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Speaker 1: the Bloomberg with you. It's kind of fun to test out, right,

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Speaker 1: Jeff Right. So if you're reading a story about Tesla

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Speaker 1: Chrome Store to try it out. Yeah, and it's called lens.

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Speaker 1: We should probably say what it's called, right, all right, right,

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Speaker 1: it's called lens. Learn more at Bloomberg dot com slash lens.

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Speaker 1: Hello and welcome to another edition of the Ad Thoughts Podcast.

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Speaker 1: I'm Tracy Alloway and I'm Joe wisntal So, Joe, I'm

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Speaker 1: trying to think have we ever had a Nobel Prize

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Speaker 1: winner on the show? Can you remember? I don't think so.

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Speaker 1: I don't think we have. Yeah, I don't think we

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Speaker 1: have either um and not to get your hopes up,

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Speaker 1: we don't have one UM for this episode either. Shoot. Yeah,

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Speaker 1: very disappointing. We do have someone who has a really

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Speaker 1: good shot at eventually, UM getting one. We actually have

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Speaker 1: the recent winner of the latest John Bates Clark Metal.

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Speaker 1: It's a medal that's given by the American Economics Association

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Speaker 1: to economists under forty who make big contributions to the

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Speaker 1: field of economics. Isn't it also true that among economists

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Speaker 1: the John Bates Clark Medal is actually considered to be

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Speaker 1: more impressive and prestigious than the Nobel Prize. You're probably right,

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Speaker 1: I mean, statistically, I think the recipients go on to

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Speaker 1: win a Nobel and of course the John Bates one

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Speaker 1: is confined to young economists, so that's pretty impressive. Another thing, uh,

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Speaker 1: that sort of will make today's episode distinct is it's

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Speaker 1: kind of timely. So I kind of like the fact

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Speaker 1: that on our podcast we usually talk about things that

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Speaker 1: aren't really in the news. We sort of give people

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Speaker 1: a rest bite from the top stories of the day

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Speaker 1: and just let them explore something completely out there. But

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Speaker 1: this one might actually be a little bit on the news. Yeah,

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Speaker 1: So we are definitely huge fans of financial and markets

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Speaker 1: and economic history. And the guy that won this medal,

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Speaker 1: I should just go ahead and say his name. It's

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Speaker 1: David Donaldson. He's an associate professor of economics at Stanford.

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Speaker 1: But Professor Donaldson is basically famous for being a sort

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Speaker 1: of trade economic historian. And he's famous for one paper

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Speaker 1: in particular. It's called Railroads of the Raj and it

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Speaker 1: basically went back and looked at the railway network that

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Speaker 1: was built up by the British in the late eighteen

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Speaker 1: hundreds early nineteen hundreds in India and he used that

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Speaker 1: to check what impact building that infrastructure has on trade

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Speaker 1: and general incomes in India. So I mean imagine putting

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Speaker 1: that paper together, right, It definitely sounds like it would

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Speaker 1: be quite a task. All right, well should we just

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Speaker 1: go ahead and ask him. Let's let's bring him in. David,

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Speaker 1: thank you so much for joining us today. Hey guys,

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Speaker 1: thanks for having me. You know, we just mentioned probably

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Speaker 1: your most famous paper on railroads of the raj and

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Speaker 1: that was a paper you started, I think when you

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Speaker 1: were a grad student. Um, but you worked on it

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Speaker 1: for years and years and years, and if I'm right,

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Speaker 1: it's it's still actually hasn't been published. It's still forthcoming, right. Yeah,

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Speaker 1: I've been I've been very slow in many ways. So um,

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Speaker 1: but yeah, it's something that you know, has occupied my

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Speaker 1: interest for a long time. It struck me as a

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Speaker 1: you know, among other episodes in world history, one of

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Speaker 1: one of the great episodes that integrated economy has made

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Speaker 1: it easier for one market to trade with another market,

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Speaker 1: and that trade, that integration happens obviously internationally, things like

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Speaker 1: the Panama Canal or the sewer Is Canal, the invention

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Speaker 1: of the steamship did that across oceans, and then you know,

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Speaker 1: the railroads sort of did that within countries like India

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Speaker 1: and of course famously also the United States. So before

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Speaker 1: we even get to what you did to work on

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Speaker 1: this paper and or even the conclusions, What originally sparked

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Speaker 1: your interest in this particular event from history. Well, actually

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Speaker 1: I was. I was a grad student, and I knew

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Speaker 1: I was interested in trade, but and I knew I

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Speaker 1: was particularly interested in intra national trade, that is, kind

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Speaker 1: of trade across regions of the same country, which has

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Speaker 1: typically always been hard to study, just for for lack

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Speaker 1: of data. Basically, when when goods cross international borders, customs

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Speaker 1: agencies have always tax that you know, not always, but

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Speaker 1: the most part most countries throughout history of taxed international trade,

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Speaker 1: and so they've kept a record of the flow of

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Speaker 1: international trades, whereas typically when goods moved within countries they

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Speaker 1: don't get either tax or or recorded, and we don't

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Speaker 1: know much about it. But I was always interested in India,

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Speaker 1: and I got got wind to the fact that at

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Speaker 1: the time when I was doing my doctoral work, and

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Speaker 1: in fact almost still to this to this day, India

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Speaker 1: had had sort of domestic tariffs, tariffs on a movement

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Speaker 1: of goods across state boundaries within India, and I wanted

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Speaker 1: to know more about that. I wanted to kind of

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Speaker 1: scope it out. So I went to India for for

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Speaker 1: a couple of weeks one summer and in order to

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Speaker 1: meet with people in government, people in academia, and librarians

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Speaker 1: in in order to try to sort of find out

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Speaker 1: the facts. And actually it was it was in that

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Speaker 1: process that I met Hovant. You know, just heard this

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Speaker 1: kind of endless refrain. Well, you know, the thing that

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Speaker 1: really brought India together is was the railroads opposite of

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Speaker 1: about a hundred years ago? And that, I guess pique

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Speaker 1: my interest. I assume there would be no data, you

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Speaker 1: know that that obviously the further you go back in time,

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Speaker 1: that's typically the the less data that survives. And so

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Speaker 1: I thought, well, I guess I could at least sort

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Speaker 1: of explore what exists in the in the libraries on

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Speaker 1: that historical year. And I was just shocked to learn

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Speaker 1: what was actually available, you know, um, what they've recorded

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Speaker 1: back down back then, what they published, and what still survived.

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Speaker 1: So one thing about the British railway system is it's

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Speaker 1: famous for being absolutely massive in India, I mean hundreds

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Speaker 1: of thousands of miles worth of railway. How how did

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Speaker 1: you go about collecting that data and then isolating certain

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Speaker 1: effects away from other effects because I imagine, like, if

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Speaker 1: you look at the fact that one railway line has

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Speaker 1: been built between one county and another town or whatever,

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Speaker 1: that's going to have an impact on all sorts of things. Right,

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Speaker 1: It's almost like a tree diagram, Like the impact just

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Speaker 1: keeps spreading and spreading. So how do you I have

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Speaker 1: so many questions, how do you go about kind of

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Speaker 1: limiting that impact? There's no easy, easy way to do that,

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Speaker 1: and I don't claim to have um necessarily nailed it

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Speaker 1: by any means. You know, the the ideal way you

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Speaker 1: know that any scientist learns about the world around us

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Speaker 1: is by something like an experiment, right, either a formal

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Speaker 1: lab experiment or in less controlled settings where you can't

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Speaker 1: you know, a lab experiments just advantageous because you know

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Speaker 1: that you can control everything except the one treatment that

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Speaker 1: you're trying to sort of study the effect of. But

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Speaker 1: sometimes you can sort of make sure that the treatment

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Speaker 1: is randomly assigned. That way, you know that on average

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Speaker 1: across the sort of subjects, if you like in in

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Speaker 1: the experiment, those differences will cancel out on average. And

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Speaker 1: so social scientists like me are always you know, looking

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Speaker 1: for things that that hopefully. You know, we we think

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Speaker 1: there's a case to be made for your features of

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Speaker 1: the way that the sub program was allocated to the

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Speaker 1: world having some quasi sort of random element to them.

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Speaker 1: And of course we never know if that's actually the case,

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Speaker 1: except in rare, rare instances where social scientists him herself

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Speaker 1: or the policymaker actually explicitly decided to randomize. But that's

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Speaker 1: of course extremely rare. Much more likely is the case

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Speaker 1: like the Indian railroads, where they had a complicated decision

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Speaker 1: process about where and when to um to roll out

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Speaker 1: the network. And and I should say, all that is

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Speaker 1: um it doesn't even really address the heart of your question,

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Speaker 1: which is that there will be spillovers. You know. That

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Speaker 1: is to say, in a classic lab experiment, you have

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Speaker 1: a treatment group that randomly is chosen to get the

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Speaker 1: drug or something in the control group is randomly chosen

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Speaker 1: not to. But there's a serious problem if you think

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Speaker 1: that it's possible that your control group is affected. Sort

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Speaker 1: Of one way to think about it is they're the

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Speaker 1: very fact that they were the control group means they're affected.

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Speaker 1: The other way to think about it as closer to

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Speaker 1: what you said, which is that some of the treatment

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Speaker 1: spills over onto them. They're sort of partially effected. Even

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Speaker 1: they weren't directly affected, they were partially affected. Okay, so

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Speaker 1: that's the big backdrop against which we tend to think

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Speaker 1: about these things. UM and you know you need you

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Speaker 1: need help from the re I think it's fair to

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Speaker 1: say that we don't. There's no you know, I I did.

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Speaker 1: In principle, you could design an experiment that which is

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Speaker 1: kind of completely ideally nail everything. But in the in

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Speaker 1: the real world, we don't have enough statistical power to

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Speaker 1: to follow all those spillover effects in their kind of

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Speaker 1: manifold directions. As you said, this kind of four king

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Speaker 1: tree just makes it. It's sort of that basically impossible

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Speaker 1: with the kind of data, the kind of world we

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Speaker 1: live in. UM, so you need so economists you know

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Speaker 1: like me sort of turned to um hopefully fairly uncontroversial

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Speaker 1: notions in economic theory to to help structure those you know, what,

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Speaker 1: we where we expect to find those spillovers and sort

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Speaker 1: of where therefore to sort of shine the light and

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Speaker 1: and try to see them. H And another way were

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Speaker 1: to say it would be we can sort of structure

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Speaker 1: things so that we know the kinds of places that

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Speaker 1: are likely to have had a full treatment. Other places

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Speaker 1: maybe that we we think they whatever the treatment was,

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Speaker 1: we know that some other place had three quarters of that,

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Speaker 1: or half of that, or a quarter of that, or

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Speaker 1: maybe finally just a place that we're pretty confident would

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Speaker 1: have been almost an affected by the event. So we're

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Speaker 1: always trying to do that sort of thing. And I

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Speaker 1: guess that's a high level overview of how I thought

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Speaker 1: about the problem. I want to ask you to talk

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Speaker 1: a little bit more about the process, Like how many

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Speaker 1: times did you have to travel to India, how many

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Speaker 1: libraries did you have to visit? What was it like

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Speaker 1: digging up the archives of all this data that you

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Speaker 1: discovered about the uh you know what specific data you

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Speaker 1: ended up looking at, and then also you know what

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Speaker 1: did you discover about the ramifications of the railway build up? Yeah,

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Speaker 1: I mean the process was certainly unlike anything I imagine

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Speaker 1: I would end up doing doing my PhD. Um the

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Speaker 1: first thing that you just have to find out what

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Speaker 1: data exists, So that's UM kind of a scoping project.

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Speaker 1: I this never would have been possible. I lived in

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Speaker 1: London at the time I was doing my PhD. At

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Speaker 1: the London School of Economics. And none of this would

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Speaker 1: have been possible if it weren't for the fact that

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Speaker 1: the main libraries in London actually had as good a

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Speaker 1: collection of kind of official government British Indian publications as

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Speaker 1: did any library in India, almost better maybe because things

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Speaker 1: have been preserved better. Yeah, so I that, I mean,

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Speaker 1: that made it all sort of possible given where I

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Speaker 1: was based, and also made it possible because just getting

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Speaker 1: the sort of numbers out of the books was of

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Speaker 1: course just a huge, huge project because obviously I think,

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Speaker 1: I it's complete back at the envelope calculation. But I

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Speaker 1: once convinced myself there was somewhere between fifty and a

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Speaker 1: hundred man you know, but a person years worth of

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Speaker 1: the work involved in typing in the numbers. So I

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Speaker 1: was never going to do it myself. And so that

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Speaker 1: but that process, you know, very Fortunately, just around the

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Speaker 1: time I was doing this, in the mid two thousands,

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Speaker 1: late two thousands, was just when people started talking about

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Speaker 1: business process outsourcing, you know, the ability to hire, um,

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Speaker 1: somebody to do a fairly low skilled task like typing

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Speaker 1: in numbers on a computer terminal far away. If you know,

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Speaker 1: if only you can find them, you know, And of

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Speaker 1: course that wasn't hard by that period of time. And

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Speaker 1: pay them that was also relatively easy once I could

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Speaker 1: raise the money and then finally just get the get

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Speaker 1: the raw materials to them. And I could never send

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Speaker 1: these books obviously they're they're way too big and and

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Speaker 1: and they're obviously they're held in libraries. But but I

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Speaker 1: was able to organize a team of people to take

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Speaker 1: kind of digital photographs of every page. It kind of

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Speaker 1: like scanning, but way faster. You know, scanning is super slow. Um.

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Speaker 1: But luckily digital cameras just became good enough around that

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Speaker 1: time where archived based historians like me were able to um,

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Speaker 1: kind of skip the scanner, skip the photocopy, and just

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Speaker 1: go straight to a good digital camera. Anyway, So I

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Speaker 1: sent you a fifty thousand JPEGs or something. Took almost

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Speaker 1: half a year just to organize the jpeg send them

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Speaker 1: to a number of firms in in India actually, and uh,

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Speaker 1: and they sent back spreadsheets full of you know, the

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Speaker 1: numbers typed in. Um. You know, the the original publications

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Speaker 1: were kind of too low quality to trust optical digital

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Speaker 1: character recognition. And uh, at least at that period of

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Speaker 1: that technology. Yeah, so I guess you know that that's

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Speaker 1: the basic idea of how I and many people like

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Speaker 1: me kind of would convert the sort of archival paper

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Speaker 1: records into digital machine readable versions. And then right, so

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Speaker 1: I guess you know. There are a number of things

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Speaker 1: I looked at. I looked at trade actual volumes of

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Speaker 1: trade flows. I looked at prices, that is, um, whether

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Speaker 1: there was evidence that um, you know, it gets a

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Speaker 1: basic prediction that is, when two markets get connected by

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Speaker 1: between the markets, then the price of that thing should

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Speaker 1: look more similar. That's often just the basic notion of arbitrage.

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Speaker 1: If the price were different, then yet it didn't cost

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Speaker 1: much to move it. You can always just sort of

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Speaker 1: buy in the cheap place and then sell the good

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Speaker 1: at the high at the high price place. So we

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Speaker 1: kind of think that if if arbitrage is likely to

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Speaker 1: be like that likely to happen, and it certainly did

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Speaker 1: in this environment of British India, then we would expect

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Speaker 1: that the railroads should narrow the difference in prices of

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Speaker 1: exactly the same good over over to say two points

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Speaker 1: in space. And there's a the data was consistent with

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Speaker 1: that obviously as well that as railroads connected places, their

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Speaker 1: prices started to converge. And then finally I looked at

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Speaker 1: um the kind of consequences for income. As you put it,

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Speaker 1: there's such a stresses sort of aggregate income, think of

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Speaker 1: kind of like a county or a district as they

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Speaker 1: were called in British India. We're looking at the just

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Speaker 1: this is is not individual people. I wasn't able to

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Speaker 1: look at inequality or anything important like that, but on aggregate,

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Speaker 1: the sort of the closest notion we could get to,

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Speaker 1: sort of g d P of of a county kind

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Speaker 1: of went up and on average went up. And you're right,

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Speaker 1: because the spillovers and and the fact that everybody's experience

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Speaker 1: was different. These averages can be of course a little misleading,

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Speaker 1: but but they represent the average, and the average was

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Speaker 1: an increase of about eighteen percent in g d P. Huh.

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Speaker 1: We are going to pause for a short break, but

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Speaker 1: knowledge to work and grow your business with c i T.

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Speaker 1: Put knowledge to work, David. I know the Railroads of

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Speaker 1: the RAJ paper is probably your most famous work, but

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Speaker 1: a lot of people were pointing out when you won

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Speaker 1: the John Bates Metal that it was fantastic that you

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Speaker 1: had won it. All the two hundredth anniversary or birthday

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Speaker 1: of the notion of comparative advantage, which is of course

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Speaker 1: a big, big deal in economics, and it's basically the

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Speaker 1: idea that people can specialize in one type of industry

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Speaker 1: or production, and then they can trade with other people

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Speaker 1: who are also specialized and everyone can eventually benefit. Walk

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Speaker 1: us through your work when it comes to comparative advantage.

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Speaker 1: What have you been looking at and what have you found? All?

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Speaker 1: Everything I've done on comparative advantage. In fact, most of

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Speaker 1: my work really since my doctoral thesis has been joined

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Speaker 1: with a guy at m I T named are No

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Speaker 1: Costano and and so we um we started thinking about

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Speaker 1: comparative advantage and you're you're right. It was about two

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Speaker 1: d years ago to this kind of day that David

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Speaker 1: Ricardo really wrote down the first logical argument about why

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Speaker 1: trade between two people or two regions or two countries

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Speaker 1: would would you know, should benefit both of those people?

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Speaker 1: In that trade, and and the essence of his argument

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Speaker 1: was just a kind of simple example with two activities.

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Speaker 1: They were cloth and wine, and his example that and

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Speaker 1: two countries. These were England and Portugal, and the jet.

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Speaker 1: The example completely generalizes to as many countries and products

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Speaker 1: as you want. But but the two by two was

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Speaker 1: the minimum ingredient to kind of see the point. So

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Speaker 1: the one kind of catch that are know and I

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Speaker 1: got interested in, and this this point was well known,

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Speaker 1: but we kind of we were were a little bit

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Speaker 1: taken aback when we discovered it for ourselves, was that, Um,

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Speaker 1: once those two regions are actually trading, you know, I

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Speaker 1: suppose you observe them today, suppose you were observed England,

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Speaker 1: or even in David Kardo's time, you observe England and

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Speaker 1: Portugal they're actually trading cloth and line with each other.

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Speaker 1: Then you know, according to that very same model in

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Speaker 1: which that that you're using to explain the trade and

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Speaker 1: the understand the consequences of trade in that exact same model, um,

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Speaker 1: actually it would have to be the case that one

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Speaker 1: of one of the two activities is not being done

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Speaker 1: in one of the two countries, at least as as

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Speaker 1: you put it kind of there will be specialization and

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Speaker 1: and and that means, you know, in the sense that

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Speaker 1: there's something that somebody's not doing. So I guess it

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Speaker 1: won't surprise you that we kind of predict you'd expect

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Speaker 1: that England was not producing much or any wine, right, Um,

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Speaker 1: they that was sort of the result of specialization, was

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Speaker 1: that England was not producing wine. And the theory tells

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Speaker 1: us that there's good reasons for that. It's that England

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Speaker 1: is relatively worse at making wine than they aren't making

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Speaker 1: cloth relative to Portugal. But the question is how bad

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Speaker 1: are they making wine? And we kind of know they're bad,

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Speaker 1: both from introspection and also from the theory. We know

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Speaker 1: that they couldn't have been good, or else they would

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Speaker 1: have been producing it. But how bad and so And

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Speaker 1: of course that basic idea pervades all of economic life.

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Speaker 1: I mean, I know I benefit from not doing my

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Speaker 1: own dental work, But how bad would life be if

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Speaker 1: I had to be my own dentist? You know, I

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Speaker 1: I don't know, but I know it would be awful.

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Speaker 1: And that kind of unknown number, it's not just unknown,

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Speaker 1: it's kind of unknowable, right, I mean, Um, it's so

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Speaker 1: that so basically everything I've been working on with comparative

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Speaker 1: advantage with our no has this flavor of how could

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Speaker 1: an economist ever hope to know that fourth number in

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Speaker 1: Ricardo's example, That's the basic idea we got interested in

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Speaker 1: where one could know that in general that sort of

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Speaker 1: fourth number, or generally, just how good are regions at

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Speaker 1: doing the things they don't do? And um, the first

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Speaker 1: example that came to our mind was agriculture. We thought,

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Speaker 1: that's a case where you know, um, nowadays, regions of

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Speaker 1: let's say Iowa, most most farms grow either corn or

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Speaker 1: soy or maybe wheat. You know, other regions of the

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Speaker 1: US do different crops. And but so the question again

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Speaker 1: comes up, well, how bad how bad would life be

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Speaker 1: if we didn't sort of outsource all of our specialization,

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Speaker 1: all of our output of corn and soy to to

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Speaker 1: the corn and soy belt, right, I mean, what if

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Speaker 1: we had to do it ourselves in New St New England,

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Speaker 1: or in California or in the southeast. You know, we were,

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Speaker 1: of course, you know, since discovered, but deep down weren't

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Speaker 1: too surprised to learn that that there are, of course,

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Speaker 1: entire scientific fields, typically under the name of agronomy where

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Speaker 1: their goal is to, in a sense kind of just

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Speaker 1: try to tell farmers, you know, how, given your soil,

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Speaker 1: given your climate, given everything else about your local environment,

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Speaker 1: how good would you be at growing any of the

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Speaker 1: following kind of list of crops. You know, corn and soy,

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Speaker 1: but also cotton, and and and and wheat and peanuts,

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Speaker 1: you know, And and it's that advice that agronomous give

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Speaker 1: to farmers that of course helps those farmers make the

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Speaker 1: right decision about what to grow. But we so we

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Speaker 1: sort of in a sense kind of downloaded that advice

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Speaker 1: in a in a data file, you know, the advice

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Speaker 1: from the agronomous, the actual numbers on you know, if

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Speaker 1: this small parcel of land somewhere in the U s.

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Speaker 1: Divide the US up into about a million small parcels

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Speaker 1: of land, and the agronomos will tell you how good

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Speaker 1: each parcel would be at a whole, at virtually any crop.

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Speaker 1: And of course it doesn't mean the agronomous are necessarily right,

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Speaker 1: but they but they have kind of hundreds of years

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Speaker 1: of their own randomized trials in a sense, and and

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Speaker 1: greenhouse trials, etcetera, and all physiological knowledge of how crops

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Speaker 1: work too in order to build up those numbers. So anyway,

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Speaker 1: so we were kind of designed to study around that

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Speaker 1: information because we thought it was it was it was

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Speaker 1: the core of the economic notion of comparative vantage was

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Speaker 1: to know things like, you know, how how how hard

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Speaker 1: would it be to grow peanuts in Iowa. So we

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Speaker 1: then kind of use that information and filtered it through

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Speaker 1: the last hundred year hundred and twenty years or so

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Speaker 1: of US um agricultural history, you know, as the as

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Speaker 1: the U. S. Counties. The one one, one plausible story

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Speaker 1: that we think is consistem with the data is that

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Speaker 1: over the last hundred thirty years or so, at least

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Speaker 1: this study started in eight the ability for one county

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Speaker 1: in the US to trade with another county in the U. S.

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Speaker 1: Or with you know, consumers elsewhere in the US, like

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Speaker 1: in a big city. Uh, that ability to trade you know,

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Speaker 1: dramatically improved. We had the railroads, we had the interstates,

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Speaker 1: we had you know, the the the invention of the truck,

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Speaker 1: you know, major major improvements in the ability to trade.

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Speaker 1: And so we wanted to kind of quantify how how

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Speaker 1: beneficial that that process was for the U. S. Economy

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Speaker 1: as the whole as a whole, and what the numbers

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Speaker 1: of came out. We're we're startling to me at least,

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Speaker 1: you know, the U s. Agriculture has been this incredible

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Speaker 1: growth story, right, I mean, in a sense, the fastest

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Speaker 1: product sustained productivity growth that that we've experienced in any

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Speaker 1: sector has happened in agriculture over the last hundred years

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Speaker 1: or so. And that's why we can feed the nation

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Speaker 1: and beyond with I don't forget the exact number, but

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Speaker 1: something under under three percent of the workforce. And so

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Speaker 1: that dramatic growth and productivity is just amazing. But according

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Speaker 1: to our estimates, about half of it comes from just

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Speaker 1: pure allocative efficiency in the sense of kind of specialization,

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Speaker 1: places being sort of free as markets are trading, places

432
00:23:42,359 --> 00:23:44,520
Speaker 1: are free to specialize in what they're good at and

433
00:23:44,520 --> 00:23:47,199
Speaker 1: not produce what they're bad at, and that that of

434
00:23:47,200 --> 00:23:49,960
Speaker 1: course enhances aggregate productivity, and that those are the gains

435
00:23:49,960 --> 00:23:52,640
Speaker 1: from trade, those are the gains due to comparative advantage

436
00:23:52,640 --> 00:23:55,840
Speaker 1: at work. That's pretty startling. We just have a few

437
00:23:55,920 --> 00:23:57,960
Speaker 1: minutes left, and of course we want to talk about

438
00:23:58,040 --> 00:24:03,560
Speaker 1: some current applicability of your research, and obviously the place

439
00:24:03,960 --> 00:24:06,320
Speaker 1: I think most people's minds would go is sort of,

440
00:24:06,440 --> 00:24:08,440
Speaker 1: you know, some of the trade disputes that the US

441
00:24:08,560 --> 00:24:12,560
Speaker 1: might soon find itself in. But something else occurred to

442
00:24:12,640 --> 00:24:15,680
Speaker 1: me that is maybe a little more off the beaten path,

443
00:24:15,760 --> 00:24:19,720
Speaker 1: but also interesting, which is that India, which you studied, obviously,

444
00:24:20,320 --> 00:24:23,080
Speaker 1: is still dealing with a lot of the same things

445
00:24:23,080 --> 00:24:26,239
Speaker 1: that you talked about in terms of the diversity of

446
00:24:26,240 --> 00:24:29,960
Speaker 1: its regions and lack of a completely coherent domestic free

447
00:24:29,960 --> 00:24:34,359
Speaker 1: trade area. I believe it was sometime last year that

448
00:24:34,440 --> 00:24:38,119
Speaker 1: the Modi government tried to uh sort of move forward

449
00:24:38,160 --> 00:24:41,080
Speaker 1: and pushing through a national sales tax so that there

450
00:24:41,119 --> 00:24:44,720
Speaker 1: wouldn't be this sort of disparate tax regime across regions.

451
00:24:45,040 --> 00:24:48,280
Speaker 1: There's also efforts to have the sort of national payment

452
00:24:48,400 --> 00:24:54,119
Speaker 1: system to harmonize and unify payments. Do you see applicability

453
00:24:54,200 --> 00:24:56,480
Speaker 1: of your work on India's rail system to some of

454
00:24:56,480 --> 00:25:00,760
Speaker 1: the big domestic debates happening in Indian policy right now? Yeah?

455
00:25:00,760 --> 00:25:04,240
Speaker 1: I do, I mean I of course, you know, it

456
00:25:04,240 --> 00:25:07,080
Speaker 1: would be naive to suggest that sort of the It

457
00:25:07,160 --> 00:25:10,520
Speaker 1: is easy to translate a lesson from one technology one

458
00:25:10,560 --> 00:25:12,480
Speaker 1: point in the past, you know, because it's a very

459
00:25:12,520 --> 00:25:15,560
Speaker 1: backward technology was this. These trains were slow and you know,

460
00:25:15,920 --> 00:25:17,720
Speaker 1: they were the first trains of the world that's ever seen.

461
00:25:18,119 --> 00:25:21,440
Speaker 1: So even just for studying trains, they're they're misleading. A

462
00:25:21,520 --> 00:25:25,040
Speaker 1: piece of evidence the more versatile and long living I

463
00:25:25,040 --> 00:25:28,119
Speaker 1: think piece of evidence is more to do with or

464
00:25:28,119 --> 00:25:29,800
Speaker 1: a lesson that we learned from that that that kind

465
00:25:29,840 --> 00:25:33,120
Speaker 1: of work is more just to do with the the

466
00:25:33,200 --> 00:25:35,680
Speaker 1: overall sense of benefits from trading. You know, if if

467
00:25:35,680 --> 00:25:38,480
Speaker 1: you if you do something that allows more trade, the

468
00:25:38,520 --> 00:25:40,480
Speaker 1: odds are good that people are going to benefit on

469
00:25:40,560 --> 00:25:43,320
Speaker 1: the whole, that the average kind of the total pie

470
00:25:43,440 --> 00:25:46,160
Speaker 1: will grow. Um. And that's the kind of thing I've

471
00:25:46,200 --> 00:25:48,679
Speaker 1: tried to I've tried to quantify and I think you know,

472
00:25:48,800 --> 00:25:51,160
Speaker 1: so you know, the lesson from that that work I've done,

473
00:25:51,160 --> 00:25:53,440
Speaker 1: as well as the huge body of work that I've

474
00:25:53,480 --> 00:25:56,760
Speaker 1: read that other that other people have done that's built

475
00:25:56,800 --> 00:25:59,359
Speaker 1: up our knowledge of that uh tends to leave a

476
00:25:59,440 --> 00:26:02,880
Speaker 1: pretty an equivocal picture that on aggregate, those games can

477
00:26:02,920 --> 00:26:05,680
Speaker 1: be um, you know, exist and or there in the data,

478
00:26:05,880 --> 00:26:09,640
Speaker 1: and they're and they're important and they're worth um yeah,

479
00:26:09,720 --> 00:26:11,480
Speaker 1: they're worth not standing kind of in the way of

480
00:26:11,600 --> 00:26:13,359
Speaker 1: you know that, so when we when we make it

481
00:26:13,400 --> 00:26:15,280
Speaker 1: hard for people within the same country to trade with

482
00:26:15,320 --> 00:26:17,560
Speaker 1: each other, we stand in the way of those benefits. Uh,

483
00:26:18,080 --> 00:26:19,600
Speaker 1: just way we make it hard for me to do

484
00:26:19,680 --> 00:26:21,760
Speaker 1: We make it hard for me to hire a dentist

485
00:26:21,880 --> 00:26:23,880
Speaker 1: rather than doing my own dental work. You know, we

486
00:26:23,880 --> 00:26:25,720
Speaker 1: we stand in the way of gains from trade between

487
00:26:25,720 --> 00:26:28,399
Speaker 1: me and the dentist. Thankfully, in the US, there's not

488
00:26:28,520 --> 00:26:32,840
Speaker 1: much discussion of intrnational trade barriers, and in fact, everyone

489
00:26:32,880 --> 00:26:36,240
Speaker 1: tends to agree that improving infrastructure, transportation infrastructure would be

490
00:26:36,280 --> 00:26:37,960
Speaker 1: a good idea. You know, we we don't think it's

491
00:26:38,520 --> 00:26:40,440
Speaker 1: you know, we we want to encourage more trade between

492
00:26:40,440 --> 00:26:45,120
Speaker 1: Colorado and California, or Kansas or Connecticut, and we we

493
00:26:45,119 --> 00:26:47,040
Speaker 1: we think we have a basic instinct that those are

494
00:26:47,359 --> 00:26:51,639
Speaker 1: things are a good idea, and and that's consistent with

495
00:26:51,680 --> 00:26:54,120
Speaker 1: the evidence that I've seen and that I've worked on myself.

496
00:26:54,680 --> 00:26:57,520
Speaker 1: And I don't think it needs to be any different internationally.

497
00:26:57,600 --> 00:26:59,520
Speaker 1: Just kind of turned into your first question about the

498
00:26:59,560 --> 00:27:03,920
Speaker 1: Internet shield trade policy terrorists. There's no good reason to

499
00:27:03,960 --> 00:27:06,919
Speaker 1: embrace international trade and yet stand in the way of

500
00:27:07,000 --> 00:27:10,840
Speaker 1: international trade, in my book, unless one possible reason it

501
00:27:10,840 --> 00:27:14,520
Speaker 1: would be extreme distributional concerns. You know, if you I've

502
00:27:14,560 --> 00:27:18,359
Speaker 1: stressed obviously aggregate gains, I have not. You know, myself

503
00:27:18,400 --> 00:27:23,480
Speaker 1: worked on the distributional consequence is how virtually any change

504
00:27:23,480 --> 00:27:25,920
Speaker 1: in the economy is very likely to have people who

505
00:27:25,920 --> 00:27:28,920
Speaker 1: are harmed by the change. You know, as Walmart displaced Kmart,

506
00:27:28,920 --> 00:27:32,480
Speaker 1: and as Amazon displaces Walmart, people suffer, right, I mean,

507
00:27:32,840 --> 00:27:36,640
Speaker 1: people who have jobs and capital tied up in the industry,

508
00:27:36,680 --> 00:27:40,399
Speaker 1: the firm that's being pushed out by competition, they suffer,

509
00:27:40,440 --> 00:27:43,720
Speaker 1: and and competition from foreigners is no different. So we

510
00:27:43,800 --> 00:27:47,320
Speaker 1: need policies that help minimize that suffering. But but standing

511
00:27:47,320 --> 00:27:49,920
Speaker 1: in the way of growing the aggregate pie I don't

512
00:27:49,960 --> 00:27:52,879
Speaker 1: think is likely to be the best policy solution to

513
00:27:52,920 --> 00:27:56,439
Speaker 1: those challenges. David, can I ask just one very very

514
00:27:56,520 --> 00:27:59,480
Speaker 1: quick follow up, Um, which is I mean, given the

515
00:27:59,520 --> 00:28:03,359
Speaker 1: body of academic research that points out the benefits of

516
00:28:03,400 --> 00:28:06,960
Speaker 1: trade and the benefits of comparative advantage and specialization, why

517
00:28:07,000 --> 00:28:10,040
Speaker 1: do you think the notion that trade is a zero

518
00:28:10,160 --> 00:28:13,760
Speaker 1: sum game seems to persist in the wider world, and

519
00:28:13,800 --> 00:28:15,719
Speaker 1: in some parts of the world seems to be growing.

520
00:28:16,320 --> 00:28:19,520
Speaker 1: I think there's two basic answers. One is um, yeah,

521
00:28:19,600 --> 00:28:23,760
Speaker 1: and this is a well known problem that political scientists

522
00:28:23,800 --> 00:28:26,000
Speaker 1: have talked about for a long time that whenever you

523
00:28:26,040 --> 00:28:29,919
Speaker 1: have something that with you know, as I stressed lot originally,

524
00:28:29,960 --> 00:28:31,240
Speaker 1: any change I can think of it is going to

525
00:28:31,320 --> 00:28:34,840
Speaker 1: generate benefits for you know, some people, maybe everyone, and

526
00:28:34,920 --> 00:28:38,040
Speaker 1: some costs for some people. But sort of trade and

527
00:28:38,080 --> 00:28:40,200
Speaker 1: many many other things like it, you know, free markets

528
00:28:40,200 --> 00:28:44,920
Speaker 1: in general, tenders that have very concentrated um costs. When

529
00:28:44,920 --> 00:28:48,200
Speaker 1: I say concentrated, concentrated on a relatively small chunk of

530
00:28:47,760 --> 00:28:51,520
Speaker 1: the of the population, and very dispersed benefits, right, I mean,

531
00:28:51,600 --> 00:28:55,680
Speaker 1: think of think of China, right, They've displaced somewhere, you know,

532
00:28:56,040 --> 00:28:58,720
Speaker 1: relatively I mean a huge number of jobs in the

533
00:28:58,800 --> 00:29:02,120
Speaker 1: in the in the absolute, but relative to the total population,

534
00:29:02,120 --> 00:29:06,560
Speaker 1: a relatively small percentage of workers. Yet virtually all of

535
00:29:06,640 --> 00:29:10,160
Speaker 1: us every day, you know, consume things that that that

536
00:29:10,240 --> 00:29:13,840
Speaker 1: are cheaper and maybe only exist thanks to large foreign

537
00:29:13,840 --> 00:29:17,360
Speaker 1: manufacturing countries like China. So so um. But you know,

538
00:29:17,360 --> 00:29:19,800
Speaker 1: I think deep down there's a bit of us a

539
00:29:19,880 --> 00:29:24,160
Speaker 1: incumbent producer bias in our in our society, you know,

540
00:29:24,240 --> 00:29:26,520
Speaker 1: the it's as if you know, it's as if as

541
00:29:26,520 --> 00:29:28,640
Speaker 1: a nation we think I mean, I don't mean we all,

542
00:29:28,680 --> 00:29:31,560
Speaker 1: but if you you listen to a number of people,

543
00:29:31,600 --> 00:29:34,520
Speaker 1: you get this impression that we were just dying to produce, right,

544
00:29:34,560 --> 00:29:38,200
Speaker 1: I mean when when my wife and I trade, you know,

545
00:29:38,240 --> 00:29:41,800
Speaker 1: when we when we kind of bargain over who's gonna

546
00:29:41,840 --> 00:29:45,320
Speaker 1: sort of cook the food and who's gonna mow the lawn.

547
00:29:45,360 --> 00:29:47,920
Speaker 1: You know, I think we specialized according to comparative vantage.

548
00:29:47,960 --> 00:29:50,240
Speaker 1: We understand that that's in our mutual best interest. We

549
00:29:50,280 --> 00:29:53,440
Speaker 1: tend not to fight over who gets to do more producing. Yeah,

550
00:29:53,720 --> 00:29:57,520
Speaker 1: we you know, the debate is more about like, if anything,

551
00:29:57,600 --> 00:30:00,560
Speaker 1: you know, we'd both rather not produce. But so how weirdly,

552
00:30:00,600 --> 00:30:03,520
Speaker 1: at the national level, when it concerns international trade, we

553
00:30:03,520 --> 00:30:06,240
Speaker 1: were we stress over the fact that there's a trade deficit.

554
00:30:06,320 --> 00:30:08,680
Speaker 1: You know, that's of course, like the other people are

555
00:30:08,680 --> 00:30:10,720
Speaker 1: doing the producing, you know, you know, we shouldn't sort

556
00:30:10,760 --> 00:30:13,800
Speaker 1: did in some sense embrace that. So I don't understand

557
00:30:13,800 --> 00:30:17,240
Speaker 1: how at the micro level people understand that that kind

558
00:30:17,280 --> 00:30:21,520
Speaker 1: of consuming is good and producing is costly and unpleasant,

559
00:30:21,560 --> 00:30:24,800
Speaker 1: whereas the macro level it's sort of the opposite. I

560
00:30:25,440 --> 00:30:29,040
Speaker 1: I sort of think deep down that might be because producers,

561
00:30:29,200 --> 00:30:31,360
Speaker 1: you know, I have have a lot of power obviously

562
00:30:31,400 --> 00:30:34,240
Speaker 1: for an individual firm and would much rather produced than

563
00:30:35,200 --> 00:30:38,560
Speaker 1: than not right, Uh, that's how they make their profits.

564
00:30:38,560 --> 00:30:41,560
Speaker 1: But that an aggregate societal level, we we should sort

565
00:30:41,600 --> 00:30:44,840
Speaker 1: of embrace the fact that we can um consume for

566
00:30:44,960 --> 00:30:48,360
Speaker 1: less effort, and that's the basic notion of gains from

567
00:30:48,400 --> 00:30:51,000
Speaker 1: trade is just more productive. We can get more for

568
00:30:51,120 --> 00:30:55,760
Speaker 1: less um for less input. All right. Dave Donaldson, Associate

569
00:30:55,800 --> 00:30:59,240
Speaker 1: economics professor at Stanford and the recent winner of the

570
00:30:59,320 --> 00:31:03,920
Speaker 1: John Bates Clark Metal Award, Thank you so much. Fascinating conversation,

571
00:31:04,280 --> 00:31:08,800
Speaker 1: fascinating work. I can't wait to continue seeing the evolution

572
00:31:08,840 --> 00:31:12,320
Speaker 1: of what you've done. Really appreciate you coming on the podcast. Well,

573
00:31:12,360 --> 00:31:14,320
Speaker 1: like I said, thanks again for talking about me. It's

574
00:31:14,320 --> 00:31:16,480
Speaker 1: been a play. Yeah, come back when you want a

575
00:31:16,520 --> 00:31:31,120
Speaker 1: Noball prize. Okay, I don't want to drink it, So, Joe,

576
00:31:31,240 --> 00:31:34,080
Speaker 1: I thought that was fascinating and it was sort of

577
00:31:34,200 --> 00:31:37,200
Speaker 1: right in the sweet spot for our All Thoughts podcast

578
00:31:37,280 --> 00:31:41,560
Speaker 1: because it uses all these historical examples to really illustrate

579
00:31:41,600 --> 00:31:44,280
Speaker 1: some of the stuff that's going on today. Right. Yeah,

580
00:31:44,320 --> 00:31:48,920
Speaker 1: I love that conversation. Um. I think my favorite detail

581
00:31:49,360 --> 00:31:52,800
Speaker 1: was the idea that the best recorded data on all

582
00:31:52,840 --> 00:31:57,320
Speaker 1: of this Indian trading and taxes and income levels and

583
00:31:57,440 --> 00:32:01,520
Speaker 1: whether was what was how in the UK and then

584
00:32:01,760 --> 00:32:05,200
Speaker 1: the ultimate solution to putting that in usable form was

585
00:32:05,240 --> 00:32:08,360
Speaker 1: to take photographs of all the pieces of paper and

586
00:32:08,400 --> 00:32:13,280
Speaker 1: then email fifty JPEGs two people in India appropriately enough

587
00:32:13,520 --> 00:32:17,480
Speaker 1: to then put back into an Excel spreadsheet to be

588
00:32:17,560 --> 00:32:19,800
Speaker 1: usable for an economist. I mean, it does make you

589
00:32:19,840 --> 00:32:23,800
Speaker 1: wonder what other academic research could be enabled by new

590
00:32:23,840 --> 00:32:27,360
Speaker 1: technology relatively soon, based on old data. And I think

591
00:32:27,400 --> 00:32:31,200
Speaker 1: we've had similar discussions about this before, at least I

592
00:32:31,240 --> 00:32:34,280
Speaker 1: think I did with Sid Verma and Simon Henrisen. The

593
00:32:34,360 --> 00:32:38,400
Speaker 1: other thing that I thought was really interesting was some

594
00:32:38,520 --> 00:32:40,880
Speaker 1: of the stuff he was saying about the way we

595
00:32:40,960 --> 00:32:45,640
Speaker 1: trade within countries versus the way we trade internationally. And

596
00:32:45,680 --> 00:32:48,880
Speaker 1: it does seem to be that trading with our neighbors

597
00:32:48,920 --> 00:32:52,200
Speaker 1: within a country, with the exception of India, um just

598
00:32:52,240 --> 00:32:55,960
Speaker 1: seems to be much more palatable to us than trading

599
00:32:56,040 --> 00:32:59,000
Speaker 1: with other people outside of the country, which I assume

600
00:32:59,040 --> 00:33:01,440
Speaker 1: speaks to human natuy here a little bit. Yeah, I mean,

601
00:33:01,480 --> 00:33:04,800
Speaker 1: I think this does really get to questions of nationhood,

602
00:33:04,960 --> 00:33:08,000
Speaker 1: so that if we see, you know, they're even just

603
00:33:08,080 --> 00:33:12,400
Speaker 1: sort of domestically within this country. We have some places

604
00:33:12,440 --> 00:33:16,520
Speaker 1: that have done very well, like the coastal areas San Francisco,

605
00:33:16,560 --> 00:33:19,640
Speaker 1: New York, some places that have done really poorly, and

606
00:33:19,640 --> 00:33:22,440
Speaker 1: we it doesn't really you know, for the most part,

607
00:33:22,720 --> 00:33:27,120
Speaker 1: intra national gains from trade don't seem to get people

608
00:33:27,120 --> 00:33:30,000
Speaker 1: anxious or winners and losers. But then the idea that

609
00:33:30,520 --> 00:33:32,880
Speaker 1: you know, if we expanded beyond the borders and some

610
00:33:32,920 --> 00:33:36,160
Speaker 1: country is doing well, or and the perception that parts

611
00:33:36,160 --> 00:33:38,960
Speaker 1: of this country are losing out in that trade, then

612
00:33:39,000 --> 00:33:41,320
Speaker 1: that really sort of you know, strikes a deep chord

613
00:33:41,400 --> 00:33:44,400
Speaker 1: with people, gives them anxiety. I don't think there's any

614
00:33:44,440 --> 00:33:46,400
Speaker 1: obvious way to resolve that. I think the sort of

615
00:33:46,440 --> 00:33:50,440
Speaker 1: purely academic way perhaps still doesn't sit right with people.

616
00:33:50,440 --> 00:33:52,440
Speaker 1: And obviously we see that playing politically, but it is

617
00:33:52,440 --> 00:33:55,320
Speaker 1: a really interesting comparison. All right, shall we call it

618
00:33:55,400 --> 00:33:58,040
Speaker 1: a day, Let's do it cool. All right, This has

619
00:33:58,080 --> 00:34:01,880
Speaker 1: been another edition of the APPS podcast. I'm Tracy Alloway.

620
00:34:01,960 --> 00:34:04,880
Speaker 1: You can follow me on Twitter at Tracy Alloway, and

621
00:34:04,920 --> 00:34:07,400
Speaker 1: I'm Joe Wis and all follow me on Twitter at

622
00:34:07,440 --> 00:34:12,000
Speaker 1: the Stall and also Tracy, I realized we never unlike

623
00:34:12,040 --> 00:34:15,880
Speaker 1: some other podcasts, thank are awesome producers, So I think

624
00:34:15,920 --> 00:34:19,520
Speaker 1: we should start doing that. Maybe we're just ungrateful. I

625
00:34:19,560 --> 00:34:22,520
Speaker 1: think we're it's it's just I realized other people do that.

626
00:34:22,600 --> 00:34:24,560
Speaker 1: We've never been doing it. We've been doing this podcast

627
00:34:24,600 --> 00:34:28,200
Speaker 1: for a long time. Should I say we change the tradition?

628
00:34:29,000 --> 00:34:32,440
Speaker 1: We want to thank are awesome producers and Sarah Patterson

629
00:34:32,480 --> 00:34:35,960
Speaker 1: who is on Twitter at Sarah patt with two ts,

630
00:34:35,960 --> 00:34:47,440
Speaker 1: and the head of Podcasts Alick McCabe. But knowledge to

631
00:34:47,520 --> 00:34:50,120
Speaker 1: work and grow your business with c i T from

632
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633
00:34:55,320 --> 00:34:59,000
Speaker 1: and treasury management services for small and middle market businesses.

634
00:34:59,200 --> 00:35:01,960
Speaker 1: Learn more at i T dot com put Knowledge to

635
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Speaker 1: Work