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Speaker 1: Hello, and welcome to another episode of the Odd Lots Podcast.

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Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway.

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Speaker 1: Tracy, I'm super late to everything. First of all, did

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Speaker 1: you play Wordle?

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Speaker 2: I did. I didn't get obsessed with it like some

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Speaker 2: people did, but I think we were all fairly bored

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Speaker 2: during that time period and on the lookout for any

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Speaker 2: sort of entertainment.

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Speaker 1: And it never clicked. But I'm always late to everything.

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Speaker 1: Like a year ago, people were telling me it's like, oh,

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Speaker 1: you gotta play this game trade all, and I did end,

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Speaker 1: but like three or four months ago, I started really

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Speaker 1: getting into it, and I think you're into it now too.

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Speaker 3: Yeah.

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Speaker 2: I think we both started playing around the same time,

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Speaker 2: probably because we heard about it from the same person.

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Speaker 2: It is a fun game, so for those who don't

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Speaker 2: know it is like Wordle. It basically presents a sort

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Speaker 2: of graphic schematic of an unnamed country's exports, and you

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Speaker 2: have to try to guess what country it is.

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Speaker 1: Yeah, exactly, And I'm not very good at geography, so

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Speaker 1: often it'll say like, Okay, you're close, but it's like

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Speaker 1: you have to go fifteen hundred kilometers to the northwest.

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Speaker 1: I'm pretty bad at geography, so I'm not good. But

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Speaker 1: as playing this game and watching the way the different

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Speaker 1: shapes of different countries export mix, I feel like I've

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Speaker 1: really started to learn things about the world.

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Speaker 2: I have learned so much about the economy of Angola.

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Speaker 3: Right.

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Speaker 1: So you see a country and it's like eighty percent

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Speaker 1: of their exports are like coffee in gold, right or

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Speaker 1: something like that, and you're like, Okay, this is a

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Speaker 1: relatively poor country. It has a lot of growth left

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Speaker 1: to do. And then you see another country and it's

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Speaker 1: like advanced circuits and medicine and hangars and bananas and

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Speaker 1: all this stuff, and like, oh, and you start to

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Speaker 1: see these shapes and these distributions. That's sort of like

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Speaker 1: tell you things. It's like, Okay, I can guess maybe

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Speaker 1: this is in Europe, or maybe they have a lot

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Speaker 1: of things, maybe it's in Eastern Europe. You suddenly sort

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Speaker 1: of learn like how rich countries goods exports really differ

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Speaker 1: from poorer countries goods exports.

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Speaker 2: Yeah, totally. There are three clues that it gives you.

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Speaker 2: One is distance, as you just mentioned, and the other

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Speaker 2: is total size of exports. So that gives you an

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Speaker 2: indication of how big or small that economy is. And

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Speaker 2: then that third thing is the nature of the exports,

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Speaker 2: and it sort of divides them up into different categories,

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Speaker 2: but it gives you a really good snapshot of a

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Speaker 2: country's economy. And as you play the game, you start

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Speaker 2: to recognize I guess certain economic export attributes. Yeah, go

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Speaker 2: with certain types of countries or economies.

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Speaker 1: Right, so integrated circuits and palm oil, probably Southeast Asia, right,

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Speaker 1: something like that. Anyway, this game that you and I

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Speaker 1: have become obsessed with, it's sort of based on this

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Speaker 1: work related to economic complexity, the Atlas of Economic Complexity,

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Speaker 1: this sort of idea, and this is something that's come up.

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Speaker 1: It came up in an episode we did with Henry

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Speaker 1: Williams and David Os. It came up with the episode

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Speaker 1: we did with Dan Wong and why some countries can

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Speaker 1: develop airline industries and other countries can't. This idea that

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Speaker 1: complexity of goods exports complexity is in itself a sort

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Speaker 1: of predictor of wealth.

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Speaker 2: Yeah, and maybe it's also a desirable model for countries

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Speaker 2: to sort of aspire to, this idea that maybe they

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Speaker 2: want to get away from simply producing a bunch of

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Speaker 2: t shirts, So like fifty percent of their economy is

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Speaker 2: T shirt exports or something like that. They want to

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Speaker 2: get to a place where they have expertise across a

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Speaker 2: broad and value added sort of realm of exports.

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Speaker 1: Or maybe they export a lot of nickel and they

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Speaker 1: want to be in like refined nickel, right, some nickel

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Speaker 1: relatais the commodity just pure Anyway, this has sort of

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Speaker 1: like really opened up a lot of these conversations playing

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Speaker 1: like thinking about the world. So I'm really excited about

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Speaker 1: our guest because our guest has done more work on

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Speaker 1: this idea of economic complexity and why nations are able

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Speaker 1: to develop complex, rich economies. He's also the creator of

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Speaker 1: that at list of economic complexity. We're going to be

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Speaker 1: speaking with Ricardo Housman. He's a professor at the Harvard

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Speaker 1: Kennedy School and the founding director of the Harvard Growth Lab.

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Speaker 1: All of my trade off friends are super excited about

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Speaker 1: listening to this conversation. Doctor Husman. Thank you so much

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Speaker 1: for coming on odd Lats.

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Speaker 3: Oh, it's a pleasure to be with you. Thank you

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Speaker 3: for inviting me.

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Speaker 1: Absolutely. What is economic complexity?

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Speaker 3: Economic complexity is an attempt to measure how much countries

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Speaker 3: or places know what to do, so like I try

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Speaker 3: to measure no. How Now, if you want to think

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Speaker 3: about knowledge and say, well, and I know people who

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Speaker 3: have a bachelor's degree, people who will hire high school dropouts,

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Speaker 3: people who have a PhD. That sort of tells you

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Speaker 3: how much a person knows. But if you ask yourself,

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Speaker 3: how much does a society know, well, that would be different.

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Speaker 3: That would not be characterized by the average, say the

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Speaker 3: average number of years of schooling that the society has.

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Speaker 3: Not A society that is full of just dentists will

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Speaker 3: know less than a society that is half dentists and

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Speaker 3: half lawyers, or a society that is a third dentist,

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Speaker 3: a third lawyer, or third engineers. So in some sense

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Speaker 3: you want to know how much the whole of society knows.

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Speaker 3: And one of the important things about knowledge is that

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Speaker 3: knowledge at the societal level has been exploding exponentially, but

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Speaker 3: our mental capacity to know has not. So the way

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Speaker 3: the economy has been adapting and adopting growing amounts of

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Speaker 3: knowledge is by putting different bits of knowledge in different heads,

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Speaker 3: sort of like parallel processing. You know, if you want

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Speaker 3: to run a company, you will need somebody who knows

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Speaker 3: about accounting, about finance, about marketing, about human resource management,

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Speaker 3: about contracts, about taxes, about procurement, about engineering. So you

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Speaker 3: want to have a lot of knowledge to run these things,

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Speaker 3: but you cannot stuff that knowledge into a single head.

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Speaker 3: You have to spread it into a bunch of heads,

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Speaker 3: and then you have to bring those heads together back again.

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Speaker 3: You have to kind of put humpty dumpty back together again.

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Speaker 3: So the way in which a society grows is it

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Speaker 3: grows its knowledge by putting different bits of knowledge in

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Speaker 3: different heads and then by bringing those heads together. Now,

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Speaker 3: if a society makes very simple things, it makes things

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Speaker 3: that can be done by few people, because the knowledge

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Speaker 3: that is needed to make one of those things, you know,

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Speaker 3: fits in just a few heads. But if you're going

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Speaker 3: to do stuff that requires not a lot of knowledge,

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Speaker 3: you'll have to bring many, many more of these heads together.

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Speaker 3: You'll have to network these brains together to make that thing.

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Speaker 3: So complexity emerges as the consequence of distributed knowledge in society.

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Speaker 3: You have different bits of people knowing different things, and

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Speaker 3: then you have to bring those things together, and then

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Speaker 3: these complex networks emerge from that process. So, in some sense,

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Speaker 3: what is really driving growth is this growth of knowledge

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Speaker 3: and the growth of using that knowledge, and consequently it's

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Speaker 3: in this spread of different bits of knowledge in different

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Speaker 3: heads and the ability to bring those heads together to

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Speaker 3: make relatively long chains of brains.

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Speaker 2: I have a bunch of questions already about comparing and

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Speaker 2: contrasting measuring complexity versus the way economics has traditionally handled

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Speaker 2: some of this. But maybe a step back question why

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Speaker 2: did you decide to start looking into this? What is

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Speaker 2: the benefit of looking at complexity within a particular society

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Speaker 2: or economy.

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Speaker 3: It goes back to the question and when Adam Smith

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Speaker 3: asked himself what's the source of the wealth of nations?

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Speaker 3: He said it was the division of labor? But why

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Speaker 3: the hell would the division of labor matter? And he

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Speaker 3: gives a thin factory example that if you split the

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Speaker 3: work and this guy does the head and this guy

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Speaker 3: does the body, et cetera, that increased productivity. That idea,

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Speaker 3: I think has a kernel of what the story is.

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Speaker 3: But this stuff becomes incredibly powerful when you're talking about

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Speaker 3: knowledge driving the economy and driving society. So it's really

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Speaker 3: about the division of knowledge that's what drives growth. It's

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Speaker 3: the division of knowledge that allows the whole to know

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Speaker 3: more than its parts. And so how would you go

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Speaker 3: about measuring what a society knows how to do. Well,

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Speaker 3: let's look at what they do, because if they do something,

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Speaker 3: it means that they know how to do it right,

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Speaker 3: So it's proof that they know how to do it.

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Speaker 3: So we can look at what a society does to

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Speaker 3: figure out what is it that they know how to do.

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Speaker 3: So when you look at a country and say, okay,

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Speaker 3: this country is only good at doing a few things,

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Speaker 3: and this other country is good at doing many more things.

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Speaker 3: And this country here is making things that seem to

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Speaker 3: be simple things that can be done in small groups.

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Speaker 3: This other country that has to do things that require

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Speaker 3: this very broad network of brains coming together to make something.

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Speaker 3: It tells you something about how those economies are managing knowledge.

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Speaker 1: Tracy is sort of hinting at this, and I want

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Speaker 1: to sort of drill down on this before moving on.

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Speaker 1: You know, there are various traditional ways in which we

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Speaker 1: measure economies. GDP is more or less a different way

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Speaker 1: of like you just add up everyone's income and you say, okay,

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Speaker 1: this country is richer than this country, and this country

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Speaker 1: is richer than the next. Give us the basics. You

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Speaker 1: know you have country A and country B. How do

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Speaker 1: you like measure? Okay, this country is more comp more

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Speaker 1: complex economy capable of more complexity than country be.

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Speaker 3: So let me first say you can measure GDP and

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Speaker 3: so GPS how much countries are able to make in

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Speaker 3: terms of income. That doesn't tell you why they are

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Speaker 3: able to make it. Economic complexity is trying to get

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Speaker 3: at why is it that you are able to generate

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Speaker 3: more income? What's underpinning that? And for that, we like

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Speaker 3: to measure how much a society knows in our standard measure.

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Speaker 3: We've now applied it to a whole different bunch of fields,

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Speaker 3: not just in exports but other things. But we started

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Speaker 3: with exports, and the reason why we started with exports

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Speaker 3: is because we needed a data set that included all

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Speaker 3: the countries in the world, so we could benchmark all

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Speaker 3: the countries in the world with a standardized classification. And

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Speaker 3: since international trade it involves different countries, they've all agreed

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Speaker 3: on some common classification scheme, so it was expedient for us.

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Speaker 3: But experts are also something that tells you whether a

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Speaker 3: society is good enough at making something that it is

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Speaker 3: able to sell abroad, So it's kind of like a

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Speaker 3: litmus test that you're pretty good at making something. So

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Speaker 3: I don't really care how much they make. I just

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Speaker 3: care that they are able to make it, so that

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Speaker 3: that knowledge is somewhere in that society. So the way

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Speaker 3: you would think about calculating how much knowledge of society

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Speaker 3: has would say, tell me how many different things that

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Speaker 3: are they able to make? You're mentioning angola, or they

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Speaker 3: make mostly oils essentially that's their thing, or do they

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Speaker 3: do many things? So the diversity of their export basket

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Speaker 3: is kind of like a first cut, right, But you

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Speaker 3: would say, well, but products differ in how knowledge intensive

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Speaker 3: they are and how difficult they are to make. So

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Speaker 3: we found a trick on how to measure how difficult

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Speaker 3: it is to make a product by simply asking the

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Speaker 3: question how many countries are able to make this product.

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Speaker 3: So if you talk about raw wood, many many countries

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Speaker 3: export raw wood. If you tell me about microscopes or

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Speaker 3: X raymage, very few countries are able to export those things.

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Speaker 3: So that tells you something already about how difficult it

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Speaker 3: is to make these things, right, So we call that

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Speaker 3: the ubiquity of a product. And then you can ask

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Speaker 3: yourself the question, Okay, on average, how ubiquitous are the

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Speaker 3: products that this country makes? That is this country making

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Speaker 3: mostly things that are simple to do that everybody knows

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Speaker 3: how to do, or or they make things that are

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Speaker 3: hard to do, things that are done in few places.

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Speaker 3: So that's kind of like a different cut out of

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Speaker 3: the data. And you simply repeat this process an infinite

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Speaker 3: number of times and it generates an algorithm that ranks

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Speaker 3: both the countries in terms of how complex they are

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Speaker 3: and the products in terms of how complex they are.

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Speaker 3: So essentially, it's an operation on this matrix if you

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Speaker 3: want that relates countries to the products that they make.

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Speaker 3: It is an eigenvector of that matrix. Essentially, if you

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Speaker 3: want to get fancy at the mouth, but it captures

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Speaker 3: this idea how many things are we able to do

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Speaker 3: and how complicated it is to do those things.

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Speaker 2: So is it possible to have a good economic outcome

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Speaker 2: with low complexity? Or I guess another way of saying

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Speaker 2: this is is complexity as synonym for development or wealth here?

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Speaker 2: Or capturing something different? Could you have a country that

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Speaker 2: scores low on complexity but is still a relatively good

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Speaker 2: place to live from an economic perspective.

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Speaker 3: So one major thing that economic complexity does not capture

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Speaker 3: is natural resource wealth. Okay, so when you make a graphs, say,

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Speaker 3: of the relationship between how complex you are are and

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Speaker 3: how rich you are, the outliers tend to be countries

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Speaker 3: that are natural resource rich. So they get their income

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Speaker 3: not so much by what they know how to do,

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Speaker 3: but from the natural resource wealth that they happen to have.

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Speaker 2: So a place like the United Arab Emirates or Saudi

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Speaker 2: Arabia perhaps might score low on complexity, but they have

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Speaker 2: a ton of oil wealth and so they're still quite

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Speaker 2: prosperous on a GDP per capita basis that sort of

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Speaker 2: thing exactly.

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Speaker 3: So, or you could say, tell me, controlling for their

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Speaker 3: natural resource wealth, they have a lot of complexity or not.

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Speaker 3: And let me tell you a few of the things

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Speaker 3: we have established. The first one is that the complexity

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Speaker 3: correlates very highly with how rich you are, and if

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Speaker 3: you control for your natural resource wealth, it correlates even better.

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Speaker 3: So there's a very strong relationship between economic complexity and

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Speaker 3: natural resource and your GDP per capita in level. So

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Speaker 3: that's a very strong relationship. But more importantly than that,

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Speaker 3: countries that are more complex than you would have expected

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Speaker 3: them to be given their income level tend to grow

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Speaker 3: faster in the future, and countries that have relatively low

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Speaker 3: complexity relatives to their income level tend to grow less

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Speaker 3: in the future. So your economic complexity relative to your

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Speaker 3: income level is a predictor of how fast you will

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Speaker 3: be able to grow. And typically when we look at

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Speaker 3: how good these predictions are, they tend to be best

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Speaker 3: at a horizon of about ten years, So it tells

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Speaker 3: you something about what your next decade is likely to

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Speaker 3: look like.

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Speaker 1: What's an example of maybe something in history in which

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Speaker 1: at some point a country scored significantly high complexity and

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Speaker 1: then did it over the next several years. It's like, yeah,

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Speaker 1: this country really boomed. What's an example that stands.

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Speaker 3: Out of this, Well, there are many examples, but suppose

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Speaker 3: if you have take the picture in two thousand and eight,

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Speaker 3: two outliers were India and Greece. India had extremely low

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Speaker 3: income levels for its level of complexity, and Greece had

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Speaker 3: extremely high income levels for their level of complexity. And

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Speaker 3: what happened is that India has been the fastest growing

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Speaker 3: large country in the world since then, and Greece collapsed.

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Speaker 3: So we also see countries that increase their complexity initially

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Speaker 3: by moving into a new set of products. In the

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Speaker 3: case of Thailand, they first moved into garments and spent

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Speaker 3: a whole decade adding a bunch of different garments to

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Speaker 3: their export baskets. Suddenly they got into electronics and then

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Speaker 3: started to add a bunch of electronics to their export basket,

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Speaker 3: and then they got into cars and machines and so on.

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Speaker 3: So they have been increasing their complexity and have very

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Speaker 3: sustained high growth rates in the process. In general, what

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Speaker 3: we find is that only about a fifth of the

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Speaker 3: countries that were poorer than the US, say, in nineteen

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Speaker 3: seventy have really caught up narrowed the gap with the US. Okay,

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Speaker 3: since nineteen seventy onwards, only twenty percent of countries narrowed

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Speaker 3: their income gap with the US. Those twenty percent of

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Speaker 3: countries that narrow their income gap with the US increase

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Speaker 3: their complexity very significantly. The other eighty percent did not.

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Speaker 3: So I would tell you that sustained growth implies this

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Speaker 3: process of absorbing knowledge, distributing in a your society, mobilizing

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Speaker 3: that knowledge to make more things and more complex things.

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Speaker 3: Because you get more complex. Things are essentially things that

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Speaker 3: require more knowledge, and things that require more knowledge require

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Speaker 3: deeper networks of humans collaborating, whether it's in a single

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Speaker 3: firm or in a longer value chain.

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Speaker 2: Can you talk a little bit more about how you

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Speaker 2: build complexity and diffuse that knowledge within a society, because

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Speaker 2: I am imagine if you're a developing country, maybe you

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Speaker 2: find that you have a competitive advantage in one type

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Speaker 2: of thing. I'm going to go back to the T

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Speaker 2: shirt example. You can make T shirts cheaper than anyone

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Speaker 2: else and more efficiently, I guess, And then I would

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Speaker 2: imagine the temptation is to just stick with that specialization

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Speaker 2: and just do the thing that you are currently really

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Speaker 2: good at. So how do countries actually break out of

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Speaker 2: that dynamic and start developing expertise in other areas.

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Speaker 3: There are essentially three mechanisms that I would like to mention.

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Speaker 3: The first one is that countries tend to move from

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Speaker 3: where they're good at to what I like to call

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Speaker 3: or Stuart Kaufman coin the phrase the adjacent possible. When

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Speaker 3: we look at countries adding products to their export basket,

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Speaker 3: those products tend to be cognitively near. If you want

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Speaker 3: the products that they were making before. And one of

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Speaker 3: the contributions we've made is we've developed a technique to

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Speaker 3: measure this cognitive proximity for all the products in the world.

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Speaker 3: And you can locate every country in the world and

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Speaker 3: find out what's in their adjacent possible So countries tend

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Speaker 3: to move from the things that they are currently good

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Speaker 3: at to things that are in their adjacent possible And

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Speaker 3: we call this cognitive map of the products of the world.

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Speaker 3: We call it the product space. And this product space

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Speaker 3: is very heterogeneous. There are some parts of the product

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Speaker 3: space there where you have products that are tightly connected

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Speaker 3: to each other. So if you know how to make

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Speaker 3: one kind of product is kind of like easy to move.

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Speaker 3: You have a rich adjacent possible. You have many ways

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Speaker 3: of reorganizing that knowledge to make other things. We like

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Speaker 3: to use the metaphor that products are like trees, and

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Speaker 3: firms are like monkeys. They live on trees, they exploit

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Speaker 3: certain trees. So the product space is like the map

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Speaker 3: of the forests. And so you can, by the way,

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Speaker 3: if you go to the Atlas and Economic complexity, we

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Speaker 3: have the we choose a country, we have the product space.

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Speaker 3: We will tell you where in that forest, does this

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Speaker 3: country have its monkeys, and then it can tell you

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Speaker 3: know which trees are close to those monkeys, and it

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Speaker 3: can tell you so you know, what are other characteristics

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Speaker 3: of those trees that might make it sexier or less

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Speaker 3: sexy to move in that direction. Okay, so the first

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Speaker 3: thing I want to say is that countries tend to

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Speaker 3: diversify by moving to their adjacent possible depending on where

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Speaker 3: they started, and not every country starts with the same

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Speaker 3: deck of cards. They don't start with their monkeys in

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Speaker 3: the same place. Some countries start with their monkeys in

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Speaker 3: very very promising parts of the product space because their

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Speaker 3: trees are very closely connected to each other there, so

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Speaker 3: it's easy for the monkeys to jump from tree to tree,

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Speaker 3: and other parts of the product space are very sparse

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Speaker 3: with their trees are very far from each other, so

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Speaker 3: it's hard for those monkeys to move. Okay, So that's

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Speaker 3: mechanism one, move towards the adjacent possible. Mechanism two is

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Speaker 3: that you have to solve this chicken and neck problem.

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Speaker 3: You don't know how to do the things you don't do,

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Speaker 3: but you need to know how to do things to

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Speaker 3: start doing things you are not doing before. So you

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Speaker 3: need watchmakers to make watches. But how do you become

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Speaker 3: a watchmaker in a country that doesn't make watches. How

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Speaker 3: do you solve this chicken and egg problem. We think

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Speaker 3: that this solving the chicken and eck problem is the

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Speaker 3: thing that forces countries into moving just to the adjacent

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Speaker 3: possible because it's hard for them to solve too many

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Speaker 3: of these chicken and egg problems at the same time.

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Speaker 3: But one way to accelerate the solution of these chicken

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Speaker 3: and eck problems is to bring watchmakers from outside your country.

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Speaker 3: That is, you may not have what makers in your country,

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Speaker 3: but maybe you start with a group of Swiss watchmakers

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Speaker 3: and they'll train the next generation of watchmakers and now

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Speaker 3: suddenly you do watchmaking right. So migration plays an outsized

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Speaker 3: role in diversification because you need to add knowledge that

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Speaker 3: was not in the system before. So if you can

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Speaker 3: attract people that had knowledge that was not in the

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Speaker 3: country and you can engage them having worked there and

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Speaker 3: have that knowledge spread, that seems to be very important.

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Speaker 3: There's a very nice story about Bangladesh here where you know,

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Speaker 3: if you look at Bangladesh in the ATHLETs of economic complexity,

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Speaker 3: you'll tell you that ninety percent of their exports are

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Speaker 3: garments and that they all started in the eighties. These

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Speaker 3: exports of garments, Well, what underpinned that was a company

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Speaker 3: which was called Desh and that company sent one hundred

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Speaker 3: and twenty six of its workers for six months training

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Speaker 3: program in Korea. Because the program that company was created

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Speaker 3: by Dai Wu. So these guys went to Korea. Yeah,

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Speaker 3: trained in Korea, came back, started the company and started

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Speaker 3: to produce. Fifty six of those people left the company

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Speaker 3: to create their own startups, and those fifty six children

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Speaker 3: of this company DSH are the core of the export

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Speaker 3: industry of garments in Bangladesh. So in some sense you

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Speaker 3: have to infect the system with knowledge and assure that

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Speaker 3: the mechanisms are going to allow that knowledge to spread.

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Speaker 1: Tracy prefaced the question. She talked about a country that

401
00:23:32,240 --> 00:23:34,200
Speaker 1: exports a lot of cheap T shirts and we sort

402
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Speaker 1: of think of a T shirts as being low value,

403
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Speaker 1: and you just mentioned the beginning of Bangladesh's process to

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Speaker 1: become wealthier and had a textile export company. But even

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Speaker 1: at least a T shirt, there's going to be some machinery,

406
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Speaker 1: there's going to be this something of a commodity supply

407
00:23:50,840 --> 00:23:54,200
Speaker 1: chain that has to be organized. There are certain engineering

408
00:23:54,280 --> 00:23:59,399
Speaker 1: aspects of it versus say, another country that may export

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Speaker 1: cocoa and coffee, in which I imagine that maybe it's

410
00:24:02,880 --> 00:24:05,720
Speaker 1: roughly the same level in terms of income, but strikes

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Speaker 1: me as a simpler process of selling coffee, beans or cocoa.

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Speaker 1: Are there certain goods like that consistently that, even though

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Speaker 1: they may seem rudimentary our early predictors of okay, at

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Speaker 1: least this country has some capacity to have monkeys jump

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Speaker 1: from tree to tree, so to speak.

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Speaker 3: You have given a fantastic example of what makes parts

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Speaker 3: of the product space denser and what makes parts of

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00:24:31,800 --> 00:24:36,760
Speaker 3: the product space sparser, because garments are in a dense

419
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Speaker 3: part of the product space. If you know how to

420
00:24:38,840 --> 00:24:41,200
Speaker 3: make one kind of garment, you can make very different

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00:24:41,280 --> 00:24:44,919
Speaker 3: kinds of garments. But in order to make garments and

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00:24:44,960 --> 00:24:48,720
Speaker 3: export them competitively, right, you need to have an industrial

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00:24:48,840 --> 00:24:53,359
Speaker 3: zone where materials can go in and out, where workers

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00:24:53,440 --> 00:24:57,760
Speaker 3: can go in and out, where there's power, where there's water,

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00:24:58,560 --> 00:25:01,919
Speaker 3: where there's a good logistic election to a port or

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00:25:01,920 --> 00:25:06,080
Speaker 3: to an airport, right where the custom service works more

427
00:25:06,160 --> 00:25:09,880
Speaker 3: or less well, and maybe has to do complex sophisticated

428
00:25:09,920 --> 00:25:13,520
Speaker 3: things like letting the textiles come in in bond without

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00:25:13,560 --> 00:25:17,400
Speaker 3: paying VAT and tariffs so that if they are going

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00:25:17,440 --> 00:25:21,080
Speaker 3: to be exported, so you save on these transaction costs.

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00:25:21,359 --> 00:25:26,719
Speaker 3: So getting government industry going is pretty complicated, and it

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Speaker 3: has taken fifteen years for Ethiopia to get into it,

433
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Speaker 3: and they're ballion. They had to build these industrial zones,

434
00:25:35,359 --> 00:25:38,200
Speaker 3: they had to provide electricity to these industrial zones. They

435
00:25:38,200 --> 00:25:41,840
Speaker 3: had to build a railway to Djibouti. An incredible number

436
00:25:41,880 --> 00:25:44,840
Speaker 3: of things that were not there that were part of

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00:25:44,960 --> 00:25:48,919
Speaker 3: so like the ecosystem that garments require. But once you

438
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Speaker 3: have that ecosystem, well in the same industrial zone, maybe

439
00:25:52,840 --> 00:25:56,359
Speaker 3: with the same port and the same electricity and the

440
00:25:56,359 --> 00:25:59,280
Speaker 3: same water and maybe even the same workers, you could

441
00:25:59,720 --> 00:26:03,600
Speaker 3: s electronics. Maybe you can do some auto parts. In

442
00:26:03,640 --> 00:26:07,280
Speaker 3: the end. What's the difference between a car seat and

443
00:26:07,480 --> 00:26:11,199
Speaker 3: another leather product, So you may start producing things for

444
00:26:11,280 --> 00:26:14,840
Speaker 3: the auto industry and so on. So these things, so

445
00:26:15,119 --> 00:26:19,200
Speaker 3: garments would have many neighbors. It's easier to move from

446
00:26:19,280 --> 00:26:22,359
Speaker 3: garments to other things then to move from co co

447
00:26:22,600 --> 00:26:25,880
Speaker 3: to other things. Because you know, if you make coffee, well,

448
00:26:25,920 --> 00:26:29,560
Speaker 3: coffee grows in the tropics between nine hundred meters above

449
00:26:29,560 --> 00:26:32,920
Speaker 3: sea level and thirteen hundred meters above sea level let's say,

450
00:26:32,960 --> 00:26:37,280
Speaker 3: between three thousand and four thousand feet, And it requires

451
00:26:37,280 --> 00:26:40,040
Speaker 3: a tree to provide shadow. And so if you suddenly say,

452
00:26:40,160 --> 00:26:42,639
Speaker 3: you know what, I'm not going to grow coffee anymore,

453
00:26:42,680 --> 00:26:45,439
Speaker 3: I'm going to do something else, well, what else are

454
00:26:45,480 --> 00:26:47,960
Speaker 3: you going to do between three thousand and four thousand

455
00:26:48,000 --> 00:26:52,960
Speaker 3: feet altitude? It's in a mountainous region. So it does

456
00:26:53,040 --> 00:26:58,400
Speaker 3: not make diversification from coffee into other things very easy.

457
00:26:58,680 --> 00:27:01,680
Speaker 3: But diversifying in an an industrial song from one kind

458
00:27:01,720 --> 00:27:05,520
Speaker 3: of manufacturer to another kind of manufacturer is much easier.

459
00:27:05,680 --> 00:27:07,480
Speaker 2: You know, I kind of enjoy it just hearing the

460
00:27:07,520 --> 00:27:10,800
Speaker 2: specific examples of how this works. Do you have a

461
00:27:10,960 --> 00:27:14,879
Speaker 2: favorite example of this sort of monkey's jumping from trees

462
00:27:15,160 --> 00:27:18,760
Speaker 2: dynamic or maybe even one going in reverse? Like, I'm

463
00:27:18,760 --> 00:27:21,640
Speaker 2: curious how that happens as well, how an economy would

464
00:27:21,680 --> 00:27:23,720
Speaker 2: maybe lose complexity over time.

465
00:27:24,480 --> 00:27:26,960
Speaker 3: Okay, so let me maybe give you an example of both.

466
00:27:27,480 --> 00:27:30,840
Speaker 3: You know, a lot to increase in complexity in Japan

467
00:27:30,920 --> 00:27:35,120
Speaker 3: and Korea did not happen because new companies were created

468
00:27:35,160 --> 00:27:39,639
Speaker 3: to do more things, but because established companies, these chiballs

469
00:27:39,640 --> 00:27:43,200
Speaker 3: in Korea, these kde etsus in Japan, diverse to fight

470
00:27:43,280 --> 00:27:47,480
Speaker 3: internally into more things. So a company like Samson started

471
00:27:47,600 --> 00:27:50,840
Speaker 3: in sugar trading, and you know now they are the

472
00:27:50,960 --> 00:27:56,080
Speaker 3: largest producer of semiconductors and s grams and TV screens

473
00:27:56,160 --> 00:28:00,720
Speaker 3: and smartphones. That process of transformation happened inside the company,

474
00:28:01,119 --> 00:28:04,840
Speaker 3: and it happened by adding capabilities to their capabilities. So

475
00:28:04,880 --> 00:28:07,960
Speaker 3: for example, you'd say Finland is a country that had

476
00:28:08,000 --> 00:28:11,840
Speaker 3: a lot of trees, and traditional development economists would have said,

477
00:28:12,160 --> 00:28:14,560
Speaker 3: cut those trees and sell wood. And then they would say, no,

478
00:28:14,720 --> 00:28:17,879
Speaker 3: don't don't sell wood. Make furniture with that wood, or

479
00:28:17,920 --> 00:28:21,160
Speaker 3: make paper with that wood. Add value to your raw materials.

480
00:28:21,720 --> 00:28:24,680
Speaker 3: But that's not where the story really went. It's sort

481
00:28:24,680 --> 00:28:26,439
Speaker 3: of like Finland had a lot of trees, so they

482
00:28:26,440 --> 00:28:28,320
Speaker 3: have to cut the trees. But to cut the trees,

483
00:28:28,359 --> 00:28:31,399
Speaker 3: you need tools to cut trees. You need machines to

484
00:28:31,440 --> 00:28:34,840
Speaker 3: cut trees. So they became good at tools and machines

485
00:28:34,880 --> 00:28:38,360
Speaker 3: that cut wood, and from there they moved to tools

486
00:28:38,360 --> 00:28:41,000
Speaker 3: and machines that cut because not everything is made out

487
00:28:41,040 --> 00:28:43,120
Speaker 3: of wood, and from there they made they went to

488
00:28:43,200 --> 00:28:47,960
Speaker 3: automated machines that cut, because cutting everything by hand can

489
00:28:48,000 --> 00:28:51,160
Speaker 3: be either boring or imprecise. And then they said, you know,

490
00:28:51,280 --> 00:28:54,600
Speaker 3: from automated machines that cut, they want to just automated machines.

491
00:28:54,640 --> 00:28:56,400
Speaker 3: Why do we need to cut? There's more to life

492
00:28:56,400 --> 00:29:00,920
Speaker 3: than just cutting, right, And then from autumnate machines they

493
00:29:01,040 --> 00:29:05,600
Speaker 3: ended up in Nokia. So the process is a process

494
00:29:05,600 --> 00:29:09,320
Speaker 3: of adding capabilities to your capabilities, because once you know

495
00:29:09,400 --> 00:29:12,800
Speaker 3: how to do something, there is something in that cognitive

496
00:29:12,880 --> 00:29:16,800
Speaker 3: vicinity that you could do. Now. An interesting example of

497
00:29:16,920 --> 00:29:19,920
Speaker 3: going backwards is let me give you the example of

498
00:29:19,960 --> 00:29:24,320
Speaker 3: South Africa. South Africa is a country that has a

499
00:29:24,360 --> 00:29:29,520
Speaker 3: lot of coal mineral resources, and they knew how to

500
00:29:29,560 --> 00:29:34,560
Speaker 3: transform that coal into cheap electricity, and that cheap electricity

501
00:29:35,160 --> 00:29:39,320
Speaker 3: made them very competitive in mining and in metal processing

502
00:29:40,360 --> 00:29:45,600
Speaker 3: and in relatively energy intensive manufacturing. Now they messed up

503
00:29:45,640 --> 00:29:49,840
Speaker 3: their electricity company. Their electricity company lost the capacity to

504
00:29:49,920 --> 00:29:56,400
Speaker 3: sell cheap electricity, and now they not only have expensive electricity,

505
00:29:56,440 --> 00:30:00,120
Speaker 3: they have very lousy electricity with a lot of blackout

506
00:30:00,560 --> 00:30:04,440
Speaker 3: and something called load shedding, so like plan shutdowns, and

507
00:30:04,560 --> 00:30:09,560
Speaker 3: that has made manufacturing activity very very complicated, and that

508
00:30:09,640 --> 00:30:13,080
Speaker 3: has caused them to lose a lot of complexity, so

509
00:30:13,520 --> 00:30:17,120
Speaker 3: in say nineteen ninety, they have the same complexity as China,

510
00:30:17,720 --> 00:30:21,960
Speaker 3: and now China has increased its complex complexity dramatically and

511
00:30:22,360 --> 00:30:24,520
Speaker 3: South Africa has gone in the opposite record.

512
00:30:41,280 --> 00:30:45,680
Speaker 2: Since you mentioned the Japanese and Korean conglomerates just then,

513
00:30:46,280 --> 00:30:47,840
Speaker 2: that reminds me of something I want to ask you,

514
00:30:47,880 --> 00:30:50,800
Speaker 2: which is, is there a point at which there can

515
00:30:50,840 --> 00:30:54,680
Speaker 2: be too much complexity? I'm imagining, for instance, a big

516
00:30:54,680 --> 00:30:59,160
Speaker 2: company and suddenly they have their fingers in a thousand

517
00:30:59,240 --> 00:31:02,600
Speaker 2: different parts, and maybe they're okay at doing a bunch

518
00:31:02,640 --> 00:31:06,000
Speaker 2: of those things, but maybe they're not particularly good at it,

519
00:31:06,040 --> 00:31:09,520
Speaker 2: and it becomes inefficient and the sort of like lumbering

520
00:31:09,680 --> 00:31:13,920
Speaker 2: everything everywhere, all at once. Entity is that a concern

521
00:31:13,960 --> 00:31:16,360
Speaker 2: at all, either on a corporate level or on an

522
00:31:16,440 --> 00:31:17,560
Speaker 2: economy wide level.

523
00:31:18,760 --> 00:31:20,920
Speaker 3: I think it's more on a corporate level than on

524
00:31:20,920 --> 00:31:24,360
Speaker 3: an economy white level. I would say, if you're a company,

525
00:31:24,520 --> 00:31:28,480
Speaker 3: maybe you realize that there's this adjacency that you could exploit,

526
00:31:29,040 --> 00:31:31,720
Speaker 3: and maybe you start exploiting that adjacency, But then you

527
00:31:31,800 --> 00:31:35,960
Speaker 3: realize that managing the two organizations might be too complex,

528
00:31:36,320 --> 00:31:39,920
Speaker 3: so you spin it off, and spin offs sell part

529
00:31:40,000 --> 00:31:44,280
Speaker 3: so that you keep a coherent entity that's easier to manage.

530
00:31:44,600 --> 00:31:46,840
Speaker 3: That's great, But if you spun it off, it means

531
00:31:46,840 --> 00:31:49,240
Speaker 3: that somebody else bought it, and somebody else is using

532
00:31:49,280 --> 00:31:52,880
Speaker 3: that knowledge to produce those things. So I think it's

533
00:31:52,960 --> 00:31:56,120
Speaker 3: a concern for firms. How do you keep your coherence?

534
00:31:56,480 --> 00:31:59,959
Speaker 3: I would say, still explore. I think there's a lot

535
00:32:00,240 --> 00:32:04,520
Speaker 3: of value in exploring your adjacency, developing that adjacency, and

536
00:32:04,560 --> 00:32:07,280
Speaker 3: maybe spinning it off later on, and it will add

537
00:32:07,320 --> 00:32:10,360
Speaker 3: to the value of the company. At the societal level,

538
00:32:10,480 --> 00:32:13,840
Speaker 3: I don't see any evidence of that. I see societies

539
00:32:13,840 --> 00:32:18,080
Speaker 3: that are relatively small and are amazingly complex. I'll give

540
00:32:18,080 --> 00:32:20,840
Speaker 3: you the example of Slovenia. Who would have thought a

541
00:32:20,880 --> 00:32:24,719
Speaker 3: country of two million people, they export thirty five billion

542
00:32:24,800 --> 00:32:28,840
Speaker 3: dollars or more and an incredibly large diversity of things.

543
00:32:29,160 --> 00:32:32,760
Speaker 3: They're super plugged in to value chains in Austria, value

544
00:32:32,800 --> 00:32:37,920
Speaker 3: chains in Germany. In they do pretty sophisticated stuff and

545
00:32:38,320 --> 00:32:42,520
Speaker 3: with only two million people. So I don't think there's

546
00:32:42,680 --> 00:32:46,440
Speaker 3: too much limit to the growth of complexity because there

547
00:32:46,480 --> 00:32:49,680
Speaker 3: isn't that much limit to the growth of knowledge in

548
00:32:49,720 --> 00:32:52,360
Speaker 3: a society, and you don't have to be big, to

549
00:32:52,440 --> 00:32:55,120
Speaker 3: be very knowledgeable as a society.

550
00:32:55,360 --> 00:32:57,640
Speaker 1: I have so many questions. I loved your sort of

551
00:32:57,760 --> 00:33:01,600
Speaker 1: like brief industrial history of Finland. And it's like, you

552
00:33:01,680 --> 00:33:04,560
Speaker 1: go from exporting wood, then you have tools that cut wood,

553
00:33:04,560 --> 00:33:06,320
Speaker 1: then you have tools that cut, and then you have

554
00:33:06,400 --> 00:33:08,120
Speaker 1: things that do things that don't just cut, and then

555
00:33:08,120 --> 00:33:09,720
Speaker 1: you have no suddenly of no can it makes a

556
00:33:09,760 --> 00:33:13,760
Speaker 1: lot of sense you describe it. I'm curious. Generally speaking,

557
00:33:13,880 --> 00:33:18,120
Speaker 1: we've seen countries lately who are major exporters of raw

558
00:33:18,160 --> 00:33:22,200
Speaker 1: commodities attempt to move up the value chaine, so to speak,

559
00:33:22,640 --> 00:33:27,960
Speaker 1: by insisting that, say, a mining company in Indonesia can't

560
00:33:28,000 --> 00:33:30,000
Speaker 1: just come and take the nickel and sell it, that

561
00:33:30,040 --> 00:33:32,600
Speaker 1: they need to set up some sort of domestic refining

562
00:33:32,760 --> 00:33:36,440
Speaker 1: operation in Indonesia, so that something more complex than just

563
00:33:36,480 --> 00:33:40,520
Speaker 1: selling the nickel. What is your history teach us about

564
00:33:40,840 --> 00:33:43,520
Speaker 1: countries that have done a better job or not of

565
00:33:43,560 --> 00:33:47,080
Speaker 1: getting out of, say the so called resource curse. Are

566
00:33:47,080 --> 00:33:50,680
Speaker 1: there certain strategies that work better than others in terms

567
00:33:50,680 --> 00:33:55,440
Speaker 1: of a country not just being dependent on a single

568
00:33:55,640 --> 00:33:58,360
Speaker 1: commodity that does not have many adjacencies.

569
00:34:00,640 --> 00:34:05,000
Speaker 3: So let me say that one of the most passtrating

570
00:34:05,120 --> 00:34:08,160
Speaker 3: ideas in the field of economic development is the idea

571
00:34:08,719 --> 00:34:11,440
Speaker 3: that you should focus on adding value to your raw

572
00:34:11,480 --> 00:34:15,520
Speaker 3: materials because there's so much more you can do. Then

573
00:34:15,600 --> 00:34:18,319
Speaker 3: the things that can be done by relying only on

574
00:34:18,360 --> 00:34:22,520
Speaker 3: the raw materials that you happen to have. Your opportunity

575
00:34:22,560 --> 00:34:27,160
Speaker 3: set is much much wider than that. So, suppose you

576
00:34:27,200 --> 00:34:30,440
Speaker 3: have nickel, It may make a lot of sense to

577
00:34:30,560 --> 00:34:35,160
Speaker 3: process the nickel locally because when your mind something, you know,

578
00:34:35,320 --> 00:34:37,560
Speaker 3: if it's a good mind, it might have two percent

579
00:34:37,640 --> 00:34:41,000
Speaker 3: nickel or three percent nickel. So you want to separate

580
00:34:41,440 --> 00:34:44,799
Speaker 3: ninety eight ninety seven percent stuff so you don't have

581
00:34:44,880 --> 00:34:48,000
Speaker 3: to transport that much stuff that is worthless, right, so

582
00:34:48,080 --> 00:34:50,840
Speaker 3: you want to do the refining and some of the

583
00:34:50,880 --> 00:34:55,080
Speaker 3: processing nearby, just to save on transportation costs. But if

584
00:34:55,080 --> 00:34:59,120
Speaker 3: you're going to do a lithium ion battery, well you

585
00:34:59,200 --> 00:35:01,280
Speaker 3: might have the nickel, but you don't have the lithium,

586
00:35:01,400 --> 00:35:04,719
Speaker 3: you don't have the chromium, you don't have the other

587
00:35:04,840 --> 00:35:07,960
Speaker 3: minerals that go into it. So you will have some

588
00:35:08,120 --> 00:35:11,160
Speaker 3: of them, but you will have to import the other ones. Now,

589
00:35:11,280 --> 00:35:14,879
Speaker 3: think if you're trying to make a cell phone, Well,

590
00:35:14,920 --> 00:35:16,960
Speaker 3: what is the raw material that you have to have

591
00:35:17,080 --> 00:35:20,919
Speaker 3: locally that will make the cell phone, well, I mean

592
00:35:21,680 --> 00:35:25,360
Speaker 3: too many none. So if you are going to be

593
00:35:25,480 --> 00:35:27,759
Speaker 3: making cell phones, it's because you are going to be

594
00:35:27,800 --> 00:35:30,880
Speaker 3: able to connect to a bunch of value chains between

595
00:35:30,920 --> 00:35:34,960
Speaker 3: the people who are able to make the memory and

596
00:35:35,080 --> 00:35:40,239
Speaker 3: the processors and the screen and the touch screen, you know,

597
00:35:40,320 --> 00:35:44,120
Speaker 3: the surface that can detect where your finger is and

598
00:35:44,200 --> 00:35:48,239
Speaker 3: all these different parts. So that that doesn't happen in

599
00:35:48,280 --> 00:35:51,160
Speaker 3: a single company, that happens in a bunch of many companies.

600
00:35:51,400 --> 00:35:53,359
Speaker 3: So if you want to get into that kind of thing,

601
00:35:53,400 --> 00:35:57,319
Speaker 3: which might be possible. So say you are in Legos, Nigeria. Well,

602
00:35:57,800 --> 00:36:00,799
Speaker 3: Legos is a port city, so anything you need you

603
00:36:00,840 --> 00:36:03,040
Speaker 3: can bring into the port. You don't have to have

604
00:36:03,080 --> 00:36:05,600
Speaker 3: that raw material in your country. So in general, I

605
00:36:05,600 --> 00:36:09,000
Speaker 3: would say, if you have raw materials, maximize the value

606
00:36:09,000 --> 00:36:12,080
Speaker 3: of your raw materials. But most of the things that

607
00:36:12,120 --> 00:36:15,160
Speaker 3: you could do next may have nothing to do with

608
00:36:15,320 --> 00:36:18,960
Speaker 3: processing those raw materials. And the best example here is Dubai.

609
00:36:19,520 --> 00:36:23,000
Speaker 3: Dubai long many moons ago had oil, it no longer

610
00:36:23,040 --> 00:36:26,080
Speaker 3: has oil. Abu Dhabi has oil, but Dubai doesn't have

611
00:36:26,160 --> 00:36:29,560
Speaker 3: oil anymore. But Dubai has an airport that is a

612
00:36:29,560 --> 00:36:34,320
Speaker 3: major hub. It has Emmerate Airlines, which is a major airline.

613
00:36:34,480 --> 00:36:37,600
Speaker 3: It has Dubai Ports, which is a network of global ports.

614
00:36:37,600 --> 00:36:40,400
Speaker 3: It has a lot of logistics. It has the regional

615
00:36:40,400 --> 00:36:45,440
Speaker 3: headquarters of multinational corporations, It has universities where people go

616
00:36:45,560 --> 00:36:48,160
Speaker 3: to study there, et cetera. So they have added a

617
00:36:48,160 --> 00:36:51,160
Speaker 3: lot of stuff to their If you want export basket

618
00:36:51,760 --> 00:36:56,400
Speaker 3: that is super distantly related to oil, they would probably

619
00:36:56,440 --> 00:36:59,200
Speaker 3: not have gotten there had they not have oil. That

620
00:36:59,280 --> 00:37:02,760
Speaker 3: allowed them to build that infrastructure, that to build the amenities,

621
00:37:02,960 --> 00:37:06,520
Speaker 3: to build the things that are attracted and the other activities.

622
00:37:06,920 --> 00:37:10,399
Speaker 3: But they are not about oil refinding, they're not about plastics,

623
00:37:10,520 --> 00:37:11,719
Speaker 3: they're not in the value chain.

624
00:37:11,760 --> 00:37:17,800
Speaker 2: Avoid the way you described the way that diversification or

625
00:37:17,840 --> 00:37:21,640
Speaker 2: development works, this idea of monkeys jumping from tree to tree.

626
00:37:22,040 --> 00:37:26,960
Speaker 2: It sounds very naturalistic, like a natural progression of expertise.

627
00:37:27,000 --> 00:37:31,520
Speaker 2: But I'm curious what role you think government policy could

628
00:37:31,640 --> 00:37:35,799
Speaker 2: play in that process, particularly in the context of what

629
00:37:35,840 --> 00:37:38,440
Speaker 2: we see nowadays, which really seems to be a resurgence

630
00:37:38,560 --> 00:37:41,480
Speaker 2: in some parts of the world in industrial policy that

631
00:37:41,719 --> 00:37:46,360
Speaker 2: is aimed at developing specific new types of technology or capabilities.

632
00:37:47,600 --> 00:37:50,560
Speaker 3: Well, definitely, I think that the government has a lot

633
00:37:50,600 --> 00:37:53,600
Speaker 3: of useful things that it can do. First of all,

634
00:37:54,239 --> 00:38:01,960
Speaker 3: every technology, every industry lives in a environment of relatively

635
00:38:02,040 --> 00:38:06,359
Speaker 3: specific public goods that the government needs to provide. So,

636
00:38:06,480 --> 00:38:10,719
Speaker 3: for example, suppose the society adopts the car as a technology,

637
00:38:11,480 --> 00:38:17,640
Speaker 3: and for transportation, well, cars need roads, Cars need traffic rules,

638
00:38:18,200 --> 00:38:22,520
Speaker 3: Cars need traffic lights and traffic signs. They need traffic

639
00:38:22,600 --> 00:38:28,000
Speaker 3: cops to enforce those rules. So the car technology lives

640
00:38:28,000 --> 00:38:32,080
Speaker 3: in an environment of public goods that make that car useful.

641
00:38:32,120 --> 00:38:34,960
Speaker 3: A car with no roads it would be useless. A

642
00:38:35,040 --> 00:38:39,080
Speaker 3: car in roads with no rules and no traffic signs

643
00:38:39,080 --> 00:38:42,959
Speaker 3: and so on may be too dangerous. So that technology

644
00:38:43,000 --> 00:38:47,960
Speaker 3: lives in an environment of public goods. And typically governments

645
00:38:47,960 --> 00:38:51,520
Speaker 3: are pretty lousy at producing the public goods that are

646
00:38:51,560 --> 00:38:55,920
Speaker 3: needed by the industries that exist. They are typically hopeless

647
00:38:56,320 --> 00:38:59,279
Speaker 3: in producing the public goods of the industries that don't

648
00:38:59,360 --> 00:39:03,240
Speaker 3: yet exist. So if you want that industry to exist,

649
00:39:03,840 --> 00:39:06,040
Speaker 3: you need to make sure that the public goods that

650
00:39:06,040 --> 00:39:10,520
Speaker 3: that industry will require are provided. So, for example, it's

651
00:39:10,680 --> 00:39:15,719
Speaker 3: going to be extremely difficult to sell electric vehicles in

652
00:39:15,760 --> 00:39:19,439
Speaker 3: a society that cannot assure people that they are going

653
00:39:19,520 --> 00:39:23,520
Speaker 3: to be charging stations, but nobody is going to build

654
00:39:23,600 --> 00:39:27,960
Speaker 3: charging stations for a market of electric vehicles that does

655
00:39:28,000 --> 00:39:34,440
Speaker 3: not yet exist. So these things can be addressed through policy,

656
00:39:34,520 --> 00:39:38,560
Speaker 3: these chicken and egg problems, these coordination problems, this provision

657
00:39:38,600 --> 00:39:41,920
Speaker 3: of public goods that industries are going to need. For example,

658
00:39:41,960 --> 00:39:48,040
Speaker 3: suppose that you want to export a fresh blueberries the

659
00:39:48,080 --> 00:39:51,880
Speaker 3: way Peru does, Okay, and they're the major exporter of

660
00:39:51,920 --> 00:39:54,719
Speaker 3: blueberries these days. An industry that started in Chile and

661
00:39:54,760 --> 00:39:57,560
Speaker 3: then moved to Argentina and now it's in Peru. Well,

662
00:39:57,600 --> 00:40:01,279
Speaker 3: you cannot export fresh produce if you do not have

663
00:40:02,000 --> 00:40:05,080
Speaker 3: a green lane in customs, if you don't have a

664
00:40:05,320 --> 00:40:08,919
Speaker 3: cold storage transportation chain what they call a cold chain,

665
00:40:09,640 --> 00:40:13,600
Speaker 3: if you don't have fit to sanitary agreements with the market,

666
00:40:13,719 --> 00:40:17,319
Speaker 3: you're going to be selling this stuff too. So that

667
00:40:17,440 --> 00:40:20,040
Speaker 3: industry is only going to exist in the context of

668
00:40:20,080 --> 00:40:24,480
Speaker 3: these public goods that make that industry feasible. So I

669
00:40:24,520 --> 00:40:27,840
Speaker 3: think governments have to engage in the nitty gritty of

670
00:40:27,880 --> 00:40:32,160
Speaker 3: the public goods that new industries need, and they'll have

671
00:40:32,200 --> 00:40:35,799
Speaker 3: to get engaged in the nitty gritty of chicken and

672
00:40:35,840 --> 00:40:39,319
Speaker 3: egg problems. Even within the private sector that could like

673
00:40:39,520 --> 00:40:41,920
Speaker 3: the example I gave you of the charging stations and

674
00:40:41,960 --> 00:40:46,480
Speaker 3: the eeds so that markets are able to develop. So

675
00:40:46,600 --> 00:40:50,399
Speaker 3: I do think that there's an important contributing role that

676
00:40:50,600 --> 00:40:53,680
Speaker 3: industrial policies can play to facilitate monkeys moving.

677
00:40:54,560 --> 00:40:57,200
Speaker 2: I have just one more question, which is a very

678
00:40:57,200 --> 00:41:00,440
Speaker 2: important one. How good are you at trade all the

679
00:41:00,440 --> 00:41:02,480
Speaker 2: fact that you know that Peru is one of the

680
00:41:02,520 --> 00:41:04,800
Speaker 2: biggest exporters of blueberries nowadays?

681
00:41:04,880 --> 00:41:07,879
Speaker 1: Is it just super nice history of which countries where

682
00:41:07,960 --> 00:41:09,560
Speaker 1: the blueberry exporters in the past.

683
00:41:11,480 --> 00:41:15,640
Speaker 3: Well, I mean it's a bit unfair. This is my

684
00:41:15,760 --> 00:41:18,880
Speaker 3: day job, this is what I do, so it's not

685
00:41:18,920 --> 00:41:21,000
Speaker 3: on my hobby. So this is what I think about

686
00:41:21,040 --> 00:41:21,440
Speaker 3: all day.

687
00:41:22,000 --> 00:41:25,279
Speaker 1: We talk a lot about and trade again, goods exports,

688
00:41:25,320 --> 00:41:27,280
Speaker 1: and there's a really good reason to look at exports

689
00:41:27,320 --> 00:41:29,759
Speaker 1: because there's that sort of like discipline of like you

690
00:41:29,800 --> 00:41:32,520
Speaker 1: can't force another country to buy your goods, and so

691
00:41:32,800 --> 00:41:35,720
Speaker 1: like looking at goods exports is really interesting. I'm curious

692
00:41:35,760 --> 00:41:39,719
Speaker 1: about work you've done of looking at services through the

693
00:41:39,719 --> 00:41:43,759
Speaker 1: complexity lens and can countries rise up and become rich

694
00:41:44,120 --> 00:41:47,040
Speaker 1: if they never go through the manufacturing process, because as

695
00:41:47,080 --> 00:41:49,879
Speaker 1: you talk about, you know, manufacturing links all different kinds

696
00:41:49,880 --> 00:41:54,120
Speaker 1: of things supply chains, ports, electricity systems, cutting, and various

697
00:41:54,120 --> 00:41:56,840
Speaker 1: things like that. Can it be done through the services.

698
00:41:56,480 --> 00:41:58,920
Speaker 3: Room, I think? So let me give you the example

699
00:41:59,000 --> 00:42:02,480
Speaker 3: of Panama. Panama had a canal and the canal was

700
00:42:02,560 --> 00:42:05,120
Speaker 3: run by the Americans, and the Americans just wanted the

701
00:42:05,120 --> 00:42:07,840
Speaker 3: ships to go through. So when the Panama can became

702
00:42:07,920 --> 00:42:14,320
Speaker 3: Panamanian in nineteen ninety seven, so they started to think, okay,

703
00:42:14,360 --> 00:42:16,160
Speaker 3: what can we do with the canal. Well, we want

704
00:42:16,239 --> 00:42:18,800
Speaker 3: the ships to not just go through, but to stop,

705
00:42:18,800 --> 00:42:22,239
Speaker 3: So let's build some ports. Maybe, let's do some logistics,

706
00:42:22,280 --> 00:42:25,920
Speaker 3: some transshipment. They said, well, what do these people need?

707
00:42:25,960 --> 00:42:29,840
Speaker 3: They need a financial services, so why don't we create

708
00:42:29,880 --> 00:42:33,600
Speaker 3: an offshore financial center. And then they decided, you know what,

709
00:42:33,800 --> 00:42:38,240
Speaker 3: why don't we become a hub for regional multinational headquarters.

710
00:42:38,400 --> 00:42:42,840
Speaker 3: And they happened to stumble into having a very successful airline,

711
00:42:42,920 --> 00:42:45,760
Speaker 3: Copa Airline. It's the more successful company in the region.

712
00:42:46,160 --> 00:42:50,560
Speaker 3: So that made having regional headquarters and multinational corporations very

713
00:42:50,600 --> 00:42:53,760
Speaker 3: practical because from Panama City you can go to anywhere

714
00:42:53,760 --> 00:42:55,880
Speaker 3: in Latin America and the US and a bunch of

715
00:42:55,920 --> 00:43:00,520
Speaker 3: other destinations. So suddenly you have a bunch of people.

716
00:43:00,760 --> 00:43:04,480
Speaker 3: They have some forty thousand people who work at multinational

717
00:43:04,520 --> 00:43:08,640
Speaker 3: corporations under special visas that work in Panama, and they

718
00:43:08,680 --> 00:43:14,239
Speaker 3: want to have amenities, restaurants and museums, cultural activities, good schools,

719
00:43:14,320 --> 00:43:18,680
Speaker 3: good healthcare. So guess what. You become a good destination

720
00:43:18,800 --> 00:43:21,560
Speaker 3: to attract other people and other talent, and you become

721
00:43:21,600 --> 00:43:24,600
Speaker 3: a good tourist destination. So in the example I've just

722
00:43:24,640 --> 00:43:28,160
Speaker 3: given you, it's a bunch of service industries that are

723
00:43:28,200 --> 00:43:31,000
Speaker 3: connected to each other. And by the way, Panama is

724
00:43:31,000 --> 00:43:33,480
Speaker 3: the country in Latin America that has had this has

725
00:43:33,520 --> 00:43:35,280
Speaker 3: dispost over the last thirty years.

726
00:43:35,520 --> 00:43:39,200
Speaker 1: I realized I have one more really important question that

727
00:43:39,280 --> 00:43:41,680
Speaker 1: we can't leave a I'm curious, like you have new

728
00:43:41,719 --> 00:43:44,279
Speaker 1: research out, So is there anything that jumps out at

729
00:43:44,280 --> 00:43:46,080
Speaker 1: you right now in terms of which countries are on

730
00:43:46,160 --> 00:43:48,680
Speaker 1: the move, what is the big picture trends in her

731
00:43:49,040 --> 00:43:52,160
Speaker 1: who's moving? And like what is happening here in the

732
00:43:52,239 --> 00:43:55,080
Speaker 1: richest country in the world, or I think are pretty

733
00:43:55,080 --> 00:43:59,080
Speaker 1: close to it in terms of trends in our own complexity.

734
00:43:59,080 --> 00:44:03,440
Speaker 3: Here. First of all, let me invite your listeners to

735
00:44:03,520 --> 00:44:07,040
Speaker 3: all visit the aplus of economic complexity we have just

736
00:44:07,160 --> 00:44:09,920
Speaker 3: updated it with twenty twenty one data and we run

737
00:44:10,120 --> 00:44:14,680
Speaker 3: growth projections for the following decade, and there you'll find

738
00:44:14,719 --> 00:44:20,120
Speaker 3: that countries like China, Vietnam, Uganda, India, we expect to

739
00:44:20,160 --> 00:44:22,799
Speaker 3: be growing a lot. The US has had in the

740
00:44:22,840 --> 00:44:26,200
Speaker 3: past a very significant decline in its complexity, and you

741
00:44:26,239 --> 00:44:29,200
Speaker 3: see it a little bit in how reliant the US

742
00:44:29,360 --> 00:44:32,959
Speaker 3: is on value chains outside the US, even for sophisticated

743
00:44:33,040 --> 00:44:37,480
Speaker 3: products like semiconductors and stuff. So in our current research,

744
00:44:37,560 --> 00:44:42,000
Speaker 3: we're also exploring a major change that is coming that

745
00:44:42,040 --> 00:44:45,160
Speaker 3: we know is coming, it's in the process, it's already happening,

746
00:44:45,520 --> 00:44:51,000
Speaker 3: which is this decarbonization process. What is de carbonization going

747
00:44:51,080 --> 00:44:54,360
Speaker 3: to do to the world, to global economy. Obviously, countries

748
00:44:54,400 --> 00:44:57,520
Speaker 3: that export oil and natural gas and coal are going

749
00:44:57,520 --> 00:45:00,439
Speaker 3: to face headwinds, But countries are going to to need

750
00:45:00,680 --> 00:45:06,080
Speaker 3: solar panels and windmills and fertilizers that are green and electoralizers,

751
00:45:06,120 --> 00:45:08,680
Speaker 3: and so there's a lot of stuff that will be growing.

752
00:45:09,400 --> 00:45:13,080
Speaker 3: So the structure of global demand will be shifting, and

753
00:45:13,160 --> 00:45:15,719
Speaker 3: we're trying to exploit ways in which we can help

754
00:45:15,760 --> 00:45:19,720
Speaker 3: countries figure out how they can grow in a world

755
00:45:19,719 --> 00:45:23,000
Speaker 3: that is attempting to decarbonize, and that is a very

756
00:45:23,040 --> 00:45:25,680
Speaker 3: different frame from the current frame. The current frame is

757
00:45:26,040 --> 00:45:29,360
Speaker 3: countries are being asked by the Paris Agreement, tell me

758
00:45:29,400 --> 00:45:34,520
Speaker 3: what are your commitments to lower your emissions. In our framework,

759
00:45:34,680 --> 00:45:38,240
Speaker 3: we are asking countries, look, the world wants to decarbonize.

760
00:45:38,360 --> 00:45:42,279
Speaker 3: What can your country do to enable the rest of

761
00:45:42,320 --> 00:45:45,120
Speaker 3: the world to buy the things that they will need

762
00:45:45,160 --> 00:45:49,480
Speaker 3: to decarbonize. Those are going to be your export industries.

763
00:45:49,560 --> 00:45:52,360
Speaker 3: Those are going to be the large, fast growing products

764
00:45:52,360 --> 00:45:55,160
Speaker 3: of the future. How can you get into them? And

765
00:45:55,239 --> 00:45:58,040
Speaker 3: we are putting that in the context of our product

766
00:45:58,040 --> 00:46:00,360
Speaker 3: space and our methods, et cetera, to figure out to

767
00:46:00,400 --> 00:46:04,560
Speaker 3: help countries figure out paths to growth that will help

768
00:46:04,600 --> 00:46:05,040
Speaker 3: the world of.

769
00:46:05,040 --> 00:46:09,040
Speaker 1: Carbonis Ricardo Houseman. This was such a great conversation. Can

770
00:46:09,080 --> 00:46:11,319
Speaker 1: we do a live episode with you at some point

771
00:46:11,320 --> 00:46:14,680
Speaker 1: where people just throw goods and countries at you and

772
00:46:14,719 --> 00:46:17,200
Speaker 1: we on stage and you just sort of tell a

773
00:46:17,200 --> 00:46:20,640
Speaker 1: little bit of competition. Can we can we do that

774
00:46:20,680 --> 00:46:22,080
Speaker 1: at some point in the future, will come to you

775
00:46:22,120 --> 00:46:23,680
Speaker 1: wherever you are and make it happen. I think it

776
00:46:23,680 --> 00:46:24,799
Speaker 1: would be really a lot of fun.

777
00:46:25,840 --> 00:46:27,360
Speaker 3: I would definitely have fun.

778
00:46:27,480 --> 00:46:30,280
Speaker 1: Okay, I'm so. I actually had tons of more questions

779
00:46:30,360 --> 00:46:34,240
Speaker 1: like how random like little islands become like helicopter export hubs.

780
00:46:34,280 --> 00:46:37,440
Speaker 1: But this was so great. Really appreciate you coming on.

781
00:46:37,640 --> 00:46:40,680
Speaker 1: Fascinating conversation. Thank you so much for coming on outline.

782
00:46:41,160 --> 00:46:42,399
Speaker 3: Thank you, thank you, thank you.

783
00:46:42,360 --> 00:46:57,480
Speaker 1: For having me, Tracy. I really want to do that

784
00:46:57,520 --> 00:46:59,600
Speaker 1: where we get Ricardo out of stage and someone goes,

785
00:46:59,600 --> 00:47:02,360
Speaker 1: like men's suits, and then he tell us the history

786
00:47:02,400 --> 00:47:04,680
Speaker 1: of like which country is sell the most men's suits,

787
00:47:04,719 --> 00:47:06,760
Speaker 1: so what they used to sell, and why one country

788
00:47:07,000 --> 00:47:12,080
Speaker 1: stopped because they started producing some get the soccer cleats

789
00:47:12,360 --> 00:47:14,480
Speaker 1: whatever it is, and which are whatever it is. I

790
00:47:14,480 --> 00:47:15,680
Speaker 1: think that would be really fun.

791
00:47:15,680 --> 00:47:20,120
Speaker 2: The evolution of those manufacturing and lunch capabilities. I will

792
00:47:20,120 --> 00:47:22,399
Speaker 2: say that, I think for the rest of my days,

793
00:47:22,440 --> 00:47:25,160
Speaker 2: whenever I think of economic development and diversification, I'm going

794
00:47:25,239 --> 00:47:29,440
Speaker 2: to be envisioning monkeys swinging from tree to treat a tree.

795
00:47:29,520 --> 00:47:32,000
Speaker 1: Yeah, grabbing all these I love it. Such a vivid

796
00:47:32,080 --> 00:47:35,080
Speaker 1: image of how like an economic ecosystem works.

797
00:47:35,200 --> 00:47:35,359
Speaker 3: Now.

798
00:47:35,520 --> 00:47:37,440
Speaker 1: I really enjoyed that. And it's sort of like all

799
00:47:37,520 --> 00:47:39,960
Speaker 1: these things that like sort of like a bunch of

800
00:47:39,960 --> 00:47:41,320
Speaker 1: things clicked in that conversation.

801
00:47:41,640 --> 00:47:44,279
Speaker 2: Well, here's the most important question. Do you think it's

802
00:47:44,280 --> 00:47:46,360
Speaker 2: going to help you be better at trade all No,

803
00:47:46,520 --> 00:47:47,160
Speaker 2: because I'm not.

804
00:47:47,120 --> 00:47:50,719
Speaker 1: Good at geography, and so like maybe I don't know,

805
00:47:50,840 --> 00:47:52,560
Speaker 1: maybe it will. I think I just need to study

806
00:47:52,560 --> 00:47:52,919
Speaker 1: the map.

807
00:47:53,000 --> 00:47:54,200
Speaker 2: You don't need the geography.

808
00:47:54,320 --> 00:47:54,480
Speaker 3: Hint.

809
00:47:54,600 --> 00:47:56,680
Speaker 2: If you get it in your first go, Joe, that's

810
00:47:56,719 --> 00:47:57,759
Speaker 2: what you should be aiming for.

811
00:47:58,160 --> 00:47:58,360
Speaker 3: You know.

812
00:47:58,400 --> 00:48:01,160
Speaker 1: What I thought was really interesting was like this idea

813
00:48:01,280 --> 00:48:04,480
Speaker 1: of like getting out of the resource curse is not

814
00:48:04,560 --> 00:48:08,000
Speaker 1: as straightforward as just oh, we're going to do more

815
00:48:08,040 --> 00:48:10,120
Speaker 1: with the thing that we already sell. So yeah, and

816
00:48:10,160 --> 00:48:13,399
Speaker 1: his point about Dubai was really interesting, how like there

817
00:48:13,400 --> 00:48:16,040
Speaker 1: are things that might go into selling a resource, like

818
00:48:16,320 --> 00:48:20,320
Speaker 1: having a port or having cold storage chain, or certain

819
00:48:20,360 --> 00:48:23,880
Speaker 1: things that aren't necessarily the thing itself, and that often

820
00:48:23,960 --> 00:48:26,680
Speaker 1: these sort of like the new trajectory of development may

821
00:48:26,719 --> 00:48:29,360
Speaker 1: not be that thing, but some of the other goods

822
00:48:29,360 --> 00:48:31,440
Speaker 1: and services that went into making the thing.

823
00:48:31,840 --> 00:48:35,200
Speaker 2: Well, the other thing that is sort of important in

824
00:48:35,320 --> 00:48:40,040
Speaker 2: that conversation is the idea of governments making specific decisions

825
00:48:40,080 --> 00:48:42,840
Speaker 2: about this, and that certainly comes into play with Dubai.

826
00:48:42,920 --> 00:48:46,520
Speaker 2: You know, Dubai made a very conscious decision acknowledged that

827
00:48:46,719 --> 00:48:49,160
Speaker 2: it wouldn't have oil forever and so it needed to

828
00:48:49,200 --> 00:48:52,239
Speaker 2: diversify its economy and then proceeded to do so. I

829
00:48:52,320 --> 00:48:55,000
Speaker 2: think it's true in places like South Korea that also

830
00:48:55,040 --> 00:48:57,879
Speaker 2: score very high on complexity nowadays, they had a lot

831
00:48:57,920 --> 00:49:02,240
Speaker 2: of different types of industrial paul and also media policy, yeah,

832
00:49:02,320 --> 00:49:06,319
Speaker 2: to make k pop a thing, which we've discussed previously

833
00:49:06,520 --> 00:49:10,160
Speaker 2: on the show, and even some small island nations that

834
00:49:10,239 --> 00:49:13,359
Speaker 2: become helicopter manufacturers. To your point, Joe, I think there's

835
00:49:13,360 --> 00:49:15,200
Speaker 2: a lot of direction taking place there as well.

836
00:49:15,560 --> 00:49:17,640
Speaker 1: The world is so interesting now. I think I'm going

837
00:49:17,680 --> 00:49:19,719
Speaker 1: to spend the rest of the day looking at the

838
00:49:19,760 --> 00:49:22,560
Speaker 1: atlas of economic complexity and just like clicking from country

839
00:49:22,560 --> 00:49:23,040
Speaker 1: to country.

840
00:49:23,120 --> 00:49:26,200
Speaker 2: It is really fun. And I know Ricardo spoke about this,

841
00:49:26,360 --> 00:49:30,080
Speaker 2: but you can look at sort of suggestions or feasible

842
00:49:30,080 --> 00:49:33,960
Speaker 2: opportunities for future economic development, which is really really fun.

843
00:49:34,320 --> 00:49:37,000
Speaker 1: That is really fun. And maybe I just might memorize

844
00:49:37,000 --> 00:49:39,040
Speaker 1: every single one so that I could get all the

845
00:49:39,040 --> 00:49:39,799
Speaker 1: tradells in one.

846
00:49:40,000 --> 00:49:42,600
Speaker 2: Do we need to do competitive trade all competition?

847
00:49:42,680 --> 00:49:42,879
Speaker 3: Yes?

848
00:49:43,120 --> 00:49:45,359
Speaker 2: I think that should be our next live event. All right,

849
00:49:45,400 --> 00:49:47,640
Speaker 2: shall we leave it there, Let's leave it there. This

850
00:49:47,680 --> 00:49:50,560
Speaker 2: has been another episode of the ad Thoughts podcast. I'm

851
00:49:50,560 --> 00:49:53,160
Speaker 2: Tracy Alloway. You can follow me on Twitter at Tracy

852
00:49:53,160 --> 00:49:54,160
Speaker 2: Alloway and.

853
00:49:54,160 --> 00:49:57,120
Speaker 1: I'm Joe Wisenthal. You can follow me on Twitter at

854
00:49:57,160 --> 00:50:00,799
Speaker 1: the Stalwart. Follow our guest Ricardo Houseman on Twitter. He's

855
00:50:00,880 --> 00:50:05,359
Speaker 1: at Ricardo Underscore Houseman. Follow our producers Carman Rodriguez at

856
00:50:05,440 --> 00:50:09,680
Speaker 1: Carmen Arman and Dash Bennett at Dashbot. And check out

857
00:50:09,680 --> 00:50:13,120
Speaker 1: all of our podcasts at Bloomberg under the handle at podcasts.

858
00:50:13,239 --> 00:50:15,680
Speaker 1: And for more odd Lots content, go to Bloomberg dot

859
00:50:15,680 --> 00:50:19,480
Speaker 1: com slash odd Lots, where we have transcripts, a blog

860
00:50:19,520 --> 00:50:23,279
Speaker 1: in a newsletter, and we have a trade all room

861
00:50:23,480 --> 00:50:27,080
Speaker 1: in the discord where you Discord dot gg slash odd

862
00:50:27,160 --> 00:50:29,200
Speaker 1: Lots listeners are in their twenty four to seven talk

863
00:50:29,239 --> 00:50:31,759
Speaker 1: about all these topics, and there is a room where

864
00:50:31,800 --> 00:50:34,279
Speaker 1: everyone posts how they did on the Trader that day.

865
00:50:34,640 --> 00:50:37,919
Speaker 1: So a play the Trade oll and b post your scores.

866
00:50:37,880 --> 00:50:40,319
Speaker 2: And if you enjoy all thoughts, please leave us a

867
00:50:40,400 --> 00:50:44,120
Speaker 2: positive review on your favorite podcast platform. Thanks for listening,