So is there anything like that in the work?
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So will you at least start a newsletter like I've been telling you, like we were talking
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So one is I'm not very comfortable with the idea of me as a public intellectual.
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But that's not something you choose.
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That's something that kind of comes with the territory, right?
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I just feel that that makes you a bit distant from the people.
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But anyway, that's my own personal sort of this thing.
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Maybe I don't like the phrasing of that or whatever, or I don't think of myself as that.
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I think of myself more as a storyteller.
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So books, definitely, I mean, it's part of the storytelling tradition.
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And I know a lot of people feel that that can convey.
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At this moment, I don't have a feel a compelling need to write a book newsletter, yes, definitely
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that thought has crossed my mind and evaluating it.
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And if I feel that, yes, I can do something which will add value to people who might be
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interested in what I have to say, maybe I will start a newsletter.
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And that's interesting, like a digressive thought.
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I was on a WhatsApp conversation with some friends yesterday, and one of them sent a
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couple of pages from these bookies reading, which was written in the 60s, I forget the
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name of the book, but it's about the degradation of the word intellectual, how it almost became
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a pejorative in certain circles.
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And the point that the author there was making was that, listen, you know, intelligent is
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not a bad word, but intellectual seems to be a bad word.
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And I think part of the reason for that is that it's been given a political taint just
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in the same way as, you know, the words liberal and progressive can also be used to, you know,
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Yeah, but I get what you're saying.
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My discomfort is not at all from that.
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So you're a Bengali, you will understand half Bengali.
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So there is this thing called Atel, Atel Vaj or Atel.
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So my sense of discomfort, I think, springs from that, that you very soon become an Atel
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and you talk about things.
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So someone once told me, and it was an unkind remark, but I think there's a kernel of truth
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in it, that an intellectual talks about things he doesn't fully understand or doesn't have
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My counter to that would be that two of the gentlemen we mentioned earlier in the show,
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Milton Friedman and Samuelson, were public intellectuals and, you know, both of them
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influence a field to differing degrees and both of them influence a wider world out there
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Friedman certainly huge influence on me.
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Those are my models when you think of public intellectuals, but I'll move on to my last
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I mean, this is a semantic issue anyway.
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And my last question is this, and it's a common one for many guests who come here, that my
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listeners love to read as well, contrary to popular perception.
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People still read in this day and age, people still crave depth.
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So what I'd like you to do is recommend books which have meant a lot to you, not necessarily
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to do with your field, but just books which have meant a lot to you, which might have
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changed the way you look at the world in some big or small way, or even books which you
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might have read recently and are so excited about, you want to stand on a soapbox and
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shout, hey, everybody read this.
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So I really liked, I mean, this is very recent.
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So it is in my memory, Tim Hartford's latest book, How to Make the World End Up, especially
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one of the chapters is on biases and our interpretation of statistics, which is, I think, a large
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part of what we discussed today.
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And I think he has some good answers on how to counter those biases.
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And for any citizen who is interested in evaluating the world around him or her, his or her country,
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There is a section on big data, which also is very worth reading because this has become
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And Hartford's take is a bit critical.
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And I think some of the arguments he makes are worth considering.
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So that is one that immediately comes to mind.
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The other one is Understanding Statistics by Anthony Davis is a very small, slim volume.
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And if I'm not mistaken, the PDF is available for free from the Cato Institute, which published
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It is written with a slightly libertarian sort of perspective, but that does not detract
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from the book in any way if you do not believe in that ideology.
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The other part is it uses American examples to illustrate statistical concepts.
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But even that, I think most readers will be able to sort of grasp.
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And it's written in a very sort of clear, plain English and very basic sort of stuff.
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But even some of the very basic stuff, sometimes we don't recognize, even those with statistical
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training sometimes ignore.
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So I think it's valuable both for a lay beginner as well as someone who wants to refresh or
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once wants to see how statistics is applied.
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So for both sets of readers, listeners, et cetera, it's a very valuable book.
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This I read this year itself, although this is a very old book.
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So the book is called The Antilles of the Savannah.
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It's by Chinua Achibe, one of the most famous writers to have emerged from Africa.
#
And he got his fame from earlier works.
#
And this is quite a late book.
#
It was in the late 1980s.
#
And it tells the story of three friends who had studied together.
#
One becomes an advisor to the ruler or dictator or authoritarian ruler, whatever you want
#
And one is a journalist and the ruler, the key advisor and the journalist, they're all
#
sort of batch mates or had studied together.
#
And the differences between them, the ideological sort of disagreements, how they navigate,
#
how it influences their personal and political sort of lives and how things work.
#
And you feel that there's a lot of universality in the book in that it captures a lot of,
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you know, some of our current questions also quite brilliantly.
#
So I really like that and Elena Ferrante, I already talked about, I finished the four
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part Neapolitan series.
#
And see, it's obviously about female friendship.
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It's about, you know, how two women sort of throughout their life maintain that bond.
#
There is envy, jealousy, at the same time, there is camaraderie, there is sharing, there
#
That is a central sort of theme running through the entire series.
#
But it is also a tale about inequality and how difficult it is for someone who is not
#
from a certain social class to make it.
#
And even after making it feel a kind of an imposter syndrome.
#
And there are some parts where one felt that, you know, whatever journalism I do, whatever
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ways I can write about inequality, and I have written a lot, one just fails to, you know,
#
capture it in the way that a novelist or a playwright would do.
#
So that is the third is this I read during the pandemic.
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The Gentleman in Moscow by Amor Toulouse, I think that this will be familiar to many
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It's also fairly recent.
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Madhavan also recommended this as one of his books.
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Yeah, it had a very calming influence also, because I started reading it, I think, during
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the second wave when there was another lockdown.
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And this is also about a man in lockdown, in some sense, is locked up in a Russian hotel.
#
And this entire, you know, this revolution and its, you know, hype and all the way it
#
disrupted lives of people, even those who believed in the revolution, captures beautifully.
#
And it also, it also has a lot of, I don't know, there are a lot of parts to it, the
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pace at which the writer tells the story, the philosophical aspects to it, and the way
#
it evolves, and using just that one hotel as the center of the plot, basically, everything
#
Most of the story unfolds in one hotel in Moscow.
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And this is written by someone who is an American.
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So that I actually gifted to a friend of mine.
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And now, in a sense, you've gifted it to all of us.
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So thank you for these great recommendations.
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And I kind of agree that great art captures the human condition in a way that journalism
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sometimes can't do, but journalism also fulfills functions that are necessary and unique.
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So more power to you and all the work you do.
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And thank you so much for spending so many hours chatting with me today.
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That has been wonderful.
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And it's been wonderful talking to you and I too learned a lot.
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If you enjoyed listening to this episode, dive into the show notes, enter rabbit holes
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You can follow Pramit on Twitter at Pramit underscore B.
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You can follow me at Amit Verma, A-M-I-T-B-A-R-M-A.
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You can browse past episodes of The Scene and the Unseen at sceneunseen.in.
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Thank you for listening.
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