1 00:00:02,240 --> 00:00:06,800 Speaker 1: This is Masters in Business with Very Ridholts on Bloomberg Radio. 2 00:00:07,200 --> 00:00:10,320 Speaker 1: This week on the podcast, I have an extra extra 3 00:00:10,400 --> 00:00:14,400 Speaker 1: special guest. His name is Michael Spence, and this is 4 00:00:14,400 --> 00:00:18,240 Speaker 1: a conversation just filled with wonky goodness. He is the 5 00:00:18,280 --> 00:00:21,239 Speaker 1: winner of the Nobel Prize and Economics from two thousand one, 6 00:00:21,720 --> 00:00:28,600 Speaker 1: effectively on information theory, about how information impacts market structures. 7 00:00:28,840 --> 00:00:34,600 Speaker 1: We talk about asymmetries and gaps and really how digital 8 00:00:35,280 --> 00:00:40,000 Speaker 1: economies are just changing the entire world. Um Spence has 9 00:00:40,040 --> 00:00:43,720 Speaker 1: had a number of really fascinating forecasts that have all 10 00:00:43,760 --> 00:00:47,120 Speaker 1: turned out to be quite prescient. Uh. He's almost blase 11 00:00:47,280 --> 00:00:51,520 Speaker 1: about it. He he describes them as all beevitable. Uh. 12 00:00:51,560 --> 00:00:54,840 Speaker 1: This is really a fascinating conversation. If you're interested at 13 00:00:54,880 --> 00:01:00,800 Speaker 1: all in information signaling, in how economies di elop and grow, 14 00:01:01,560 --> 00:01:06,560 Speaker 1: about the impact of not just technology but government institutions 15 00:01:06,560 --> 00:01:09,600 Speaker 1: and intellectual capital, you're going to find this to be 16 00:01:09,680 --> 00:01:14,119 Speaker 1: an absolutely fascinating conversation. So, with no further ado, my 17 00:01:14,280 --> 00:01:21,240 Speaker 1: interview with n y Us Michael Spence. This is Masters 18 00:01:21,280 --> 00:01:25,760 Speaker 1: in Business with Barry Ridholts on Bloomberg Radio. My extra 19 00:01:25,840 --> 00:01:29,160 Speaker 1: special guest this week is Michael Spence. He is the 20 00:01:29,160 --> 00:01:31,840 Speaker 1: two thousand and one Nobel Prize winner in Economics for 21 00:01:31,920 --> 00:01:36,800 Speaker 1: his work on the dynamics of information flows in markets. 22 00:01:36,840 --> 00:01:39,560 Speaker 1: He is the former dean of the Stanford Graduate School 23 00:01:39,600 --> 00:01:42,880 Speaker 1: of Business. He is currently a professor at n HYU Stern. 24 00:01:43,720 --> 00:01:47,920 Speaker 1: He is a senior advisor at General Atlantic, a very 25 00:01:48,000 --> 00:01:52,520 Speaker 1: large private equity firm which manages about thirty five billion dollars. 26 00:01:52,560 --> 00:01:55,800 Speaker 1: Michael Spence, Welcome to Bloomberg. Thank you very It's great 27 00:01:55,800 --> 00:01:59,560 Speaker 1: to be here. So I'm fascinated by the work you 28 00:01:59,640 --> 00:02:02,880 Speaker 1: do um your Nobel Prize. I'm going to take what 29 00:02:03,120 --> 00:02:06,360 Speaker 1: is normally a somewhat esoteric research area and see if 30 00:02:06,360 --> 00:02:10,679 Speaker 1: I can make it understandable how people make decisions when 31 00:02:11,200 --> 00:02:16,160 Speaker 1: critical information is hard to find. Is that a fair oversimplification, Yeah, 32 00:02:16,200 --> 00:02:18,960 Speaker 1: it's certainly part of it. Yep. So so tell us 33 00:02:18,960 --> 00:02:21,000 Speaker 1: about that. What how did you find your way into 34 00:02:21,040 --> 00:02:26,799 Speaker 1: that space and what are the dynamics of information flows? Well, 35 00:02:27,000 --> 00:02:30,000 Speaker 1: so I got I got interested in this when I 36 00:02:30,040 --> 00:02:34,120 Speaker 1: was a graduate student, probably little color commentary. By the 37 00:02:34,160 --> 00:02:38,120 Speaker 1: time I had finished my general exams, which is the 38 00:02:38,160 --> 00:02:40,800 Speaker 1: first two years of a PhD program. I've been in 39 00:02:40,880 --> 00:02:45,440 Speaker 1: school up through high school, thirteen years in Canada. UM. 40 00:02:45,639 --> 00:02:48,080 Speaker 1: Then I had four years at Princeton, These were all 41 00:02:48,080 --> 00:02:52,919 Speaker 1: wonderful experiences. And two years at Oxford UM a road scholar, 42 00:02:53,000 --> 00:02:56,760 Speaker 1: A road scholar, yeah, doing mathematics eventually and UM, and 43 00:02:56,800 --> 00:02:59,000 Speaker 1: then two years in the PhD program. And I've had 44 00:02:59,000 --> 00:03:03,880 Speaker 1: about enough, and so I went to one of my advisors, 45 00:03:03,919 --> 00:03:06,880 Speaker 1: he's a great friend, Dick Sauser at the Kennedy School, 46 00:03:07,160 --> 00:03:09,399 Speaker 1: and I said, I think I'm gonna quit. And he said, 47 00:03:09,400 --> 00:03:12,640 Speaker 1: what are you gonna do? And I said, well, I mean, 48 00:03:12,800 --> 00:03:15,440 Speaker 1: you know, I'm tired of, you know, talking to myself 49 00:03:15,520 --> 00:03:20,880 Speaker 1: and sitting in a library and stuff. So UM he said, uh. 50 00:03:21,160 --> 00:03:24,079 Speaker 1: He said, the problem is you don't have enough human contact. 51 00:03:24,120 --> 00:03:27,360 Speaker 1: You should teach. And so he did two things for me. 52 00:03:27,440 --> 00:03:29,360 Speaker 1: He he gave me a little bit of his course 53 00:03:29,480 --> 00:03:33,600 Speaker 1: on social choice theory, which I screwed up royally my 54 00:03:33,639 --> 00:03:35,920 Speaker 1: first outing as a teacher. And the second thing he 55 00:03:35,960 --> 00:03:39,440 Speaker 1: did is he made me what he called rapport tour 56 00:03:40,240 --> 00:03:43,800 Speaker 1: of a faculty seminar in the then new, new, one 57 00:03:43,840 --> 00:03:47,000 Speaker 1: year old Kennedy School. It was extraordinary group of people, 58 00:03:47,120 --> 00:03:51,640 Speaker 1: you know. It was Francis Bathor and um Lestero came 59 00:03:51,680 --> 00:03:54,960 Speaker 1: down to visit from m I T and Ken Arrow, 60 00:03:55,480 --> 00:03:58,320 Speaker 1: Tom Shelling, Uh, just as a quite a run of 61 00:03:58,320 --> 00:04:01,280 Speaker 1: Nobel laureates and that, oh no, it was pretty amazing. 62 00:04:01,640 --> 00:04:05,280 Speaker 1: Not all of them have been recognized in that capacity then, 63 00:04:06,400 --> 00:04:09,120 Speaker 1: And so that was fascinating. And my job was to 64 00:04:09,200 --> 00:04:11,880 Speaker 1: turn what was a kind of general discussion you know 65 00:04:11,920 --> 00:04:15,760 Speaker 1: how those go all over the place into nine pages 66 00:04:15,760 --> 00:04:21,599 Speaker 1: that made it look like just brilliant you know, linear exposition. 67 00:04:22,240 --> 00:04:23,640 Speaker 1: So I did that and I had a lot of 68 00:04:23,640 --> 00:04:26,320 Speaker 1: fun doing it. And Kenn Arrow, to who is dying day, 69 00:04:26,360 --> 00:04:29,159 Speaker 1: said that that was the thing he thought was my 70 00:04:29,240 --> 00:04:34,640 Speaker 1: greatest skill, Rappert touring um. In the course of that, 71 00:04:34,760 --> 00:04:37,560 Speaker 1: Lessera came down and started talking about what he called 72 00:04:37,600 --> 00:04:41,920 Speaker 1: statistical discrimination. So I don't want to, you know, make 73 00:04:41,960 --> 00:04:46,680 Speaker 1: this too nerdy, but basically, whenever you have okay, when 74 00:04:46,720 --> 00:04:50,120 Speaker 1: when you have missing information, then basically you get people classified, 75 00:04:50,680 --> 00:04:54,000 Speaker 1: you know, by what you can see or detect as 76 00:04:54,000 --> 00:04:57,640 Speaker 1: opposed to what you don't see. Uh, and Less's idea, 77 00:04:57,960 --> 00:05:01,159 Speaker 1: and that automatically produces discriminate nation. So what happens in 78 00:05:01,240 --> 00:05:04,880 Speaker 1: any you know, economic or social context is that the 79 00:05:05,320 --> 00:05:08,520 Speaker 1: members of a group who are otherwise indistinguishable from each 80 00:05:08,560 --> 00:05:12,400 Speaker 1: other I mean, in in your world, think of asset classes, okay, 81 00:05:12,839 --> 00:05:14,960 Speaker 1: um is a little bit like this. Things get lumped 82 00:05:15,000 --> 00:05:16,920 Speaker 1: into an asset class because they're supposed to be sort 83 00:05:16,920 --> 00:05:21,360 Speaker 1: of similar and the end telemarkets get deep informationally, you know, 84 00:05:21,560 --> 00:05:24,640 Speaker 1: you don't see all the differences, so they tend to 85 00:05:24,680 --> 00:05:29,440 Speaker 1: be averaged. Right When you average a bunch of diverse 86 00:05:29,560 --> 00:05:33,920 Speaker 1: entities that are in one of these you know, silos 87 00:05:33,960 --> 00:05:38,719 Speaker 1: that are distinguishable from other silos by what's visible or detectable, 88 00:05:39,000 --> 00:05:42,280 Speaker 1: then you basically get the people at the upper end 89 00:05:42,279 --> 00:05:45,599 Speaker 1: of some quality spectrum get treated as the average and 90 00:05:45,680 --> 00:05:48,080 Speaker 1: that's not good. And at the lower end they get 91 00:05:48,080 --> 00:05:50,920 Speaker 1: treated as the average and that's great for them. So 92 00:05:51,040 --> 00:05:54,039 Speaker 1: you're discriminating against the high quality end of the spectrum 93 00:05:54,080 --> 00:05:56,800 Speaker 1: and you're favoring the low quality. So this has to 94 00:05:56,839 --> 00:06:00,919 Speaker 1: have huge implications for people looking at, let's say, making 95 00:06:00,960 --> 00:06:05,960 Speaker 1: investments in either private or public companies. Absolutely, yeah, son, 96 00:06:06,040 --> 00:06:09,120 Speaker 1: But then it gets complicated, I mean for sure, um 97 00:06:09,520 --> 00:06:13,200 Speaker 1: So what so the phenomenon that this gives rise to 98 00:06:13,240 --> 00:06:16,320 Speaker 1: in markets is the one George Jakerlof wrote about. Although 99 00:06:16,360 --> 00:06:18,840 Speaker 1: the insurance people have known it for years, it's called 100 00:06:18,880 --> 00:06:23,200 Speaker 1: adverse selection. And what happens in that in that context 101 00:06:23,279 --> 00:06:26,600 Speaker 1: is basically have what I just described, this quality spectrum 102 00:06:26,600 --> 00:06:29,120 Speaker 1: that has the unfortunate properties. You can't tell the difference 103 00:06:30,000 --> 00:06:32,520 Speaker 1: between the the entities, think of them as things for 104 00:06:32,560 --> 00:06:36,279 Speaker 1: sale and differing in quality. Used cars is the example 105 00:06:36,360 --> 00:06:38,920 Speaker 1: he used. So what happens in a market like that 106 00:06:39,400 --> 00:06:42,680 Speaker 1: is that the price reflects the average quality, and the 107 00:06:42,760 --> 00:06:45,440 Speaker 1: people at the top end of the quality spectrum say 108 00:06:45,520 --> 00:06:48,279 Speaker 1: that's not a very good price for me, right, and 109 00:06:48,320 --> 00:06:50,880 Speaker 1: they take their product out of the market from the 110 00:06:50,920 --> 00:06:54,440 Speaker 1: top end, and then the average quality falls and eventually 111 00:06:54,440 --> 00:06:57,200 Speaker 1: people figure that out and the price goes down, and 112 00:06:57,240 --> 00:06:59,120 Speaker 1: then the people at the top end of the remaining 113 00:06:59,160 --> 00:07:01,160 Speaker 1: spectrum say that's not a very good price. I'll take 114 00:07:01,200 --> 00:07:04,839 Speaker 1: mine out right. So that's that's the origin of the 115 00:07:04,960 --> 00:07:07,440 Speaker 1: term adverse selection. People are selecting in and out of 116 00:07:07,480 --> 00:07:10,920 Speaker 1: the market, and it's adverse because the top end of 117 00:07:10,920 --> 00:07:14,640 Speaker 1: the quality spectrum leaves. First, let's talk about your book, 118 00:07:14,720 --> 00:07:19,920 Speaker 1: The Next Convergence, because it's such a fascinating application of 119 00:07:19,920 --> 00:07:23,520 Speaker 1: of information flows. Uh, there's a quote in the beginning 120 00:07:23,560 --> 00:07:28,920 Speaker 1: that I just found absolutely mind boggling. From seventeen fifty 121 00:07:29,040 --> 00:07:33,240 Speaker 1: to nineteen fifty, the average income of people living in 122 00:07:33,440 --> 00:07:39,080 Speaker 1: countries that underwent the Industrial Revolution, so their incomes rise 123 00:07:39,760 --> 00:07:45,160 Speaker 1: twenty to forty times versus the non industrialized countries. And 124 00:07:45,200 --> 00:07:48,280 Speaker 1: this was only fifteen percent of the world's population. Is 125 00:07:48,280 --> 00:07:53,600 Speaker 1: that about right X that's amazing. Yeah, So the growth 126 00:07:53,720 --> 00:07:58,160 Speaker 1: rates weren't breathtaking, you know, probably on the neighborhood of 127 00:07:58,200 --> 00:08:01,360 Speaker 1: two in real terms, are on a per capita basis, 128 00:08:01,720 --> 00:08:04,160 Speaker 1: but if you if you do it for two hundred years, 129 00:08:04,160 --> 00:08:07,520 Speaker 1: you get a fairly big ink magic of compounding exactly, 130 00:08:07,640 --> 00:08:10,960 Speaker 1: so that that was basically it. Then then you know, 131 00:08:11,040 --> 00:08:15,120 Speaker 1: the the other a living in countries that we now 132 00:08:15,160 --> 00:08:18,520 Speaker 1: call emerging economies. Most of them some of them haven't 133 00:08:18,560 --> 00:08:21,800 Speaker 1: emerged very much, but uh but there. But that's the 134 00:08:21,840 --> 00:08:26,000 Speaker 1: other group, and they were held back by m essentially 135 00:08:26,520 --> 00:08:30,760 Speaker 1: uh global economy that wasn't really open and the colonial 136 00:08:30,920 --> 00:08:33,680 Speaker 1: structure that was the governance. So the rest of the 137 00:08:33,679 --> 00:08:38,040 Speaker 1: book you basically say, well, seventeen fifty to nineteen fifty 138 00:08:38,480 --> 00:08:42,400 Speaker 1: was the Industrial Revolution. The next hundred years, I think 139 00:08:42,440 --> 00:08:48,439 Speaker 1: you referenced nine six of the world's population will join 140 00:08:48,800 --> 00:08:53,160 Speaker 1: the affluent. That's a big, bold number. We're about halfway 141 00:08:53,160 --> 00:08:58,079 Speaker 1: through that process. Our maybe a little more. How accurate 142 00:08:58,360 --> 00:09:01,640 Speaker 1: was that forecast? And and how is this actually happening? 143 00:09:03,000 --> 00:09:05,000 Speaker 1: So I think it's well underway. I mean, it may 144 00:09:05,000 --> 00:09:08,240 Speaker 1: have been a bit optimistic, but you know, China looks 145 00:09:08,280 --> 00:09:10,480 Speaker 1: like it's well on the way. It's a high middle 146 00:09:10,520 --> 00:09:13,600 Speaker 1: income country with a very good chance of being, you know, 147 00:09:14,400 --> 00:09:17,120 Speaker 1: a high income country, admittedly at the low end of 148 00:09:17,120 --> 00:09:20,160 Speaker 1: the spectrum in another ten to fifteen years. India is 149 00:09:20,200 --> 00:09:22,959 Speaker 1: a bit further behind, but there, you know, humming along. 150 00:09:23,360 --> 00:09:25,920 Speaker 1: You had them those two together, and you've got two 151 00:09:25,960 --> 00:09:29,880 Speaker 1: point I think it's seven billion people, which is a 152 00:09:29,920 --> 00:09:34,120 Speaker 1: significant fraction of the world seven right, Yeah, that's half 153 00:09:34,160 --> 00:09:37,280 Speaker 1: of my sixty percent. You've got you know, the rest 154 00:09:37,360 --> 00:09:39,600 Speaker 1: of Asia. That some of it came earlier, some of 155 00:09:39,640 --> 00:09:42,600 Speaker 1: it came later, and so on. So it's it's I 156 00:09:42,640 --> 00:09:45,600 Speaker 1: think this convergent process is going to be very hard 157 00:09:45,640 --> 00:09:49,800 Speaker 1: to stop because people, because the structures are there to 158 00:09:50,000 --> 00:09:53,120 Speaker 1: enable it, and because people have gotten the hang of 159 00:09:53,160 --> 00:09:56,360 Speaker 1: it that it's actually possible. You discussed in the book 160 00:09:56,600 --> 00:10:01,080 Speaker 1: that post World War Two Japan was a very unusual 161 00:10:01,160 --> 00:10:05,640 Speaker 1: example compared to other so called developing nations. What what 162 00:10:05,760 --> 00:10:10,599 Speaker 1: made Japan so unique, especially over that seventeen fifty to 163 00:10:10,679 --> 00:10:14,440 Speaker 1: nineteen fifty era. Yeah, so Japan is a kind of hybrid. 164 00:10:14,760 --> 00:10:17,839 Speaker 1: Um so most of Asia, by the way, and right 165 00:10:17,840 --> 00:10:20,480 Speaker 1: after World War two, Asia was by far the poorest 166 00:10:20,520 --> 00:10:24,320 Speaker 1: part of the world, worst in Africa, worst in Africa. Yeah, 167 00:10:24,400 --> 00:10:27,440 Speaker 1: And they and the economists at the time, you know, 168 00:10:27,600 --> 00:10:30,440 Speaker 1: when asked development economists, when I asked, you know, where 169 00:10:30,559 --> 00:10:32,520 Speaker 1: was the real trouble going to be? They said Asia. 170 00:10:32,679 --> 00:10:35,560 Speaker 1: Many of them said Asia, because Asia doesn't have natural 171 00:10:35,600 --> 00:10:39,360 Speaker 1: resource wealth on balanced and Africa is by far the 172 00:10:39,440 --> 00:10:44,200 Speaker 1: richest royal diamonds, you know, you name it um. And 173 00:10:44,240 --> 00:10:46,400 Speaker 1: that turned out not to be a good guest, because 174 00:10:46,480 --> 00:10:50,440 Speaker 1: that turns out that the kind of real capital that 175 00:10:50,600 --> 00:10:53,760 Speaker 1: enables this growth as people provided they're educated and so on, 176 00:10:53,880 --> 00:10:58,120 Speaker 1: and not not just uh, you know, mineral wealth. Yeah. 177 00:10:58,200 --> 00:11:03,320 Speaker 1: So so basically, I think, you know, what the situation was, 178 00:11:03,800 --> 00:11:08,439 Speaker 1: Japan was a hybrid because it had gotten to middle 179 00:11:08,480 --> 00:11:11,360 Speaker 1: income status. And the reason it got there is that 180 00:11:11,440 --> 00:11:15,200 Speaker 1: in eighteen sixty eight, the Meiji Restoration, it abandoned the 181 00:11:15,240 --> 00:11:20,560 Speaker 1: policy of isolationism, embrace trade. Embrace trade, braced openness, and 182 00:11:20,600 --> 00:11:23,360 Speaker 1: they had started to modernize. And then that, of course, 183 00:11:23,400 --> 00:11:26,240 Speaker 1: World War two wasn't and they were an imperial power 184 00:11:26,760 --> 00:11:33,120 Speaker 1: you know, all over Asia, you know, China, Korea, etcetera. Um, 185 00:11:33,160 --> 00:11:35,520 Speaker 1: So that World War Two was a huge setback, but 186 00:11:35,559 --> 00:11:38,760 Speaker 1: basically they got back on track. So over the same 187 00:11:38,800 --> 00:11:42,960 Speaker 1: period that that post industrial period, pre war post industrial period, 188 00:11:43,280 --> 00:11:48,600 Speaker 1: how come China fared so much worse than Japan. So 189 00:11:48,800 --> 00:11:53,560 Speaker 1: there there's two kind of crucial ingredients. And in the 190 00:11:53,600 --> 00:11:56,160 Speaker 1: post war growth that we've I've seen and by the way, 191 00:11:56,160 --> 00:11:58,080 Speaker 1: this is growth that we've never seen before. I mean 192 00:11:58,120 --> 00:12:00,680 Speaker 1: we're talking about extended periods like two and a half 193 00:12:00,720 --> 00:12:04,839 Speaker 1: decades of five, six, seven growth even higher in China 194 00:12:05,360 --> 00:12:07,920 Speaker 1: just never happened before. So one of the things I 195 00:12:07,960 --> 00:12:09,520 Speaker 1: was trying to do in the book is explain how 196 00:12:09,600 --> 00:12:12,559 Speaker 1: you could do that? Right? How could you have a 197 00:12:12,679 --> 00:12:15,720 Speaker 1: max of two you know before that or two and 198 00:12:15,720 --> 00:12:19,080 Speaker 1: a half? How could terms yeah in real terms, how 199 00:12:19,120 --> 00:12:22,240 Speaker 1: could you have advanced countries growing at max three in 200 00:12:22,320 --> 00:12:24,440 Speaker 1: real terms? And these people are growing at seven, eight 201 00:12:24,480 --> 00:12:27,920 Speaker 1: and nine. And the answers they're catching up, right, all 202 00:12:28,000 --> 00:12:31,400 Speaker 1: of that technology that you need to drive growth in 203 00:12:31,440 --> 00:12:37,319 Speaker 1: the long run. You know, the the solo insight UH 204 00:12:37,559 --> 00:12:39,720 Speaker 1: was already developed, so it just had to be brought 205 00:12:39,760 --> 00:12:43,000 Speaker 1: in and adapted. In other words, this isn't one country 206 00:12:43,040 --> 00:12:46,199 Speaker 1: amongst many that are all emerging at once. When you 207 00:12:46,280 --> 00:12:50,280 Speaker 1: take an emerging economy and they're surrounded by developed economies, 208 00:12:50,640 --> 00:12:54,960 Speaker 1: that's an accelerant provided they're open and provided their investing 209 00:12:54,960 --> 00:12:58,880 Speaker 1: at high enough rates. So so well, clearly China is 210 00:12:58,960 --> 00:13:03,360 Speaker 1: making massive investor. Do you consider them open enough to 211 00:13:03,400 --> 00:13:06,760 Speaker 1: continue taking full advantage of what the rest of the 212 00:13:06,760 --> 00:13:10,360 Speaker 1: globe can do for their growth at the moment? Yes, 213 00:13:10,559 --> 00:13:14,280 Speaker 1: and certainly historically once they decided to open in ninety 214 00:13:14,360 --> 00:13:18,760 Speaker 1: eight undergoing chapin Um. Now, is it logically possible that 215 00:13:18,840 --> 00:13:21,560 Speaker 1: they could close themselves off enough to to put a 216 00:13:21,600 --> 00:13:26,319 Speaker 1: major dent in their growth, Yes, it's it's it's unlikely, 217 00:13:26,400 --> 00:13:29,000 Speaker 1: I think, but it's possible. You asked about the history 218 00:13:29,040 --> 00:13:33,880 Speaker 1: of China. So China had a revolution in nine the 219 00:13:33,920 --> 00:13:39,120 Speaker 1: Communists took over. The Communists, on the positive side, probably 220 00:13:39,200 --> 00:13:42,240 Speaker 1: had the intention of making everybody better off. Right that, 221 00:13:42,400 --> 00:13:46,160 Speaker 1: you can find a lot of governing structures in the 222 00:13:46,240 --> 00:13:50,000 Speaker 1: developing world where the governing elite, whoever they are and 223 00:13:50,040 --> 00:13:52,920 Speaker 1: however they got there, are doing something other than trying 224 00:13:52,920 --> 00:13:58,280 Speaker 1: to make people better off. Lots of corruptions. There's mineral resources, 225 00:13:58,440 --> 00:14:01,440 Speaker 1: royal all I kind of thing. So if they're doing that, 226 00:14:01,559 --> 00:14:04,679 Speaker 1: nothing good is going to happen. The Chinese weren't doing that, 227 00:14:04,760 --> 00:14:07,359 Speaker 1: and they did put a hell of a lot of resources, 228 00:14:07,400 --> 00:14:10,640 Speaker 1: given the side the low levels of income in the 229 00:14:10,640 --> 00:14:14,360 Speaker 1: economy into education. What they didn't do is run a 230 00:14:14,400 --> 00:14:17,760 Speaker 1: market economy. So for the first twenty nine years they 231 00:14:17,800 --> 00:14:21,440 Speaker 1: basically got nowhere, but they built assets that were useful. 232 00:14:21,480 --> 00:14:24,720 Speaker 1: And when they changed the the development model a growth 233 00:14:24,760 --> 00:14:31,440 Speaker 1: model to opening up and and using markets and initially selectively, 234 00:14:32,040 --> 00:14:34,000 Speaker 1: then it just took off like a rocket. That was 235 00:14:34,040 --> 00:14:36,720 Speaker 1: the post Knickson era. That was the post Knickson eira, 236 00:14:36,800 --> 00:14:39,840 Speaker 1: So it was they date the Chinese date the reform 237 00:14:40,680 --> 00:14:46,440 Speaker 1: process from Let's talk a little bit about job market signaling. 238 00:14:46,480 --> 00:14:50,360 Speaker 1: We discussed some of that before. You've done a lot 239 00:14:50,400 --> 00:14:54,040 Speaker 1: of work on this tell us about the origins of 240 00:14:54,120 --> 00:14:58,680 Speaker 1: job market signaling and how it's evolved over time. So 241 00:14:58,840 --> 00:15:02,320 Speaker 1: the origin of it was was that discussion that we 242 00:15:02,400 --> 00:15:05,840 Speaker 1: had earlier that had to do with informational gaps, so 243 00:15:06,240 --> 00:15:10,280 Speaker 1: that the adverse selection problem is basically what happens in 244 00:15:10,360 --> 00:15:12,880 Speaker 1: markets if you can't close the gaps, and then we 245 00:15:12,920 --> 00:15:14,920 Speaker 1: talked about brands as a way of closing it. What 246 00:15:14,960 --> 00:15:18,240 Speaker 1: do markets do, They're going to try to close the gaps. 247 00:15:18,800 --> 00:15:21,000 Speaker 1: That's one of the things that between buyers and biber 248 00:15:21,080 --> 00:15:22,960 Speaker 1: doing buyers and sellers is the best way to think 249 00:15:23,000 --> 00:15:26,840 Speaker 1: about it. The second thing is signaling the things that 250 00:15:26,880 --> 00:15:33,360 Speaker 1: the the the sellers can do to convey actually accurate 251 00:15:33,400 --> 00:15:38,320 Speaker 1: information in the market. Um uh. The third thing that 252 00:15:38,400 --> 00:15:43,680 Speaker 1: we do collectively is regulate. Right, So financial markets have 253 00:15:43,960 --> 00:15:50,160 Speaker 1: enormously large gaps, uh. In principle um So it isn't 254 00:15:50,200 --> 00:15:53,400 Speaker 1: a great surprise that every set of financial markets in 255 00:15:53,440 --> 00:15:57,040 Speaker 1: the world has disclosure requirements, and they're pretty stringent, and 256 00:15:57,080 --> 00:16:01,480 Speaker 1: the penalties are pretty high for violating it, and and 257 00:16:01,480 --> 00:16:05,440 Speaker 1: and and when those disclosure requirements are not there are 258 00:16:05,480 --> 00:16:09,240 Speaker 1: not enforced, then you get bad misbehavior in the markets. 259 00:16:09,680 --> 00:16:11,840 Speaker 1: So what you see in the developing world all the time. 260 00:16:11,960 --> 00:16:14,560 Speaker 1: So there's there's that's interesting you bring that up because 261 00:16:14,600 --> 00:16:18,239 Speaker 1: it makes me think of the way we approach regulation 262 00:16:18,520 --> 00:16:22,160 Speaker 1: in some areas is to prevent a behavior, say this 263 00:16:22,280 --> 00:16:25,720 Speaker 1: behavior we're not going to allow. There are other aspects 264 00:16:25,720 --> 00:16:28,240 Speaker 1: where we say, well, we're gonna let you do what 265 00:16:28,240 --> 00:16:30,600 Speaker 1: you want, but you have to make these disclosures, so 266 00:16:30,760 --> 00:16:35,480 Speaker 1: buyers know, most people don't read the fine print. They're 267 00:16:35,520 --> 00:16:38,040 Speaker 1: not reading the credit card disclosures, they're not reading their 268 00:16:38,080 --> 00:16:43,120 Speaker 1: brokerage account disclosures. Given that, how does that information make 269 00:16:43,120 --> 00:16:45,880 Speaker 1: its way into the marketplace? Is the signals still there? 270 00:16:46,360 --> 00:16:50,320 Speaker 1: If of us don't read the disclosures, maybe it's of 271 00:16:50,400 --> 00:16:52,720 Speaker 1: us don't read it. Yeah, no, the answer is no, 272 00:16:52,840 --> 00:16:55,760 Speaker 1: it's not. And then you need if if it's important 273 00:16:55,800 --> 00:16:58,600 Speaker 1: to close that informational gap, then you then and you 274 00:16:58,640 --> 00:17:00,680 Speaker 1: can't close it, then you have to do do something else. 275 00:17:01,160 --> 00:17:03,440 Speaker 1: So we do all kinds of things. So we assume 276 00:17:04,200 --> 00:17:08,200 Speaker 1: that there are classes of investors who are not capable 277 00:17:08,240 --> 00:17:11,920 Speaker 1: of understanding the risk characteristics of certain kind asset classes, 278 00:17:12,240 --> 00:17:15,080 Speaker 1: and they're simply precluded from it. And you know, meaning 279 00:17:15,080 --> 00:17:18,600 Speaker 1: if you're not a credit investor, go into a hedge fund, 280 00:17:18,680 --> 00:17:21,600 Speaker 1: or don't have assets of a certain size, we assume 281 00:17:21,680 --> 00:17:25,320 Speaker 1: you can't withstand the kind of risk characteristics of that 282 00:17:25,400 --> 00:17:30,400 Speaker 1: asset class. So regulation in that sense has multiple avenues, 283 00:17:30,440 --> 00:17:34,119 Speaker 1: and they've done properly. They tend to be pragmatic response 284 00:17:34,240 --> 00:17:39,000 Speaker 1: to to real human behavior as opposed to some kind 285 00:17:39,000 --> 00:17:42,359 Speaker 1: of theory about it. Yeah, quite interesting. What about the 286 00:17:42,400 --> 00:17:47,439 Speaker 1: impact of technology on both signaling in the job market 287 00:17:47,640 --> 00:17:51,560 Speaker 1: and and the impact of regulation. Yeah. So this this 288 00:17:51,880 --> 00:17:55,000 Speaker 1: when I when when the Internet was sort of in 289 00:17:55,119 --> 00:17:58,280 Speaker 1: its early stages, meaning a kind of public property, let's 290 00:17:58,280 --> 00:18:01,640 Speaker 1: call it the mid nineties, people you know, had all 291 00:18:01,720 --> 00:18:04,479 Speaker 1: kinds of theories about what its impact was going to be. 292 00:18:04,880 --> 00:18:08,280 Speaker 1: And I got asked a lot of questions that the 293 00:18:08,359 --> 00:18:11,520 Speaker 1: gist of which we're is this going to close informational gaps? 294 00:18:12,400 --> 00:18:15,159 Speaker 1: And my answer then is the same now, but I 295 00:18:15,240 --> 00:18:17,680 Speaker 1: underestimated something. So my answer was, it's not going to 296 00:18:17,760 --> 00:18:21,240 Speaker 1: eliminate private information meaning information that I have because it's 297 00:18:21,280 --> 00:18:24,320 Speaker 1: me that you know, you're only going to get if 298 00:18:24,400 --> 00:18:28,240 Speaker 1: I somehow transmit it to you. Um. But but what 299 00:18:28,280 --> 00:18:31,560 Speaker 1: the Internet does is it gives you so many access 300 00:18:31,680 --> 00:18:35,440 Speaker 1: at very low cost of so much information that's correlated 301 00:18:35,760 --> 00:18:37,720 Speaker 1: with this kind of thing that I actually think it 302 00:18:37,760 --> 00:18:41,760 Speaker 1: does close informational gaps. And the best example of that, 303 00:18:41,840 --> 00:18:46,640 Speaker 1: I think is, um, what we see now in fintech. 304 00:18:47,400 --> 00:18:50,679 Speaker 1: You know, so you so in like and financial and 305 00:18:51,200 --> 00:18:53,919 Speaker 1: for example, an Ali babbie, you have a massive amount 306 00:18:53,960 --> 00:18:57,720 Speaker 1: of data because they basically they're half the mobile payment 307 00:18:57,760 --> 00:19:01,199 Speaker 1: system in China. They can use that data to issue 308 00:19:01,200 --> 00:19:05,680 Speaker 1: credit to little tiny businesses. There's there's a partially owned 309 00:19:05,680 --> 00:19:08,320 Speaker 1: subsidiary called my Bank that just won an award for 310 00:19:08,359 --> 00:19:12,520 Speaker 1: this using this data to make credit assessments and price 311 00:19:13,040 --> 00:19:19,040 Speaker 1: and price the credit appropriately. And uh and this these 312 00:19:19,080 --> 00:19:21,080 Speaker 1: people are otherwise blocked away. I mean, they can only 313 00:19:21,080 --> 00:19:24,000 Speaker 1: borrow from family and friends. A bank can't either assess 314 00:19:24,080 --> 00:19:28,600 Speaker 1: the risk and and or it's way too costly. So 315 00:19:28,680 --> 00:19:31,280 Speaker 1: this day and this information effectively creates a whole new mark, 316 00:19:31,320 --> 00:19:33,520 Speaker 1: creates a whole new market. I mean, and when we're 317 00:19:33,520 --> 00:19:36,880 Speaker 1: talking about I mean this, my bank has seventeen million 318 00:19:37,119 --> 00:19:43,040 Speaker 1: small business customers. Uh average number of employees five, no collateral, nothing. 319 00:19:43,320 --> 00:19:46,120 Speaker 1: They just look at their credit history, their transaction history, 320 00:19:46,200 --> 00:19:50,400 Speaker 1: transaction history, their payments history, you know, and and their 321 00:19:50,400 --> 00:19:53,880 Speaker 1: online activity. So let's talk a little bit about growth 322 00:19:53,880 --> 00:19:56,879 Speaker 1: and trade. And I want to start with the project 323 00:19:57,040 --> 00:20:00,000 Speaker 1: you did for the World Bank. Tell us a little 324 00:20:00,040 --> 00:20:02,919 Speaker 1: bit about that. It was quite fascinated. Yeah, so they 325 00:20:03,000 --> 00:20:06,040 Speaker 1: they I kind of stumbled into this because I had 326 00:20:06,080 --> 00:20:11,080 Speaker 1: not been a specialist in in um development growth related 327 00:20:11,119 --> 00:20:15,200 Speaker 1: to developing economies, and so I around about two thousand, 328 00:20:15,640 --> 00:20:18,800 Speaker 1: the early two thousands, I got a call um from 329 00:20:19,280 --> 00:20:22,520 Speaker 1: uh some folks at the World Bank who become good friends, 330 00:20:22,560 --> 00:20:24,800 Speaker 1: and they said, would you come and give a lecture 331 00:20:24,840 --> 00:20:29,199 Speaker 1: on investment and growth at the one of the spring conferences, 332 00:20:29,280 --> 00:20:32,879 Speaker 1: the Poverty Reduction and Economic Management? And I said why me? 333 00:20:33,880 --> 00:20:36,760 Speaker 1: And they said, well, you sort of you know, kind 334 00:20:36,760 --> 00:20:39,600 Speaker 1: of a microeconomic focus and pay attention to these things. 335 00:20:39,600 --> 00:20:42,119 Speaker 1: And so I thought to myself, this is this is 336 00:20:42,480 --> 00:20:45,600 Speaker 1: signaling or screening. I thought, okay, so here you meaning 337 00:20:45,600 --> 00:20:48,160 Speaker 1: that yours. They saw the Nobel and said, oh, let's 338 00:20:48,160 --> 00:20:49,800 Speaker 1: have him talk about this. Is that what you mean 339 00:20:49,800 --> 00:20:52,440 Speaker 1: by signaling? No? No, no, no, no was the decision 340 00:20:52,480 --> 00:20:55,280 Speaker 1: I made. So I made the following decision, Barry. I said, 341 00:20:55,280 --> 00:20:59,320 Speaker 1: I'm gonna do this, and there there's one of two outcomes. Uh. 342 00:20:59,440 --> 00:21:02,399 Speaker 1: Either it'll be a disaster and which I'll have learned 343 00:21:02,440 --> 00:21:04,679 Speaker 1: something that I shouldn't be, you know, mucking around with 344 00:21:04,720 --> 00:21:08,440 Speaker 1: the ten thousand people who are the experts in development 345 00:21:08,440 --> 00:21:10,640 Speaker 1: in the world. Or it'll go okay, and then I'll 346 00:21:10,720 --> 00:21:15,480 Speaker 1: learn something else. Right, So it was deliberately a screening device, 347 00:21:15,920 --> 00:21:18,480 Speaker 1: and it seemed to go okay. And from that came 348 00:21:18,520 --> 00:21:21,520 Speaker 1: a Commission on Growth and Development, and the idea behind 349 00:21:21,520 --> 00:21:25,880 Speaker 1: that commission wasn't to do original research. It was approximately 350 00:21:25,960 --> 00:21:30,879 Speaker 1: fifteen years since the Washington Consensus had been enunciated, UH, 351 00:21:31,920 --> 00:21:36,920 Speaker 1: which was it's it came out of Washington. Uh. I've 352 00:21:36,960 --> 00:21:40,880 Speaker 1: temporarily forgotten that John Williamson wrote it. It's been much 353 00:21:40,960 --> 00:21:44,560 Speaker 1: maligned and unfairly to be honest with the Washington Consensus 354 00:21:44,600 --> 00:21:48,840 Speaker 1: is perfectly sensible. Uh. Assessment of what it takes to 355 00:21:48,920 --> 00:21:51,040 Speaker 1: kind of grow and developing and developing. What was that 356 00:21:51,080 --> 00:21:54,119 Speaker 1: assessment said, well, there's a list of things. There's about 357 00:21:54,160 --> 00:21:58,679 Speaker 1: thirteen things that are crucial components. Um. Some people interpreted 358 00:21:58,760 --> 00:22:03,000 Speaker 1: as a kind of you know, uh, turnkey system. You 359 00:22:03,040 --> 00:22:06,760 Speaker 1: can't do that. Every country has idiosyncratic characteristics. But the 360 00:22:07,119 --> 00:22:09,080 Speaker 1: thing that gave the Washington and sense as a bad 361 00:22:09,160 --> 00:22:13,080 Speaker 1: name is it was taken, especially in Latin America, and 362 00:22:13,160 --> 00:22:19,760 Speaker 1: stripped down to liberalize, privatize, etcetera. And UH, and that 363 00:22:19,840 --> 00:22:24,320 Speaker 1: gave rise to kind of accesses and you know, not 364 00:22:24,600 --> 00:22:28,679 Speaker 1: terribly good results. UM. So we decided this it was 365 00:22:28,680 --> 00:22:31,320 Speaker 1: a good time. We had a lot of experience that 366 00:22:31,400 --> 00:22:35,040 Speaker 1: had been accumulated in countries like China and India and others. 367 00:22:35,400 --> 00:22:38,560 Speaker 1: Brazil had started had come out of its um twenty 368 00:22:38,560 --> 00:22:40,840 Speaker 1: five year funk and looked like it was starting to 369 00:22:40,880 --> 00:22:43,720 Speaker 1: grow um, et cetera. It was a good time to 370 00:22:43,800 --> 00:22:46,960 Speaker 1: kryd of figure out what research, what experience and so 371 00:22:47,040 --> 00:22:50,800 Speaker 1: on had taught us about that was useful and could 372 00:22:50,800 --> 00:22:53,200 Speaker 1: we kind of summarize it and give it back. So 373 00:22:53,240 --> 00:22:55,800 Speaker 1: that was the exercise. We wrote a seventy five page 374 00:22:55,880 --> 00:22:59,159 Speaker 1: report based on kind of two years of listening to 375 00:22:59,240 --> 00:23:03,080 Speaker 1: people and uh been thinking about it, and it was 376 00:23:03,119 --> 00:23:04,800 Speaker 1: meant to be an up and none of these things 377 00:23:04,840 --> 00:23:07,800 Speaker 1: are ever kind of you know, definitive, right, it was 378 00:23:07,840 --> 00:23:10,680 Speaker 1: an update. Right now we know this that we didn't 379 00:23:10,680 --> 00:23:12,720 Speaker 1: know before. And the course of doing that work, we 380 00:23:12,760 --> 00:23:15,360 Speaker 1: went and looked for countries that have grown for at 381 00:23:15,359 --> 00:23:17,840 Speaker 1: seven percent or more for twenty five years or more, 382 00:23:18,000 --> 00:23:22,359 Speaker 1: not every year, but on average. And there's thirteen countries. Really, 383 00:23:22,359 --> 00:23:25,520 Speaker 1: because that's a giant number, seven percent. Seven you double 384 00:23:25,560 --> 00:23:30,679 Speaker 1: every decade at se and uh, there's thirteen countries that 385 00:23:30,720 --> 00:23:34,000 Speaker 1: have done that at various points. China's one. Brazil in 386 00:23:34,000 --> 00:23:37,840 Speaker 1: the early post war period was one Korea. They won't 387 00:23:37,880 --> 00:23:42,280 Speaker 1: be surprised you the Timewan easy economy. Japan was in 388 00:23:42,320 --> 00:23:48,040 Speaker 1: that group. Uh, there were some surprises little Botswana. Really 389 00:23:48,119 --> 00:23:51,199 Speaker 1: he was a member of that group. Yeah, uh so, 390 00:23:51,520 --> 00:23:55,040 Speaker 1: different sizes, different government structures. It was it was pretty interesting. 391 00:23:55,440 --> 00:23:59,960 Speaker 1: Were there any consistencies across all thirteen. Yeah, they're they're 392 00:24:00,240 --> 00:24:02,560 Speaker 1: basically all the same growth model, which has come to 393 00:24:02,560 --> 00:24:05,200 Speaker 1: be called the Asian growth model. So it's it's high 394 00:24:05,280 --> 00:24:10,440 Speaker 1: levels of investment funded domestically, openness, including especially foreign direct investment, 395 00:24:10,480 --> 00:24:13,560 Speaker 1: which is the one of the principal channels for technolog 396 00:24:13,600 --> 00:24:19,080 Speaker 1: inbound technology transfer and leveraging the big global marketplace. If 397 00:24:19,119 --> 00:24:22,000 Speaker 1: you try to do this on a standalone basis, it 398 00:24:22,119 --> 00:24:24,080 Speaker 1: just doesn't work. I mean, you look at the demand 399 00:24:24,119 --> 00:24:26,639 Speaker 1: in a country with a per capita income of five dollars, 400 00:24:26,720 --> 00:24:30,640 Speaker 1: it's kind of food, shelter, energy, and not much else. 401 00:24:31,119 --> 00:24:35,439 Speaker 1: So everything we know, specialization and the atom Smith sense, 402 00:24:35,560 --> 00:24:39,720 Speaker 1: you know, taking advantage of comparative advantage. That none of 403 00:24:39,760 --> 00:24:43,000 Speaker 1: that works on a standalone basis a bit. But you know, 404 00:24:43,359 --> 00:24:46,159 Speaker 1: even a country the size of China in the early 405 00:24:46,240 --> 00:24:50,120 Speaker 1: stages is small relative to the global economy. Small economy 406 00:24:50,160 --> 00:24:52,639 Speaker 1: meaning yeah, small economy, not small number of people, but 407 00:24:52,720 --> 00:24:56,920 Speaker 1: small economy, so they can they can grow at very 408 00:24:57,040 --> 00:25:01,159 Speaker 1: high rates without really big coming a major presence in 409 00:25:01,160 --> 00:25:03,800 Speaker 1: the market and starting to turn the prices against themselves. 410 00:25:04,320 --> 00:25:06,919 Speaker 1: Um So in the book you write about China, and 411 00:25:07,040 --> 00:25:09,800 Speaker 1: remember the book was two thousand two or two thousand 412 00:25:09,880 --> 00:25:11,960 Speaker 1: three something like that. No, No, the book is two 413 00:25:11,960 --> 00:25:16,439 Speaker 1: thousand ten or eleven, Okay, exactly. You wrote, Um, China 414 00:25:16,520 --> 00:25:20,000 Speaker 1: is of U S or EU economy in ten to 415 00:25:20,080 --> 00:25:23,640 Speaker 1: fifteen years, it will be the same or bigger. It 416 00:25:23,680 --> 00:25:28,240 Speaker 1: looks like that's a very precedent forecast. Um where are 417 00:25:28,280 --> 00:25:31,560 Speaker 1: we in the process of China passing the EU or 418 00:25:31,640 --> 00:25:34,399 Speaker 1: the US in terms of their economy, not on a 419 00:25:34,440 --> 00:25:37,440 Speaker 1: per capita basis, but just on a growth basis. Yeah, 420 00:25:37,480 --> 00:25:40,520 Speaker 1: and grow. I think they're around sev now on a 421 00:25:40,560 --> 00:25:42,919 Speaker 1: growth basis, maybe a little bit more so we're just 422 00:25:43,000 --> 00:25:45,520 Speaker 1: a few years away. Yeah, it depends on I mean, 423 00:25:45,680 --> 00:25:47,560 Speaker 1: you know, the trade war could put a dent in 424 00:25:47,640 --> 00:25:52,240 Speaker 1: the kind of speed of the trajectory. Uh and and 425 00:25:52,240 --> 00:25:54,959 Speaker 1: a and a natural slowdown is occurring in China too. 426 00:25:55,119 --> 00:25:58,720 Speaker 1: I mean countries you know where they have per capita 427 00:25:58,840 --> 00:26:03,560 Speaker 1: incomes in the team meaning thousands. Um, just don't grow 428 00:26:03,600 --> 00:26:06,520 Speaker 1: at seven percent anymore. The catch up effect isn't as powerful. 429 00:26:06,560 --> 00:26:11,399 Speaker 1: They're generating technology they're becoming like advanced economies, all of 430 00:26:11,400 --> 00:26:14,400 Speaker 1: big numbers just starts to rear its exactly. Yeah, they're 431 00:26:14,440 --> 00:26:18,280 Speaker 1: kind they're they're they're becoming part of the kind of 432 00:26:18,320 --> 00:26:21,879 Speaker 1: global system that generates the technology that enables the growth 433 00:26:21,920 --> 00:26:24,520 Speaker 1: for all of us. So they mature into more of 434 00:26:24,560 --> 00:26:27,840 Speaker 1: a developed nation. So let's you mentioned the trade war. 435 00:26:27,920 --> 00:26:31,040 Speaker 1: Let's let's talk about that. Is how much of the 436 00:26:31,119 --> 00:26:35,399 Speaker 1: trade war and tariffs are heard in China? Is this 437 00:26:35,520 --> 00:26:37,960 Speaker 1: really having a major effect and what is it doing 438 00:26:38,240 --> 00:26:42,680 Speaker 1: here in the United States? Um? So in China, it's 439 00:26:42,680 --> 00:26:45,840 Speaker 1: slowing them down because they still have some dependence on 440 00:26:45,920 --> 00:26:50,920 Speaker 1: the export sector. Um And it's slowing everybody down through 441 00:26:51,160 --> 00:26:55,880 Speaker 1: uh somewhat different channel, which is uncertainty, which means globally 442 00:26:57,119 --> 00:27:01,960 Speaker 1: on globally. Yeah, So in other words, corporations are holding back, 443 00:27:02,040 --> 00:27:04,960 Speaker 1: holding back and making capex and hiring decisions because of 444 00:27:04,960 --> 00:27:08,280 Speaker 1: the uncertainty or know how this resolves? Yeah, where where 445 00:27:08,280 --> 00:27:10,359 Speaker 1: are we supposed to put our supply chains? You know, 446 00:27:10,560 --> 00:27:13,680 Speaker 1: we're just gonna gonna go away, right, There's some things 447 00:27:13,680 --> 00:27:16,879 Speaker 1: that are happening pretty fast. So, for example, China was 448 00:27:17,000 --> 00:27:22,639 Speaker 1: in the process of you know, basically moving the labor 449 00:27:22,680 --> 00:27:27,679 Speaker 1: intensive process or manufacturing assembly, which was the early expert 450 00:27:27,760 --> 00:27:31,159 Speaker 1: growth engine out And the reason has got nothing new 451 00:27:31,200 --> 00:27:33,399 Speaker 1: with the trade war is just their incomes are too 452 00:27:33,480 --> 00:27:35,800 Speaker 1: high to be the competitive place for that. They can 453 00:27:35,840 --> 00:27:38,680 Speaker 1: replace it with a higher yielding They replace it with 454 00:27:38,720 --> 00:27:41,440 Speaker 1: a higher yielding thing, and and they and the activity 455 00:27:41,480 --> 00:27:44,879 Speaker 1: itself to the extented it's not cut off by automation. 456 00:27:44,920 --> 00:27:54,199 Speaker 1: Digital technology goes to Vietnam, Bangladesh, Turkey, maybe Turkey, Ethiopia. 457 00:27:54,840 --> 00:27:58,360 Speaker 1: What about Mexico? Mexico is yeah, is there a little 458 00:27:58,400 --> 00:28:02,200 Speaker 1: Mexico is a moving upscale a little bit. Yeah, it's 459 00:28:02,200 --> 00:28:04,479 Speaker 1: a middle income country if you take an average, but 460 00:28:04,560 --> 00:28:08,239 Speaker 1: there are pockets you know where where it's it's very 461 00:28:08,320 --> 00:28:12,000 Speaker 1: lumpy distribution, it's not even so that's kind of yeah, 462 00:28:12,080 --> 00:28:15,679 Speaker 1: that's kind of where we are. UM. I think probably 463 00:28:15,720 --> 00:28:18,159 Speaker 1: for your listeners, the most important thing to understand is 464 00:28:18,240 --> 00:28:21,400 Speaker 1: China would have been clawbered twenty years ago if something 465 00:28:21,440 --> 00:28:24,280 Speaker 1: like this happened because they were dependent on the export 466 00:28:24,359 --> 00:28:27,479 Speaker 1: sector for the demand that enabled the growth. Now they 467 00:28:27,480 --> 00:28:31,399 Speaker 1: have this huge growing middle class with rising incomes like 468 00:28:31,760 --> 00:28:35,639 Speaker 1: like the United States, Um, they can enable the growth 469 00:28:35,720 --> 00:28:38,880 Speaker 1: with the domestic demand to much greater extent. That's interesting. 470 00:28:39,200 --> 00:28:42,320 Speaker 1: My sense of China versus the way the United States 471 00:28:42,360 --> 00:28:45,840 Speaker 1: has been approaching tariffs is that they play a much 472 00:28:46,040 --> 00:28:49,440 Speaker 1: longer game than we do. We think in months and quarters, 473 00:28:49,480 --> 00:28:53,040 Speaker 1: they seem to think in decades. The response to hit 474 00:28:54,480 --> 00:28:58,680 Speaker 1: the soybean farmers and the heart of the Trump base 475 00:28:59,160 --> 00:29:03,239 Speaker 1: seemed very, very calculated and not random at all. Uh, 476 00:29:03,760 --> 00:29:05,680 Speaker 1: Are they just going to wait us out until the 477 00:29:05,720 --> 00:29:09,400 Speaker 1: next president comes along? Or what's there? What's their approach 478 00:29:09,600 --> 00:29:13,760 Speaker 1: from your perspective, Well, they're probably a little puzzled, because 479 00:29:13,800 --> 00:29:16,520 Speaker 1: I think it's hard for them, as and and many 480 00:29:16,600 --> 00:29:20,360 Speaker 1: of us to figure out what with any precision the 481 00:29:20,400 --> 00:29:23,120 Speaker 1: current administration in the United States is really after. It 482 00:29:23,120 --> 00:29:25,520 Speaker 1: seems to be a bit of a moving target. But no, 483 00:29:25,640 --> 00:29:28,200 Speaker 1: I don't think they'll I don't think they'll just definitively 484 00:29:28,280 --> 00:29:30,520 Speaker 1: wait it out. They'll see if they can make agreements 485 00:29:30,560 --> 00:29:34,680 Speaker 1: where it looks like it's sensible from both mutually beneficial 486 00:29:35,200 --> 00:29:38,560 Speaker 1: to make an agreement. We have been speaking with Michael Spence. 487 00:29:38,640 --> 00:29:40,760 Speaker 1: He is a professor of economics at the n y 488 00:29:40,880 --> 00:29:44,720 Speaker 1: U Stern School of Business. If you enjoy this conversation, 489 00:29:45,040 --> 00:29:47,280 Speaker 1: we'll be sure and come back for the podcast. Extras 490 00:29:47,280 --> 00:29:50,880 Speaker 1: where we keep the tape rolling and continue discussing all 491 00:29:50,960 --> 00:29:55,560 Speaker 1: things information structure related. You can find that at iTunes, 492 00:29:55,680 --> 00:30:00,120 Speaker 1: Google Podcasts. That's your Spotify, wherever your finer podcasts are own. 493 00:30:00,800 --> 00:30:04,160 Speaker 1: We love your comments, feedback and suggestions right to us 494 00:30:04,280 --> 00:30:07,560 Speaker 1: at m IB podcast at Bloomberg dot net. Give us 495 00:30:07,600 --> 00:30:10,600 Speaker 1: a review on Apple iTunes. You could check out my 496 00:30:10,720 --> 00:30:14,120 Speaker 1: weekly column on Bloomberg dot com. Sign up for my 497 00:30:14,280 --> 00:30:18,080 Speaker 1: daily reads at Redholts dot com. I'm Barry Hults. You're 498 00:30:18,120 --> 00:30:24,920 Speaker 1: listening to Masters and Business on Bloomberg Radio. Welcome to 499 00:30:24,920 --> 00:30:27,240 Speaker 1: the podcast, Michael, Thank you so much for doing this. 500 00:30:27,240 --> 00:30:32,040 Speaker 1: This is really fascinating stuff. I am absolutely intrigued and 501 00:30:32,040 --> 00:30:34,280 Speaker 1: and when you talk about I don't want to get 502 00:30:34,280 --> 00:30:38,200 Speaker 1: too wonking. No, no, these these listeners love going into 503 00:30:38,200 --> 00:30:42,440 Speaker 1: the wonky weeds. Um. There's a couple of things, both 504 00:30:42,480 --> 00:30:45,320 Speaker 1: from the book and some of your other writings that 505 00:30:45,400 --> 00:30:47,960 Speaker 1: we didn't get to that I want to discuss. But 506 00:30:48,080 --> 00:30:51,960 Speaker 1: first I was mentioning during the break that I thought 507 00:30:52,040 --> 00:30:56,160 Speaker 1: the book really has has held up very well. It 508 00:30:56,400 --> 00:30:59,240 Speaker 1: there's nothing in it that's outdated. What are you thinking 509 00:30:59,280 --> 00:31:02,680 Speaker 1: about for your next book? Well, on the it takes 510 00:31:02,680 --> 00:31:05,680 Speaker 1: about a decade to recover from writing a book. That's 511 00:31:05,720 --> 00:31:09,320 Speaker 1: my experience. Ten years later, you're almost ready to almost 512 00:31:09,360 --> 00:31:12,120 Speaker 1: ready to start. Yeah, no, I I mean on the 513 00:31:12,160 --> 00:31:16,880 Speaker 1: development side, Verry, I would say the biggest potential shift 514 00:31:17,680 --> 00:31:20,520 Speaker 1: is it does come from the digital technology side. So 515 00:31:20,680 --> 00:31:24,480 Speaker 1: you know, you have we are either at or very 516 00:31:24,520 --> 00:31:27,080 Speaker 1: close to the point where the digital technologies, which you 517 00:31:27,120 --> 00:31:29,880 Speaker 1: know how kind of high fixed, very little variable cost 518 00:31:30,400 --> 00:31:36,800 Speaker 1: technology software replication, zero marginal cost, etcetera, basically overtake the 519 00:31:36,880 --> 00:31:41,840 Speaker 1: labor intensive ones replace. But that undercuts a very important 520 00:31:41,880 --> 00:31:44,680 Speaker 1: part of the growth model that we saw being used 521 00:31:44,760 --> 00:31:50,160 Speaker 1: essentially in every country that was really successful. Jobs. Yeah, jobs, 522 00:31:50,240 --> 00:31:52,880 Speaker 1: what are you gonna do? What's so you've got to 523 00:31:52,920 --> 00:31:55,479 Speaker 1: sell something, a good or a service to the global 524 00:31:55,520 --> 00:31:58,520 Speaker 1: economy that that part hasn't gone away? The question is 525 00:31:59,040 --> 00:32:03,120 Speaker 1: if it's not to ways and assembled electronics and stuff 526 00:32:03,480 --> 00:32:07,720 Speaker 1: and shirts and uh, textiles and apparel is the starting 527 00:32:07,760 --> 00:32:09,520 Speaker 1: point for most of these places, then what is it? 528 00:32:09,840 --> 00:32:12,920 Speaker 1: And all that stuff has gone robotic? Now is going robotic? 529 00:32:13,160 --> 00:32:18,080 Speaker 1: You know so? I mean it doesn't happen overnight, but um, 530 00:32:18,200 --> 00:32:21,080 Speaker 1: but it's happening pretty fast. I mean, you know, so 531 00:32:22,320 --> 00:32:25,840 Speaker 1: textiles is difficult because the materials soft and the robots 532 00:32:25,840 --> 00:32:28,560 Speaker 1: have trouble kind of And we actually go talk to 533 00:32:28,600 --> 00:32:30,320 Speaker 1: the people who are trying to automate this, they say, 534 00:32:30,320 --> 00:32:33,320 Speaker 1: we're not quite there yet. Um, who can't sew the 535 00:32:34,240 --> 00:32:38,720 Speaker 1: stitching straight on on your shirt yet? But it's not 536 00:32:38,880 --> 00:32:42,560 Speaker 1: that far away. So so there's a real question about 537 00:32:43,560 --> 00:32:46,880 Speaker 1: um this growth model. And one of the things that 538 00:32:46,880 --> 00:32:48,600 Speaker 1: that part of the answer is probably going to turn 539 00:32:48,680 --> 00:32:51,480 Speaker 1: out to be another aspect of digital technology, which is 540 00:32:51,480 --> 00:32:57,800 Speaker 1: you can create ecosystems, platform centered ecosystems that enable entrepreneurs 541 00:32:57,840 --> 00:33:01,840 Speaker 1: to create businesses and employee people poll and whatnot and 542 00:33:01,440 --> 00:33:05,200 Speaker 1: and I'm sorry, And an international version of that would 543 00:33:05,280 --> 00:33:09,000 Speaker 1: be moving in the direction of a partial substitute. So 544 00:33:09,320 --> 00:33:12,160 Speaker 1: when we talk about inequality, we used to talk about 545 00:33:12,240 --> 00:33:18,000 Speaker 1: developed world inequality versus emerging markets or undeveloped world. Now, 546 00:33:18,720 --> 00:33:23,040 Speaker 1: thanks in part to digital even within developed worlds, there's 547 00:33:23,320 --> 00:33:27,120 Speaker 1: inequality that has apparently risen to levels we haven't seen 548 00:33:27,480 --> 00:33:30,800 Speaker 1: for a few generations. How much of this is based 549 00:33:30,840 --> 00:33:34,800 Speaker 1: on information asymmetries and information gaps, and how much of 550 00:33:34,840 --> 00:33:39,360 Speaker 1: this is just the nature of capital and a sort 551 00:33:39,400 --> 00:33:42,360 Speaker 1: of winner take all system that seems to have been 552 00:33:43,120 --> 00:33:46,720 Speaker 1: evolved over the past few decades. Yeah, I think it's 553 00:33:46,800 --> 00:33:51,640 Speaker 1: more the latter. I mean, I think it's more a 554 00:33:51,640 --> 00:33:57,640 Speaker 1: combination of globalization and the evolution of technology have since 555 00:33:57,680 --> 00:34:02,160 Speaker 1: about the late seventies has based sickly reverse relatively benign 556 00:34:02,200 --> 00:34:05,720 Speaker 1: growth patterns with respected distribution. So they the whole thing 557 00:34:05,760 --> 00:34:10,200 Speaker 1: went started to go south, let's say around and you know, 558 00:34:10,239 --> 00:34:12,319 Speaker 1: we have big changes in approach. I mean, that was 559 00:34:12,400 --> 00:34:15,800 Speaker 1: the that was the Reagan Thatcher era, right, So we 560 00:34:15,920 --> 00:34:18,680 Speaker 1: probably had some deregulation and other things that might have 561 00:34:18,719 --> 00:34:22,359 Speaker 1: contributed to that. But but basically, you know, you've got 562 00:34:23,719 --> 00:34:27,800 Speaker 1: something that's by most people's standards, just gotten out of control. 563 00:34:28,560 --> 00:34:31,840 Speaker 1: And and of course the you know, because so income, 564 00:34:32,480 --> 00:34:35,480 Speaker 1: if you have income rising income inequality for long enough, 565 00:34:35,560 --> 00:34:39,520 Speaker 1: then it then it's on the wealth side, it's self perpetuating. 566 00:34:39,960 --> 00:34:41,680 Speaker 1: Once you get to a critical mass of wealth, you 567 00:34:41,760 --> 00:34:45,239 Speaker 1: should theoretically retain it. Yeah, in theory, unless you make 568 00:34:45,320 --> 00:34:48,440 Speaker 1: big mistakes. Well that's the old shirt. What is it? 569 00:34:48,480 --> 00:34:51,960 Speaker 1: Shorts leaves to shorts leaves in in three generations. So 570 00:34:51,960 --> 00:34:55,480 Speaker 1: so that raises some really interesting questions. We've always had 571 00:34:55,680 --> 00:34:59,200 Speaker 1: income inequality and wealth inequality. It seems to be inevitable 572 00:34:59,640 --> 00:35:03,040 Speaker 1: um in capitalism. What what you're what I'm getting a 573 00:35:03,080 --> 00:35:07,000 Speaker 1: sense that you're referring to is when they reach extremes 574 00:35:07,040 --> 00:35:10,640 Speaker 1: to the point where, uh, it threatens the social order? 575 00:35:10,680 --> 00:35:14,160 Speaker 1: Are we remotely close to that anywhere around the world. 576 00:35:14,239 --> 00:35:16,840 Speaker 1: We look at the EU and the UK and the US. 577 00:35:17,280 --> 00:35:21,359 Speaker 1: It seems there's some signs of unrest, but nothing like 578 00:35:21,600 --> 00:35:25,520 Speaker 1: France before their revolution. No. I mean, we're probably not 579 00:35:25,600 --> 00:35:27,480 Speaker 1: at that point yet, but we're pretty late in the 580 00:35:27,520 --> 00:35:30,279 Speaker 1: game because I think that the people who have been 581 00:35:30,320 --> 00:35:33,960 Speaker 1: sideswiped by the way our economies have evolved, it's a 582 00:35:33,960 --> 00:35:37,240 Speaker 1: pretty large group and they're pretty angry, in part because 583 00:35:38,160 --> 00:35:41,080 Speaker 1: it is my conjecture anyway, because we didn't do anything 584 00:35:41,080 --> 00:35:43,360 Speaker 1: about it. Right, Well, what are we supposed to do 585 00:35:43,440 --> 00:35:45,799 Speaker 1: about So let's back up a set, because that's a 586 00:35:45,840 --> 00:35:50,879 Speaker 1: fascinating subject. We've had globalization for a long time. There's 587 00:35:50,920 --> 00:35:55,960 Speaker 1: a reason why my iPhone is more powerful than what 588 00:35:56,040 --> 00:36:00,480 Speaker 1: took us to the moon forty five years ago and 589 00:36:00,840 --> 00:36:04,840 Speaker 1: plus thirty or forty a month. It's because of globalization. 590 00:36:04,880 --> 00:36:09,040 Speaker 1: It's because of automation and technology. It's given us a 591 00:36:09,120 --> 00:36:13,600 Speaker 1: higher standard of living on average. But there are clearly 592 00:36:13,840 --> 00:36:17,719 Speaker 1: winners and losers from the decline of unions, the rise 593 00:36:17,760 --> 00:36:21,480 Speaker 1: of globalization, and the increase of automation. What are we 594 00:36:21,600 --> 00:36:27,000 Speaker 1: not doing to moderate the negative impacts of that? Well, 595 00:36:27,040 --> 00:36:30,920 Speaker 1: I mean, you know, probably not using the potential for 596 00:36:31,080 --> 00:36:36,600 Speaker 1: progressive taxation on that. I'm not suggesting that's the whole answer, 597 00:36:36,640 --> 00:36:39,520 Speaker 1: but you know, but it probably needs to be part 598 00:36:39,520 --> 00:36:44,080 Speaker 1: of the answer. You know, there's there's a I think 599 00:36:44,080 --> 00:36:46,120 Speaker 1: one of the reasons is coming into focus now is 600 00:36:46,160 --> 00:36:49,040 Speaker 1: there's a bunch of really good research, you know, with 601 00:36:49,120 --> 00:36:54,040 Speaker 1: a long time arise and picket E Sayez you know Zookman, 602 00:36:54,760 --> 00:36:58,000 Speaker 1: Chatty rad Chetty at Stanford, you know that are bringing 603 00:36:58,000 --> 00:37:00,640 Speaker 1: out dimensions of this and you know, so it comes 604 00:37:00,719 --> 00:37:03,600 Speaker 1: in cycles, right, uh So, in no ways this has 605 00:37:03,640 --> 00:37:05,800 Speaker 1: been here for a while, but now we really understand 606 00:37:05,800 --> 00:37:09,080 Speaker 1: it better. We understand it better and understand the history better, 607 00:37:09,680 --> 00:37:13,480 Speaker 1: and and now are kind of rapp grappling with the responses. So, 608 00:37:13,560 --> 00:37:17,239 Speaker 1: I mean, we haven't had a presidential primary season where 609 00:37:17,280 --> 00:37:19,680 Speaker 1: people are talking about wealth taxes for a long time. 610 00:37:20,239 --> 00:37:23,520 Speaker 1: Did we previously have this? This goes back post depression 611 00:37:23,680 --> 00:37:28,240 Speaker 1: or around that era. Uh I don't know the history 612 00:37:28,239 --> 00:37:30,080 Speaker 1: well enough to be able to answer that whether we 613 00:37:30,120 --> 00:37:35,440 Speaker 1: had an actual discussion of wealth but this but it's 614 00:37:35,640 --> 00:37:37,680 Speaker 1: it's new in the post war era. I guess that's 615 00:37:37,719 --> 00:37:42,359 Speaker 1: the way I would say it. Um And but we're 616 00:37:42,400 --> 00:37:44,560 Speaker 1: in the early stages of kind of thinking this through. 617 00:37:44,600 --> 00:37:46,759 Speaker 1: I think the other thing that we learned from a 618 00:37:46,760 --> 00:37:49,879 Speaker 1: wide range of both developed and developing countries is that 619 00:37:51,360 --> 00:37:53,160 Speaker 1: is that one of the things you want to So 620 00:37:53,600 --> 00:37:56,319 Speaker 1: there's there's two there's two ways to think about inequality. 621 00:37:56,360 --> 00:37:59,720 Speaker 1: There's what economists call x post that's what actually happened. 622 00:38:00,280 --> 00:38:02,560 Speaker 1: And on that front, I think most people agree with 623 00:38:02,600 --> 00:38:05,720 Speaker 1: what you just said, which is it's okay. People understand 624 00:38:05,800 --> 00:38:07,680 Speaker 1: we're not going to all be you know, have the 625 00:38:07,760 --> 00:38:10,120 Speaker 1: same incomes and stuff, but it can get out of hand. 626 00:38:10,280 --> 00:38:14,000 Speaker 1: The extremes are not okay. And then there's what Americans 627 00:38:14,040 --> 00:38:17,000 Speaker 1: call it quality of opportunity, which is exciting. That is, 628 00:38:17,040 --> 00:38:19,760 Speaker 1: do you have a fair shot on a level playing 629 00:38:19,800 --> 00:38:23,080 Speaker 1: field at whatever this distribution out there is. And people 630 00:38:23,120 --> 00:38:27,520 Speaker 1: have been complaining that's been what's been contracted. They are 631 00:38:27,680 --> 00:38:31,200 Speaker 1: and and it's connected. You know, if if the income 632 00:38:31,239 --> 00:38:34,560 Speaker 1: in equality gets sufficiently extreme, then the lower end of 633 00:38:34,600 --> 00:38:37,440 Speaker 1: the spectrum doesn't have the resources to invest in getting 634 00:38:37,440 --> 00:38:41,360 Speaker 1: to that playing field, which is probably why this whole 635 00:38:41,400 --> 00:38:44,839 Speaker 1: IVY league, um, you know, pay extra money to get 636 00:38:44,840 --> 00:38:48,600 Speaker 1: your kids in, has has resonated so much and outrage 637 00:38:48,640 --> 00:38:52,360 Speaker 1: people so much. Have they been under illusion that there's 638 00:38:52,440 --> 00:38:56,520 Speaker 1: an equality of opportunity or was there genuinely equality of 639 00:38:56,560 --> 00:39:01,200 Speaker 1: opportunity for for most of our history? No? I mean, 640 00:39:03,280 --> 00:39:08,200 Speaker 1: relatively speaking, I think yes, but not perfect the quality 641 00:39:08,239 --> 00:39:12,719 Speaker 1: of opportunity, because that's unachievable that you know, the this 642 00:39:13,160 --> 00:39:16,440 Speaker 1: um gosh, we've got to get into USC or Harvard 643 00:39:16,520 --> 00:39:20,120 Speaker 1: or whatever. Stanford puzzles me. I mean, I've always thought, 644 00:39:20,280 --> 00:39:23,680 Speaker 1: you know, I grew up in Canada. Canada is more 645 00:39:23,760 --> 00:39:27,279 Speaker 1: like other countries in that there's a most of the 646 00:39:27,520 --> 00:39:32,359 Speaker 1: higher educational institutions are publicly largely publicly funded, and you know, 647 00:39:32,480 --> 00:39:33,960 Speaker 1: and there's a few of them, and you know, it 648 00:39:34,040 --> 00:39:37,200 Speaker 1: really matters if you get into the right ones. In America, 649 00:39:37,880 --> 00:39:41,640 Speaker 1: we've got hundreds, We've got public and private institutions. We've 650 00:39:41,680 --> 00:39:44,839 Speaker 1: got all kinds of really top flight colleges, and a 651 00:39:44,840 --> 00:39:47,080 Speaker 1: lot of your friends of mine went to these colleges 652 00:39:47,120 --> 00:39:48,759 Speaker 1: and whatnot. I went to a state school here in 653 00:39:48,800 --> 00:39:53,799 Speaker 1: New York, and I don't quite understand what the kind 654 00:39:53,800 --> 00:39:56,800 Speaker 1: of you know where where this you know self impost 655 00:39:56,840 --> 00:40:01,439 Speaker 1: pressure to get into these you know eat institutions comes from. 656 00:40:01,480 --> 00:40:05,560 Speaker 1: Because my sense is a kid going to anyone of 657 00:40:05,600 --> 00:40:09,200 Speaker 1: a huge range of institutions has a pretty good running 658 00:40:09,200 --> 00:40:12,440 Speaker 1: shot at a pretty well let me let me push 659 00:40:12,440 --> 00:40:15,880 Speaker 1: back at you. You went to Princeton, you went to Oxford, 660 00:40:15,920 --> 00:40:18,640 Speaker 1: you went to Harvard. There's not a slouch and that 661 00:40:18,880 --> 00:40:21,200 Speaker 1: you talked toward a Stanford teach an n y U. 662 00:40:21,920 --> 00:40:27,279 Speaker 1: You've been affiliated with fairly elite institutions. Are you suggesting 663 00:40:27,360 --> 00:40:30,160 Speaker 1: that you could go to a I don't want to say, 664 00:40:30,200 --> 00:40:32,880 Speaker 1: a lower to your school, but next to your school 665 00:40:33,239 --> 00:40:35,759 Speaker 1: and still have the same sorts of opportunities that you 666 00:40:35,760 --> 00:40:38,560 Speaker 1: would get at the best of the best of the best. 667 00:40:38,719 --> 00:40:42,800 Speaker 1: Is that the concern from so many people, the snowplow 668 00:40:42,880 --> 00:40:45,960 Speaker 1: parents want to remove every obstacle to their kids success. 669 00:40:46,920 --> 00:40:49,520 Speaker 1: I think, what, let me, I'm gonna guess what they're thinking. 670 00:40:50,200 --> 00:40:52,360 Speaker 1: So one of the benefits you get from going to 671 00:40:52,440 --> 00:40:56,160 Speaker 1: these schools is the education and the signal the others 672 00:40:56,200 --> 00:40:59,480 Speaker 1: the network of course, Okay, and so if you believe 673 00:40:59,560 --> 00:41:03,759 Speaker 1: the net work is crucial, then and that that's the 674 00:41:03,800 --> 00:41:07,399 Speaker 1: main thing. Then then I can start to understand. So 675 00:41:07,920 --> 00:41:12,399 Speaker 1: you know that the Princeton network in New York or 676 00:41:12,440 --> 00:41:16,120 Speaker 1: the Yale network in New York and Washington, maybe you 677 00:41:16,200 --> 00:41:19,200 Speaker 1: know you're desperate somehow to make sure your kid is 678 00:41:19,680 --> 00:41:22,879 Speaker 1: part of that, and the opportunities that that that opens up. 679 00:41:22,920 --> 00:41:27,759 Speaker 1: That that's the part that that I think is um 680 00:41:27,800 --> 00:41:31,360 Speaker 1: perhaps real, but it's also worrying, right, I mean, you 681 00:41:31,440 --> 00:41:35,160 Speaker 1: don't want to think that, uh, if you're out of 682 00:41:35,200 --> 00:41:39,160 Speaker 1: any of those networks or all of them, that the 683 00:41:39,239 --> 00:41:42,160 Speaker 1: meritocracy has failed to a point where you know, you 684 00:41:42,239 --> 00:41:45,320 Speaker 1: don't have access to the top government jobs or whatever. 685 00:41:46,000 --> 00:41:49,600 Speaker 1: So to the extent that's true and that the parents 686 00:41:49,600 --> 00:41:52,200 Speaker 1: are right, then I think we have to start worrying 687 00:41:52,239 --> 00:41:57,640 Speaker 1: about about this dimension of equality of opportunity. So so 688 00:41:58,239 --> 00:42:00,360 Speaker 1: on that note, let me ask you some questions that 689 00:42:00,400 --> 00:42:03,120 Speaker 1: didn't get to that I think are relevant. One of 690 00:42:03,200 --> 00:42:05,880 Speaker 1: the quotes in the book, um, and I don't want 691 00:42:05,880 --> 00:42:10,319 Speaker 1: to mangle this adversity is a is surprisingly awesome in 692 00:42:10,360 --> 00:42:14,720 Speaker 1: the birthplace of successful change. What what is it about 693 00:42:14,760 --> 00:42:18,799 Speaker 1: adversity that leads to change? And you also talk about 694 00:42:18,920 --> 00:42:23,840 Speaker 1: the change in dynamics during a crisis where the entrenched 695 00:42:23,880 --> 00:42:28,319 Speaker 1: interests lose a lot of their hold on on power. Yeah, 696 00:42:28,400 --> 00:42:31,799 Speaker 1: so it's not a sure thing that a crisis produces 697 00:42:31,840 --> 00:42:36,239 Speaker 1: good results, but it does create at least an opportunity 698 00:42:36,280 --> 00:42:42,240 Speaker 1: because it essentially weakens the vested interest power to maintain 699 00:42:42,320 --> 00:42:47,920 Speaker 1: the status quo. And that's why you get routine statements 700 00:42:48,000 --> 00:42:52,560 Speaker 1: like never waste a crisis, you know, etcetera um. And 701 00:42:52,640 --> 00:42:54,799 Speaker 1: there are examples. I mean, you know, one of the 702 00:42:54,880 --> 00:42:59,640 Speaker 1: members of that commission, what is a Turkish citizen at 703 00:42:59,680 --> 00:43:03,440 Speaker 1: least at the World Bank and now elsewhere, you know, 704 00:43:03,760 --> 00:43:06,880 Speaker 1: was finance minister when Turkey at a crisis and was 705 00:43:06,920 --> 00:43:09,440 Speaker 1: able to put through some reforms that you know, arguably 706 00:43:09,480 --> 00:43:12,520 Speaker 1: just you couldn't do in quote normal times. Well, a 707 00:43:12,520 --> 00:43:16,919 Speaker 1: lot of the post Depression Great Crash era reforms we've 708 00:43:16,960 --> 00:43:20,280 Speaker 1: never seen anything like before or since in the United States. 709 00:43:20,480 --> 00:43:22,600 Speaker 1: One of one of my favorite comparisons in the book 710 00:43:23,239 --> 00:43:27,480 Speaker 1: Singapore versus Cuba. Here are two countries relatively the same 711 00:43:27,520 --> 00:43:33,600 Speaker 1: size to island nations, similar populations, enormous different in enormous 712 00:43:33,640 --> 00:43:39,280 Speaker 1: differences in economic outcomes. What what explains those differences, Well, 713 00:43:40,600 --> 00:43:44,560 Speaker 1: it's basically the choice of this It's the development strategy 714 00:43:44,880 --> 00:43:50,239 Speaker 1: that that explains the difference. So um, I mean there's 715 00:43:50,239 --> 00:43:52,200 Speaker 1: a kind of literature and you know that you know, 716 00:43:52,800 --> 00:43:56,160 Speaker 1: goes back and forth between policies and institutions on the 717 00:43:56,200 --> 00:43:59,759 Speaker 1: development literature, and I think the sensible sort of a 718 00:43:59,760 --> 00:44:04,759 Speaker 1: set that his institutions do matter, but policies matter as well. 719 00:44:05,400 --> 00:44:13,360 Speaker 1: Singapore basically was relatively autocratic. But they were pretty clever. Uh. Well, 720 00:44:13,840 --> 00:44:17,200 Speaker 1: for example, you know, they figured out early on, first 721 00:44:17,200 --> 00:44:20,759 Speaker 1: of all, they have a multi ethnic structure, so they 722 00:44:20,760 --> 00:44:27,640 Speaker 1: have Malaise and Chinese and Indians, and they they probably leakwan. 723 00:44:27,719 --> 00:44:29,799 Speaker 1: You figured out that if those people got at each 724 00:44:29,800 --> 00:44:32,800 Speaker 1: other's throats that you know, would kind of they'd place apart. 725 00:44:33,280 --> 00:44:35,760 Speaker 1: So they set out to get to make the growth 726 00:44:35,760 --> 00:44:39,960 Speaker 1: patterns inclusive really from the get go, and anybody who 727 00:44:40,000 --> 00:44:43,440 Speaker 1: was not on board and that was simply marginalized kicked 728 00:44:43,440 --> 00:44:46,800 Speaker 1: out um. And they and they figured the most important 729 00:44:46,800 --> 00:44:51,120 Speaker 1: part of that was housing. Really they went after housing. UM. 730 00:44:51,160 --> 00:44:54,840 Speaker 1: So housing. The two critical elements were housing and education. 731 00:44:54,920 --> 00:44:58,200 Speaker 1: So the education is stunningly good, um over a long 732 00:44:58,239 --> 00:45:01,200 Speaker 1: period of time, and housing is subsidize so you don't 733 00:45:01,239 --> 00:45:03,880 Speaker 1: really ever have a problem with you know, where you're 734 00:45:03,880 --> 00:45:07,120 Speaker 1: going to live. And whether it's affordable and stuff like that. Huh. 735 00:45:07,360 --> 00:45:09,759 Speaker 1: And then they left things like saving free your retirement 736 00:45:09,840 --> 00:45:11,480 Speaker 1: up more or less up to you. So there's not 737 00:45:11,520 --> 00:45:15,319 Speaker 1: a lot of pension big liabilities and pension funds. But 738 00:45:15,400 --> 00:45:18,319 Speaker 1: they but that crucial piece they got right there. They 739 00:45:18,360 --> 00:45:21,880 Speaker 1: did one other thing, which is I asked, uh a 740 00:45:21,960 --> 00:45:24,560 Speaker 1: senior person who was a partner of Lee Kuan you 741 00:45:24,640 --> 00:45:26,480 Speaker 1: in the early days of development. I said, well, it 742 00:45:26,560 --> 00:45:29,799 Speaker 1: was the secret to success and in sing Pore. And 743 00:45:29,800 --> 00:45:31,279 Speaker 1: he said, well, there were two things. He was a 744 00:45:31,320 --> 00:45:33,040 Speaker 1: little this is a little tongue in cheek, but because 745 00:45:33,040 --> 00:45:34,400 Speaker 1: there were a lot of things. But he said there 746 00:45:34,400 --> 00:45:37,840 Speaker 1: were two things. One, we were really harsh on corruption. 747 00:45:38,160 --> 00:45:41,680 Speaker 1: We basically stamped it out. Uh. So we we took 748 00:45:41,719 --> 00:45:44,719 Speaker 1: care of that problem. Also, rule law really matters. Yeah, 749 00:45:44,880 --> 00:45:47,920 Speaker 1: rule of law in the sense of you know that 750 00:45:47,960 --> 00:45:49,880 Speaker 1: part that has to do with corruption, you know, a 751 00:45:49,960 --> 00:45:54,560 Speaker 1: civil servant is it's just got to be clean. Um. 752 00:45:54,719 --> 00:45:56,960 Speaker 1: And when and when there was a violation of that, 753 00:45:57,000 --> 00:46:00,000 Speaker 1: we really stamped hard. And so I said, I understan 754 00:46:00,000 --> 00:46:03,200 Speaker 1: and that part and and they said the second part 755 00:46:03,239 --> 00:46:07,200 Speaker 1: was luck really yeah, they acknowledged that, And so I said, 756 00:46:07,200 --> 00:46:10,520 Speaker 1: what do you mean and he said, well, uh, he said, 757 00:46:10,800 --> 00:46:15,560 Speaker 1: this this isn't really luck. This is sort of pragmatic opportunism, 758 00:46:15,600 --> 00:46:19,000 Speaker 1: you know, responding to things that you can't anticipate in advance, 759 00:46:19,000 --> 00:46:23,120 Speaker 1: so which sometimes is indistinguishable from luck. It was indistinguishable. 760 00:46:23,120 --> 00:46:26,400 Speaker 1: And in this case what it was was in the 761 00:46:26,440 --> 00:46:29,680 Speaker 1: post war period and during the Cold War, UM, a 762 00:46:29,800 --> 00:46:33,200 Speaker 1: system that came to be called the multi fiber agreement 763 00:46:33,200 --> 00:46:35,680 Speaker 1: was set up. And what it was was an attempt 764 00:46:35,680 --> 00:46:39,960 Speaker 1: to make sure that the textiles and apparel industry globally 765 00:46:40,280 --> 00:46:44,840 Speaker 1: was spread out across a bunch of countries and because 766 00:46:44,840 --> 00:46:47,800 Speaker 1: they wanted those countries to thrive and stay on quote 767 00:46:47,800 --> 00:46:52,480 Speaker 1: our side, um, And so there were quotas basically, and 768 00:46:52,719 --> 00:46:57,560 Speaker 1: an early major center of textile manufacturer was Hong Kong, 769 00:46:58,680 --> 00:47:02,479 Speaker 1: and they hit the quota, and the entrepreneurs in Hong Kong, 770 00:47:02,520 --> 00:47:05,879 Speaker 1: who are no slouches, started looking around the world, Hey 771 00:47:05,960 --> 00:47:09,359 Speaker 1: let's go over there. Yeah, and Singapore went, well, well 772 00:47:09,440 --> 00:47:12,799 Speaker 1: we'll do that. Um. That's what we meant by luck, 773 00:47:13,040 --> 00:47:15,680 Speaker 1: you know. So, I mean you don't plan to have 774 00:47:15,719 --> 00:47:18,440 Speaker 1: a multi fiber agreement where you know, the major player 775 00:47:18,520 --> 00:47:21,359 Speaker 1: hits the quota and you're just about ready to take 776 00:47:21,400 --> 00:47:23,640 Speaker 1: it on. But but it's but they're really not a 777 00:47:23,680 --> 00:47:27,800 Speaker 1: big text style not manufacturer anymore. They very quickly morphed 778 00:47:27,840 --> 00:47:32,279 Speaker 1: towards technology and another industry. So you don't see this 779 00:47:32,440 --> 00:47:34,800 Speaker 1: much in Hong Kong. You don't see it much in Singapore. 780 00:47:35,440 --> 00:47:38,239 Speaker 1: You know, the the handoff process is interesting. So it went, 781 00:47:38,600 --> 00:47:43,840 Speaker 1: it went to Korea, uh, and to some extent that Taiwan, 782 00:47:44,080 --> 00:47:46,080 Speaker 1: and then their their incomes rose to the point and 783 00:47:46,080 --> 00:47:48,520 Speaker 1: they had to move again, moved to China. Some of 784 00:47:48,520 --> 00:47:53,640 Speaker 1: it was in Indonesia. Um for a while, Vietnam for sure. Um, 785 00:47:53,719 --> 00:47:56,080 Speaker 1: now China is handing it off again. I mean this 786 00:47:57,480 --> 00:48:00,960 Speaker 1: passing the baton is a natural part of the dynamics. 787 00:48:01,560 --> 00:48:03,600 Speaker 1: I have a friend who used to be located in 788 00:48:03,640 --> 00:48:07,240 Speaker 1: San Diego and ended up in Vietnam, and he said, 789 00:48:07,440 --> 00:48:11,719 Speaker 1: Vietnam is today the wild West of capitalism, the just 790 00:48:11,800 --> 00:48:16,440 Speaker 1: the purest expression of Uh. Let's try an I d 791 00:48:16,560 --> 00:48:18,839 Speaker 1: and see where it goes. What do you see as 792 00:48:18,960 --> 00:48:24,400 Speaker 1: Vietnam's future in terms of future convergence? Al Right, you 793 00:48:24,440 --> 00:48:27,040 Speaker 1: know they haven't quite hit the seven percent club mark. 794 00:48:27,160 --> 00:48:31,360 Speaker 1: But um, but it's basically they were, They're part of 795 00:48:31,400 --> 00:48:35,560 Speaker 1: the process. They're on the way. I mean, if you 796 00:48:35,760 --> 00:48:38,120 Speaker 1: wanted to be skeptical, what you would say, Well, the 797 00:48:38,200 --> 00:48:41,200 Speaker 1: tensions in the South China Sea area are sufficiently high, 798 00:48:41,239 --> 00:48:43,640 Speaker 1: they'll get in a fight with China and something bat 799 00:48:43,719 --> 00:48:46,040 Speaker 1: will happen or something like that. But on at least 800 00:48:46,040 --> 00:48:50,640 Speaker 1: on the economic grounds, I don't see any reason why, uh, 801 00:48:51,080 --> 00:48:54,840 Speaker 1: you know, Vietnam won't be continue to become more and 802 00:48:54,880 --> 00:48:59,000 Speaker 1: more prosperous. So you recently wrote about three mega trends 803 00:48:59,120 --> 00:49:03,480 Speaker 1: driving structure rules shifts, uh, digital transformation, which we talked 804 00:49:03,480 --> 00:49:09,080 Speaker 1: about growing em purchasing power, and then rising nationalism and popularism. 805 00:49:09,080 --> 00:49:13,560 Speaker 1: How are these three all colliding? So I think that 806 00:49:13,680 --> 00:49:17,560 Speaker 1: they're connected with each other. So you have so the 807 00:49:17,600 --> 00:49:21,520 Speaker 1: digital I think we understand it. It's it's not one thing, 808 00:49:21,560 --> 00:49:26,000 Speaker 1: it's many things. So it has um the potential to 809 00:49:26,080 --> 00:49:30,480 Speaker 1: generate benign and highly inclusive growth patterns. But it but 810 00:49:30,600 --> 00:49:33,760 Speaker 1: it will give rise to difficult transitions as people retrain 811 00:49:34,320 --> 00:49:37,040 Speaker 1: to do some different things. So that that's kind of 812 00:49:37,040 --> 00:49:40,920 Speaker 1: coming into focus. Um, and and we're just gonna have 813 00:49:41,000 --> 00:49:44,640 Speaker 1: to sort of amplify the benign part and and deal 814 00:49:44,680 --> 00:49:48,480 Speaker 1: as best we can with the other part. Second, the 815 00:49:48,600 --> 00:49:52,000 Speaker 1: rise of these emerging economies means it powerful, which means 816 00:49:52,080 --> 00:49:55,040 Speaker 1: that the governance structure of the global economy, which for 817 00:49:55,200 --> 00:49:58,879 Speaker 1: many many years was essentially the G seven in terms 818 00:49:58,920 --> 00:50:02,520 Speaker 1: of priorities, this isn't to work anymore. Now it's CLO's 819 00:50:02,560 --> 00:50:05,760 Speaker 1: G twenty and the G twenty is more heterogy, heterogeneous 820 00:50:05,880 --> 00:50:07,839 Speaker 1: and harder. It's harder for them to kind of reach 821 00:50:07,920 --> 00:50:11,840 Speaker 1: conclusions that you know, aren't just milk toast um and stuff. 822 00:50:11,920 --> 00:50:13,960 Speaker 1: And so we're getting we have a kind of the 823 00:50:14,520 --> 00:50:18,040 Speaker 1: part of the consequence of the rise of the emerging 824 00:50:18,080 --> 00:50:21,920 Speaker 1: economies and Asia is a kind of set of centrifugal 825 00:50:21,920 --> 00:50:26,040 Speaker 1: forces with respect to governance and whatnot. Um and then 826 00:50:26,600 --> 00:50:32,919 Speaker 1: and then you know this partly economic, partly social phenomenon 827 00:50:33,560 --> 00:50:36,319 Speaker 1: that I think people are seriously studying, but I don't 828 00:50:36,320 --> 00:50:39,600 Speaker 1: think it's perfectly understood, which is people are really aren't 829 00:50:39,640 --> 00:50:44,120 Speaker 1: comfortable living in a world where where the unit is 830 00:50:44,200 --> 00:50:48,120 Speaker 1: designed is called the global economy, right, They just aren't. 831 00:50:48,640 --> 00:50:51,080 Speaker 1: And some of it's kind of they get sideswiped into 832 00:50:51,200 --> 00:50:54,520 Speaker 1: economic terms, and some of it is culture, you know, 833 00:50:54,719 --> 00:50:57,600 Speaker 1: I mean, we're not it's also history from all human 834 00:50:57,640 --> 00:51:01,400 Speaker 1: history except the last half century. It was always local. 835 00:51:01,480 --> 00:51:04,200 Speaker 1: Maybe it was regional at most, regional at most. So 836 00:51:04,480 --> 00:51:10,600 Speaker 1: so you're getting so, you've got so you're getting a 837 00:51:10,719 --> 00:51:15,520 Speaker 1: really powerful reaction again against the kind of post war 838 00:51:16,160 --> 00:51:18,720 Speaker 1: kind of trends. And if it's strong enough, then it'll 839 00:51:18,760 --> 00:51:22,919 Speaker 1: sort of dismantle. It's including some things that we don't 840 00:51:22,960 --> 00:51:25,600 Speaker 1: want to dismantle, come from the benefits of an open 841 00:51:25,600 --> 00:51:29,120 Speaker 1: global economy and specialization and all that kind of thing. So, 842 00:51:30,200 --> 00:51:32,200 Speaker 1: I mean, nobody knows where that's going to take us. 843 00:51:32,239 --> 00:51:35,239 Speaker 1: But this is this is this is a world I 844 00:51:35,239 --> 00:51:38,520 Speaker 1: would say that has more centrifugal forces and more tensions 845 00:51:38,600 --> 00:51:42,520 Speaker 1: than that I can remember for a long time. There's 846 00:51:42,520 --> 00:51:46,799 Speaker 1: a quote in in the book that I was fascinated by, um, 847 00:51:46,840 --> 00:51:50,120 Speaker 1: and I want to get your thoughts on it. Sustainable 848 00:51:50,120 --> 00:51:55,120 Speaker 1: wealth creation is ultimately built on people, human capital and knowledge, 849 00:51:55,200 --> 00:51:59,200 Speaker 1: on continuous structural change in the economy, and on systems 850 00:51:59,239 --> 00:52:04,719 Speaker 1: of economic and political organization that permit the productive deployment 851 00:52:05,080 --> 00:52:08,239 Speaker 1: of those assets. Now, when I read that today, that 852 00:52:08,320 --> 00:52:11,920 Speaker 1: seems pretty obvious, pretty self evident. I get the sense 853 00:52:11,920 --> 00:52:16,320 Speaker 1: when you wrote that that it wasn't quite as obvious. Um, 854 00:52:16,400 --> 00:52:20,560 Speaker 1: what what's your key takeaway? Why is it human capital 855 00:52:20,800 --> 00:52:24,560 Speaker 1: and not natural resources or oil or things that we 856 00:52:24,719 --> 00:52:26,799 Speaker 1: used to think of as as so important to the 857 00:52:26,840 --> 00:52:30,920 Speaker 1: economy or at least to the local wealth creation. Yeah, 858 00:52:31,000 --> 00:52:33,799 Speaker 1: so you one doesn't want to overstate these things. So 859 00:52:33,840 --> 00:52:36,440 Speaker 1: if you take an economy that's rich in human capital 860 00:52:36,520 --> 00:52:38,759 Speaker 1: and take away its energy, it's not going to do 861 00:52:38,880 --> 00:52:42,520 Speaker 1: very well. Right, So we're talking about complementary inputs, and 862 00:52:42,760 --> 00:52:45,920 Speaker 1: probably that and and statements like are a bit of 863 00:52:45,960 --> 00:52:48,840 Speaker 1: an overstatement. What I was doing. It was trying to counter, 864 00:52:49,680 --> 00:52:52,080 Speaker 1: you know, this notion that you know, there's some source 865 00:52:52,160 --> 00:52:56,400 Speaker 1: of wealth that's really important, of comparable importance to the 866 00:52:56,560 --> 00:52:59,640 Speaker 1: kind of knowledge and technology based. So let me let 867 00:52:59,640 --> 00:53:01,960 Speaker 1: me tell the way I used to think about them myself, 868 00:53:02,400 --> 00:53:04,319 Speaker 1: and I sometimes do it in class. I say, so, 869 00:53:04,400 --> 00:53:07,680 Speaker 1: I'll give you a choice that you know is completely hypothetical. 870 00:53:08,280 --> 00:53:11,759 Speaker 1: You have an economy it's doing very well, it's quite advanced. 871 00:53:12,280 --> 00:53:16,480 Speaker 1: And Choice one is you destroy essentially all the physical assets, 872 00:53:16,600 --> 00:53:20,040 Speaker 1: or a substantial fraction of them, but everything that's in 873 00:53:20,120 --> 00:53:24,520 Speaker 1: people's heads or in the libraries or in the scientific community, 874 00:53:24,000 --> 00:53:27,960 Speaker 1: a huge range of stuff. All the traffic engineers still 875 00:53:28,000 --> 00:53:30,799 Speaker 1: know how to run traffic and stuff like that. That's 876 00:53:30,840 --> 00:53:33,840 Speaker 1: one and the two is everybody gets amnesia and you 877 00:53:33,920 --> 00:53:36,839 Speaker 1: lose all that, and then I say which one would 878 00:53:36,840 --> 00:53:39,800 Speaker 1: you take? And the students always take destroy the physical assets, 879 00:53:39,840 --> 00:53:43,480 Speaker 1: and they're right there. It's hard to always rebuild that. 880 00:53:43,640 --> 00:53:47,279 Speaker 1: You can rebuild that. And the other thing is centuries 881 00:53:47,280 --> 00:53:51,439 Speaker 1: have accumulated knowledge and wisdom. So what do we think 882 00:53:51,520 --> 00:53:55,160 Speaker 1: of certain countries? And we've seen this in i Ran 883 00:53:55,200 --> 00:53:57,880 Speaker 1: as a good example, where they throw out half of 884 00:53:57,920 --> 00:54:01,279 Speaker 1: their intellectual class. They throw out around the time of 885 00:54:01,320 --> 00:54:04,480 Speaker 1: the revolution, they throw out all the educated professors and 886 00:54:04,520 --> 00:54:07,680 Speaker 1: the doctors and the lawyers. And does does that set 887 00:54:07,719 --> 00:54:11,840 Speaker 1: them back decades? It takes that long to recover? Yeah? 888 00:54:11,920 --> 00:54:15,719 Speaker 1: I think so, Yeah, definitely, especially if you know it's 889 00:54:15,760 --> 00:54:18,120 Speaker 1: an environment where it's it's hard to get people to 890 00:54:18,200 --> 00:54:21,759 Speaker 1: come back. Uh. And some of these developing countries, you know, 891 00:54:21,800 --> 00:54:24,640 Speaker 1: they export people in the early stages to go get 892 00:54:24,640 --> 00:54:28,440 Speaker 1: an education and then they and people don't return then 893 00:54:28,560 --> 00:54:30,880 Speaker 1: and for a while, I don't return. And then if 894 00:54:30,920 --> 00:54:33,120 Speaker 1: you get lucky and went, you're further down the road, 895 00:54:33,680 --> 00:54:36,879 Speaker 1: the opportunities start to to come and then they start 896 00:54:36,920 --> 00:54:39,680 Speaker 1: to come back because the opportunities are there. That's China today. 897 00:54:39,719 --> 00:54:41,560 Speaker 1: They used to send people here to get educated. They 898 00:54:41,560 --> 00:54:44,040 Speaker 1: wouldn't go home. Now a big chunk seems to be 899 00:54:44,239 --> 00:54:48,160 Speaker 1: I want to go back to Shen's in orspit. So 900 00:54:48,680 --> 00:54:51,439 Speaker 1: that's I mean, that's a tough one to multi year 901 00:54:51,680 --> 00:54:55,680 Speaker 1: thing to navigate through, but that's that's an example of that. Yeah, 902 00:54:55,719 --> 00:54:59,480 Speaker 1: I mean Europe, you know, coming into World War two, 903 00:54:59,560 --> 00:55:01,680 Speaker 1: export read an awful lot of talent. That was a 904 00:55:01,680 --> 00:55:08,000 Speaker 1: pretty important part of the the advancement of the American 905 00:55:07,680 --> 00:55:10,760 Speaker 1: I mean, well you look at our nuclear program from Germany, 906 00:55:10,800 --> 00:55:17,640 Speaker 1: effect scientists, you know, engineering talent, Weinstein von Neumann and Morgenstern, 907 00:55:17,719 --> 00:55:22,319 Speaker 1: game theory, I mean, what's on and on um. So, yeah, 908 00:55:22,360 --> 00:55:24,680 Speaker 1: it's not a good idea to export talent, and it's 909 00:55:24,719 --> 00:55:28,759 Speaker 1: not a good idea to underinvest in things that keep 910 00:55:28,960 --> 00:55:32,480 Speaker 1: you know, that kind of person around. So I live 911 00:55:32,520 --> 00:55:36,240 Speaker 1: in Italy part of the time, where in Italy malone 912 00:55:36,600 --> 00:55:41,280 Speaker 1: on pretty well put together city. And but but you're 913 00:55:41,320 --> 00:55:43,760 Speaker 1: but a general problem in Europe and they're falling behind. 914 00:55:44,200 --> 00:55:47,239 Speaker 1: And this but especially in places like Italy, is that 915 00:55:47,280 --> 00:55:50,200 Speaker 1: we don't invest enough to keep the top you know, 916 00:55:51,600 --> 00:55:54,759 Speaker 1: biomedical scientists and whatnot. It's not that I mean they 917 00:55:54,760 --> 00:56:00,600 Speaker 1: want to stay right, but you gotta have the the 918 00:56:00,640 --> 00:56:03,080 Speaker 1: research funding, you have to have the programs and stuff, 919 00:56:03,360 --> 00:56:05,520 Speaker 1: or they're gonna you know, these are the most mobile 920 00:56:05,560 --> 00:56:08,080 Speaker 1: people in the world. You know, they're gonna end up 921 00:56:08,080 --> 00:56:11,040 Speaker 1: in the United States and Britain or something like that. 922 00:56:11,160 --> 00:56:15,279 Speaker 1: So is it it's not just for career for money opportunities, 923 00:56:15,280 --> 00:56:18,440 Speaker 1: it's for research opportit, research opportunities and everything the one 924 00:56:18,560 --> 00:56:21,520 Speaker 1: that you love to do done, and so the top 925 00:56:21,560 --> 00:56:25,520 Speaker 1: talent ends up leaving it. Is that strictly a problem 926 00:56:25,760 --> 00:56:27,840 Speaker 1: in Europe or where else do we see that problem 927 00:56:27,880 --> 00:56:31,680 Speaker 1: coming up? Well, I mean no, it's not, you know, 928 00:56:31,760 --> 00:56:36,200 Speaker 1: confined to Europe. I mean I think Europe has, relative 929 00:56:36,280 --> 00:56:38,480 Speaker 1: to the income levels a kind of problem and that 930 00:56:38,520 --> 00:56:42,040 Speaker 1: they're falling behind, especially in the digital area. Maybe when 931 00:56:42,080 --> 00:56:45,839 Speaker 1: you think about it, that really influential entities in this world, 932 00:56:45,880 --> 00:56:48,879 Speaker 1: a subset of them, or the mega platforms they're all 933 00:56:48,920 --> 00:56:52,240 Speaker 1: in the United States and China, mega platforms like Ali 934 00:56:52,280 --> 00:56:55,600 Speaker 1: Bamba or Facebook or Google or go down the least 935 00:56:55,680 --> 00:56:59,200 Speaker 1: Amazon or whatever. Yeah, and that's what's attracting all the 936 00:56:59,280 --> 00:57:02,320 Speaker 1: intellectual capital. Well, it's certainly the kind of epicenter of 937 00:57:02,360 --> 00:57:06,640 Speaker 1: a lot of you know, applied innovation. So Artificial intelligence 938 00:57:06,640 --> 00:57:10,120 Speaker 1: in this modern form is a highly data intensive activity, 939 00:57:10,200 --> 00:57:16,320 Speaker 1: tends to occur around lots of data, cloud computing power, 940 00:57:17,200 --> 00:57:21,040 Speaker 1: and uh an ability to attract talent. I mean if 941 00:57:21,360 --> 00:57:23,400 Speaker 1: if if you and I were talking ten years ago 942 00:57:24,120 --> 00:57:26,560 Speaker 1: and and and I said to you, what, what's what 943 00:57:26,640 --> 00:57:29,880 Speaker 1: are the odds of this? The autonomous vehicle? You know, 944 00:57:30,240 --> 00:57:33,240 Speaker 1: business is going to be driven forward by you know, Google, 945 00:57:33,920 --> 00:57:38,040 Speaker 1: you would have said, are you crazy? But you know 946 00:57:38,480 --> 00:57:40,640 Speaker 1: a lot of that technology is coming out of Buy 947 00:57:40,720 --> 00:57:44,919 Speaker 1: Do and Google, and it's data and engineering skills, data 948 00:57:45,000 --> 00:57:48,440 Speaker 1: engineering skills and consulting car. Yeah. Quite interesting. Before I 949 00:57:48,480 --> 00:57:50,800 Speaker 1: get to my favorite questions, I ask all my guests, 950 00:57:51,160 --> 00:57:54,960 Speaker 1: I have to ask one question because I've had several 951 00:57:55,120 --> 00:57:58,520 Speaker 1: previous Nobel Prize winners, and everybody seems to have a 952 00:57:58,680 --> 00:58:02,720 Speaker 1: charming little story about that phone call they get and 953 00:58:02,720 --> 00:58:05,200 Speaker 1: and we're in that time of year right now, what 954 00:58:05,200 --> 00:58:08,439 Speaker 1: what was your experience? Like, I never got the call, 955 00:58:08,600 --> 00:58:10,840 Speaker 1: So never got a phone call. No, no, they tried, 956 00:58:11,280 --> 00:58:13,480 Speaker 1: but they phoned my home in California, and then we 957 00:58:13,520 --> 00:58:16,600 Speaker 1: had taken a little trip to Hawaii, right So what 958 00:58:16,720 --> 00:58:19,800 Speaker 1: happened was they couldn't get through and they can't wait forever. 959 00:58:20,320 --> 00:58:25,200 Speaker 1: So then if some minutes later they post this on 960 00:58:25,240 --> 00:58:29,800 Speaker 1: a website and I had I had a friend, actually 961 00:58:29,800 --> 00:58:33,840 Speaker 1: more pro more than one who u A was up 962 00:58:33,880 --> 00:58:35,880 Speaker 1: in the morning. This is probably on the West coast 963 00:58:35,920 --> 00:58:40,800 Speaker 1: four thirty or five in the morning, so he saw 964 00:58:40,840 --> 00:58:42,960 Speaker 1: a flash up on the screen and knew where I was. 965 00:58:43,040 --> 00:58:45,280 Speaker 1: So the phone call I got was from a friend 966 00:58:45,280 --> 00:58:49,200 Speaker 1: of mine. And how do you know, no one's really 967 00:58:49,240 --> 00:58:51,240 Speaker 1: pulling your leg or by that point it's on the 968 00:58:51,240 --> 00:58:54,280 Speaker 1: news and you by that point it's starting to get 969 00:58:54,320 --> 00:58:55,960 Speaker 1: to be the news, so you can be I was 970 00:58:56,000 --> 00:58:58,640 Speaker 1: not expecting it. I mean I was completely really yeah, 971 00:58:58,760 --> 00:59:03,360 Speaker 1: well because it's not a lifetime achievement award, but you know, 972 00:59:03,680 --> 00:59:08,480 Speaker 1: I had been an academic administration up until fifteen years, 973 00:59:09,120 --> 00:59:15,120 Speaker 1: you know, not kind of yeah and whatnot. So I thought, well, 974 00:59:15,600 --> 00:59:17,440 Speaker 1: that was a choice I made. I don't regret it, 975 00:59:17,480 --> 00:59:20,880 Speaker 1: but I'm not going down that road anymore. So that 976 00:59:20,960 --> 00:59:23,640 Speaker 1: came right out of the blue for me. Quite quite interesting. 977 00:59:23,880 --> 00:59:26,200 Speaker 1: So let me jump to My favorite questions are are 978 00:59:26,280 --> 00:59:29,040 Speaker 1: speed round? This is what I asked all of my guests. 979 00:59:29,080 --> 00:59:33,280 Speaker 1: Sometimes it's revealing. Um, we'll start out easy. What was 980 00:59:33,320 --> 00:59:37,040 Speaker 1: the first car you ever owned? Year making model. Uh 981 00:59:37,160 --> 00:59:40,280 Speaker 1: so it was a Chevy Nova and I think the 982 00:59:41,320 --> 00:59:45,400 Speaker 1: uh it was yellow. My parents bought up for me 983 00:59:45,640 --> 00:59:48,400 Speaker 1: and I don't remember the exact year, but it's got 984 00:59:48,400 --> 00:59:53,440 Speaker 1: to be nineteen sixty one or two. They're they're collectible now. Also, 985 00:59:53,960 --> 00:59:59,400 Speaker 1: what's the most important thing people don't know about Michael Spence? Gee? 986 00:59:59,440 --> 01:00:02,480 Speaker 1: I don't know, not much, I guess. I mean, I 987 01:00:02,520 --> 01:00:10,120 Speaker 1: don't think of anything that uh I've been some people 988 01:00:10,160 --> 01:00:12,400 Speaker 1: are you know, people don't know that they're about a 989 01:00:12,440 --> 01:00:15,960 Speaker 1: secret hobby or something. You're you're an open book. I'm 990 01:00:16,000 --> 01:00:18,040 Speaker 1: pretty open. But I mean there's probably lots of things 991 01:00:18,080 --> 01:00:19,760 Speaker 1: that people don't know. Maybe I wanted to be a 992 01:00:19,760 --> 01:00:22,439 Speaker 1: professional hockey player and stuff like that, But well, that's 993 01:00:22,440 --> 01:00:24,440 Speaker 1: your from Canada. You already said that, so we just 994 01:00:24,520 --> 01:00:28,200 Speaker 1: assumed that we did a merryment. Tell us about some 995 01:00:28,280 --> 01:00:31,320 Speaker 1: of your early mentors who influenced your career and guided 996 01:00:31,360 --> 01:00:34,560 Speaker 1: you early on. Wait a lot there were a lot 997 01:00:34,640 --> 01:00:36,160 Speaker 1: of them, you know. I mean I went to a 998 01:00:36,240 --> 01:00:39,040 Speaker 1: school it's like the Lab School at Chicago in Toronto, 999 01:00:39,240 --> 01:00:42,320 Speaker 1: attached to the University of Toronto with a very uh 1000 01:00:42,960 --> 01:00:47,000 Speaker 1: charismatic and influential coach coached us in football and hockey 1001 01:00:47,040 --> 01:00:50,280 Speaker 1: and and other things. Just a guy who made a 1002 01:00:50,320 --> 01:00:52,640 Speaker 1: big difference in our lives, the values and the kind 1003 01:00:52,640 --> 01:00:56,480 Speaker 1: of way we went about doing things. Um, and I've 1004 01:00:56,560 --> 01:00:59,240 Speaker 1: been blessed with really wonderful teachers, but I think, you know, 1005 01:01:00,000 --> 01:01:03,360 Speaker 1: eas would be what I finally became, you know, an economist. 1006 01:01:03,760 --> 01:01:06,760 Speaker 1: I would say my thesis advisors, which or Dick Zak, 1007 01:01:06,840 --> 01:01:10,840 Speaker 1: how was your Canarrow? And Tom Shelling were just enormously 1008 01:01:11,880 --> 01:01:15,360 Speaker 1: They're not only influential, they were supportive. I mean, you know, 1009 01:01:15,760 --> 01:01:19,760 Speaker 1: so I can imagine thesis advisers telling a young person 1010 01:01:19,800 --> 01:01:21,959 Speaker 1: who was sort of mucking around with something that didn't 1011 01:01:21,960 --> 01:01:25,080 Speaker 1: sound like what other people were doing. You know, probably 1012 01:01:25,160 --> 01:01:27,280 Speaker 1: you should do that later. You know, that's a bit risky. 1013 01:01:27,320 --> 01:01:29,760 Speaker 1: Don't do that for your PhD. They never said that 1014 01:01:29,800 --> 01:01:31,480 Speaker 1: to me. They said, well, was it risky what we 1015 01:01:31,560 --> 01:01:33,760 Speaker 1: were doing? Well, it could have turned out to be nothing, 1016 01:01:33,840 --> 01:01:36,160 Speaker 1: So I guess in that sense, yes, But at that 1017 01:01:36,200 --> 01:01:39,400 Speaker 1: point I was prepared to quit. Uh oh really yeah, Well, 1018 01:01:39,480 --> 01:01:42,600 Speaker 1: to me, getting a PhD and going into academic life 1019 01:01:42,680 --> 01:01:45,840 Speaker 1: was an experiment, not a decision that I was going 1020 01:01:45,880 --> 01:01:48,720 Speaker 1: to stick with, you know, through thick and thin. I mean, 1021 01:01:49,440 --> 01:01:51,919 Speaker 1: it turned out to be an experiment with a great 1022 01:01:51,960 --> 01:01:54,240 Speaker 1: outcome from my point of view. But and and ken 1023 01:01:54,440 --> 01:01:57,880 Speaker 1: Arrow eventually himself wins the Nobel Prize and act well, 1024 01:01:57,880 --> 01:01:59,640 Speaker 1: he was well well before me. He was one of 1025 01:01:59,640 --> 01:02:02,360 Speaker 1: the early ones, the one, the one that was surprised. 1026 01:02:02,400 --> 01:02:05,720 Speaker 1: I don't know if Dick Zackhauser receive a Nobel Prize. Uh, 1027 01:02:05,960 --> 01:02:08,640 Speaker 1: he's very very smart. But Tom Shelling came after me, 1028 01:02:10,280 --> 01:02:12,520 Speaker 1: who was one of your advice was one of my advisors. 1029 01:02:12,520 --> 01:02:16,640 Speaker 1: So that was a bit odd. That's interesting. Um, let's 1030 01:02:16,680 --> 01:02:21,120 Speaker 1: talk about economics in an economists in general, who influenced 1031 01:02:21,120 --> 01:02:26,720 Speaker 1: the way you think about information theory? Same group of people, say, 1032 01:02:27,160 --> 01:02:30,000 Speaker 1: same guys. I mean, I would say I mentioned Lester Thorow, 1033 01:02:30,120 --> 01:02:32,480 Speaker 1: but Dick Zackheuser, for sure. I wrote things with him, 1034 01:02:32,560 --> 01:02:36,720 Speaker 1: Tom Shelling, because he had, um how do I say it, 1035 01:02:37,640 --> 01:02:40,800 Speaker 1: a really creative mind in a different way of thinking. 1036 01:02:40,880 --> 01:02:46,720 Speaker 1: So Shelling, as you know, was um responsible for a 1037 01:02:46,800 --> 01:02:51,320 Speaker 1: kind of branch of applied game theory in which you 1038 01:02:51,400 --> 01:02:54,720 Speaker 1: know that was really important in the Cold War in 1039 01:02:54,800 --> 01:03:01,280 Speaker 1: nuclear deterrence, very unconventional, kind of not not completely formal. 1040 01:03:01,960 --> 01:03:03,880 Speaker 1: And I spent a lot of time with him he 1041 01:03:04,440 --> 01:03:07,960 Speaker 1: that was a very big influence. Sermon. Let's talk about books. 1042 01:03:08,000 --> 01:03:09,960 Speaker 1: What are some of your favorite books. What what do 1043 01:03:09,960 --> 01:03:13,560 Speaker 1: you like to read when you're not writing your own books? Well, 1044 01:03:13,560 --> 01:03:16,520 Speaker 1: it's nice, it just read for relaxation. I started to like, 1045 01:03:16,800 --> 01:03:20,240 Speaker 1: you know, my kids, some of the kids books are 1046 01:03:20,440 --> 01:03:24,320 Speaker 1: kind of fun. Uh, you know, these sort of sagas, 1047 01:03:24,360 --> 01:03:26,920 Speaker 1: like give us an example. Well, there's a book, a 1048 01:03:26,960 --> 01:03:29,520 Speaker 1: series of books, you know, the last one is apparently 1049 01:03:29,520 --> 01:03:32,600 Speaker 1: coming out in the spring, written by S. D. Smith 1050 01:03:32,640 --> 01:03:37,439 Speaker 1: called Green Ember. It's about rabbits. Okay, it's everyone said, 1051 01:03:37,440 --> 01:03:40,040 Speaker 1: while there's a saga about rabbits that comes out, and 1052 01:03:40,160 --> 01:03:45,280 Speaker 1: so they're kind of fun. Green Ember. Yeah, my um. 1053 01:03:45,440 --> 01:03:47,240 Speaker 1: One of my favorite books when I was growing up 1054 01:03:47,280 --> 01:03:49,440 Speaker 1: was The Agony in the Ecstasy. It was it's the 1055 01:03:49,560 --> 01:03:54,520 Speaker 1: historical novel um about the life of Michelangelo. I just 1056 01:03:54,600 --> 01:03:59,040 Speaker 1: found that a fascinating and be aspiring and I've I've 1057 01:03:59,080 --> 01:04:03,040 Speaker 1: had a lifelong kind of fascination with the Renaissance, right, 1058 01:04:04,320 --> 01:04:09,440 Speaker 1: mainly because so many things blossomed at exactly the same time, 1059 01:04:09,480 --> 01:04:14,360 Speaker 1: you know, our architecture, sculpture, finance, banking, you know, it 1060 01:04:14,520 --> 01:04:18,880 Speaker 1: was just this explosion of innovation around that irving Stone. 1061 01:04:20,440 --> 01:04:26,720 Speaker 1: So in the next convergence you reference that era, you say, 1062 01:04:27,240 --> 01:04:30,800 Speaker 1: previous to seventeen fifty, there was a long period where 1063 01:04:30,880 --> 01:04:33,640 Speaker 1: not a lot happened. So I mean it's almost in 1064 01:04:33,720 --> 01:04:38,040 Speaker 1: passing you reference. UM. The book that I've found intriguing 1065 01:04:38,120 --> 01:04:40,360 Speaker 1: about that was I don't know if you're familiar with it, 1066 01:04:40,640 --> 01:04:46,480 Speaker 1: A world lit only by fire explains for years, other 1067 01:04:46,520 --> 01:04:49,680 Speaker 1: than the windmill, nothing was invented. It was just a 1068 01:04:50,720 --> 01:04:55,680 Speaker 1: dead period at least in Um Western civilization. China and 1069 01:04:56,320 --> 01:05:01,600 Speaker 1: parts of UM Muslim Turkestan area were but the Western 1070 01:05:01,640 --> 01:05:05,360 Speaker 1: world no forward progress. That's right, and to the extent 1071 01:05:06,160 --> 01:05:09,480 Speaker 1: the only modification of that is that there was probably 1072 01:05:09,640 --> 01:05:12,560 Speaker 1: in the latter part of that period some scientific progress 1073 01:05:12,600 --> 01:05:18,240 Speaker 1: that didn't translate into technology and economic outcomes. UM directly right, 1074 01:05:18,360 --> 01:05:22,600 Speaker 1: only with a lag, but that that's basically right. It's amazing. 1075 01:05:22,600 --> 01:05:26,520 Speaker 1: Any of the books you want to mention, no, that's it, okay. UM, 1076 01:05:26,640 --> 01:05:29,240 Speaker 1: Here's as always an interesting question, tell us about a 1077 01:05:29,320 --> 01:05:32,400 Speaker 1: time you failed and what you learned from the experience. 1078 01:05:34,160 --> 01:05:42,919 Speaker 1: It's hard to choose. It's very very long list. I mean, UM, 1079 01:05:42,960 --> 01:05:45,400 Speaker 1: and we we often find that failure can be more 1080 01:05:45,440 --> 01:05:49,760 Speaker 1: instructive than success, which is why I asked the question. Yeah, no, 1081 01:05:49,960 --> 01:05:52,280 Speaker 1: that's true. I mean, when you're doing research, you know, 1082 01:05:52,360 --> 01:05:55,800 Speaker 1: there's a lot of they're not big, you know, kind 1083 01:05:55,800 --> 01:05:58,200 Speaker 1: of eye catching failures, but there's a lot of dead 1084 01:05:58,320 --> 01:06:01,120 Speaker 1: ends and you do learn from those. So that I mean, 1085 01:06:01,200 --> 01:06:03,320 Speaker 1: in the in a sense when I kind of aggregate 1086 01:06:03,400 --> 01:06:07,080 Speaker 1: all those up, those are probably the most influential ones. 1087 01:06:07,160 --> 01:06:09,520 Speaker 1: In Silicon Valley, where I lived for quite a long time, 1088 01:06:10,280 --> 01:06:14,480 Speaker 1: I learned over time that a failure is something that 1089 01:06:15,320 --> 01:06:19,160 Speaker 1: is important and being, you know, a culture that penalizes 1090 01:06:19,200 --> 01:06:25,080 Speaker 1: it will kill entrepreneurship and innovation. And third, I learned 1091 01:06:25,080 --> 01:06:27,840 Speaker 1: that the venture capitalists like entrepreneurs that had a failure, 1092 01:06:27,880 --> 01:06:29,920 Speaker 1: provided it was the right kind of failure. So it's 1093 01:06:29,920 --> 01:06:33,240 Speaker 1: funny you bring that up. I've spoken to colleagues in 1094 01:06:33,320 --> 01:06:38,720 Speaker 1: Europe and elsewhere, and I've heard multiple times when we've 1095 01:06:38,720 --> 01:06:41,760 Speaker 1: discussed the difference between the United States and Europe, they've said, 1096 01:06:42,200 --> 01:06:44,880 Speaker 1: the United States is the country that not only doesn't 1097 01:06:44,920 --> 01:06:48,840 Speaker 1: penalize failure, but practically rewards it, and that's very different 1098 01:06:48,880 --> 01:06:52,280 Speaker 1: than Europe. Yes, that's correct, and a lot of other places. 1099 01:06:52,720 --> 01:06:55,240 Speaker 1: I mean, this is it's really it's a little fuzzy, 1100 01:06:55,280 --> 01:06:59,000 Speaker 1: but it's really important. Uh yeah, so that's pretty deeply 1101 01:06:59,040 --> 01:07:03,960 Speaker 1: embedded in our call uture and it makes it, it 1102 01:07:04,040 --> 01:07:07,680 Speaker 1: makes us, I think, more dynamic. I totally agree. So 1103 01:07:07,960 --> 01:07:11,640 Speaker 1: what do you do for fun? Well, when you get older, 1104 01:07:11,680 --> 01:07:14,760 Speaker 1: you have sort of changes. So, um, I grew up 1105 01:07:14,800 --> 01:07:20,400 Speaker 1: playing sports hockey, hockey in particular. Um, that's that's the 1106 01:07:20,440 --> 01:07:23,840 Speaker 1: reason I ended up at Princeton to play hockey. Yeah, right, 1107 01:07:23,960 --> 01:07:28,040 Speaker 1: they well, they made a mistake, but I think they're 1108 01:07:28,040 --> 01:07:31,840 Speaker 1: okay with that decision. They were with Yeah but they wait, 1109 01:07:31,920 --> 01:07:35,120 Speaker 1: so I mean you made a mistake because you turned 1110 01:07:35,160 --> 01:07:37,560 Speaker 1: out not to be a great hockey player. Well, there 1111 01:07:37,560 --> 01:07:39,120 Speaker 1: were a bunch of us, you know, so I think 1112 01:07:39,120 --> 01:07:41,040 Speaker 1: they thought they were gonna have a great hockey team. 1113 01:07:41,040 --> 01:07:43,120 Speaker 1: But we weren't quite as good as they had hoped for. 1114 01:07:43,280 --> 01:07:47,320 Speaker 1: We were we were the Saturday night entertainment. Uh what 1115 01:07:47,960 --> 01:07:50,760 Speaker 1: But the joke about us was we could we we 1116 01:07:50,920 --> 01:07:57,960 Speaker 1: routinely um snatched defeat from the jaws of victory, and 1117 01:07:58,240 --> 01:08:02,800 Speaker 1: uh but we were fun to watch hockey anymore. No, 1118 01:08:02,960 --> 01:08:04,600 Speaker 1: I played it for a while and then you know, 1119 01:08:04,640 --> 01:08:07,880 Speaker 1: when the kids skated, you know, and for a while, 1120 01:08:08,280 --> 01:08:12,080 Speaker 1: but at some point it's uh, I don't like the 1121 01:08:12,120 --> 01:08:15,160 Speaker 1: way the professional game has gone. You know, when we 1122 01:08:15,240 --> 01:08:17,880 Speaker 1: when I started playing hockey, this is not necessarily a 1123 01:08:17,880 --> 01:08:21,160 Speaker 1: good thing, we didn't wear helmets, and then we wore 1124 01:08:21,200 --> 01:08:23,640 Speaker 1: these helmes, you know, small helmets just to protect you, 1125 01:08:23,960 --> 01:08:26,240 Speaker 1: and we were pretty careful. But where sticks went and 1126 01:08:26,280 --> 01:08:29,400 Speaker 1: what we did. Um, Now these guys are dressed up 1127 01:08:29,439 --> 01:08:32,200 Speaker 1: like you know, warriors and go to war and go 1128 01:08:32,240 --> 01:08:35,639 Speaker 1: to war. And I just I I like the International Games. 1129 01:08:35,680 --> 01:08:39,439 Speaker 1: Bigger rink, you know, more premium on you know, skating 1130 01:08:39,479 --> 01:08:41,519 Speaker 1: of the type that you saw with Bobby or and 1131 01:08:41,520 --> 01:08:45,080 Speaker 1: Wayne Ring, Wayne Gretzky. I mean, it's just a prettier game. 1132 01:08:45,479 --> 01:08:49,000 Speaker 1: The Um it's funny you mentioned that because I there's 1133 01:08:49,040 --> 01:08:53,000 Speaker 1: an and I think it's an information issue. Every time 1134 01:08:53,040 --> 01:08:58,320 Speaker 1: we add a safety device to cars, the the accident 1135 01:08:58,439 --> 01:09:01,080 Speaker 1: rate doesn't go down because as people think, oh, I 1136 01:09:01,120 --> 01:09:03,080 Speaker 1: have an air bag and a crumple zone and a 1137 01:09:03,160 --> 01:09:06,439 Speaker 1: BS and a three point safety belt, I could drive faster, 1138 01:09:06,960 --> 01:09:09,360 Speaker 1: and so we end up with safer cars, but no 1139 01:09:09,560 --> 01:09:15,320 Speaker 1: decrease in automobile deaths at all. Yeah, that's a general principle. 1140 01:09:15,360 --> 01:09:18,439 Speaker 1: I mean it's sort of like Martha's right, maas said 1141 01:09:18,640 --> 01:09:21,400 Speaker 1: every time we you know, our income score or productivity 1142 01:09:21,400 --> 01:09:23,439 Speaker 1: goes up, we'll have more people to use it all up. 1143 01:09:23,520 --> 01:09:27,840 Speaker 1: And we no, no, he didn't adjust for inflation. That 1144 01:09:27,920 --> 01:09:31,000 Speaker 1: was his his big prop um. So let's talk a 1145 01:09:31,000 --> 01:09:35,040 Speaker 1: little bit about these information gaps and structures and markets. 1146 01:09:35,560 --> 01:09:40,360 Speaker 1: What are you most optimistic about regarding uh, information gaps 1147 01:09:40,400 --> 01:09:43,840 Speaker 1: and asymmetries, and what are you most pessimistic about these days? 1148 01:09:43,880 --> 01:09:46,880 Speaker 1: Basically your work and how it applies. So I think 1149 01:09:47,000 --> 01:09:50,120 Speaker 1: on the optimistic side, I think, you know, properly deployed, 1150 01:09:50,120 --> 01:09:54,679 Speaker 1: that this digital technology does close gaps in a sort 1151 01:09:54,680 --> 01:09:58,720 Speaker 1: of start legally important and inclusive way. So it's a 1152 01:09:58,800 --> 01:10:01,880 Speaker 1: huge opportunity, and it seems to be happening. I mean, 1153 01:10:01,960 --> 01:10:07,600 Speaker 1: this digital divide sort of semi vanished. Uh. The the 1154 01:10:07,680 --> 01:10:11,080 Speaker 1: mobile internet, the mobile phone and mobile internet is kind 1155 01:10:11,080 --> 01:10:16,120 Speaker 1: of taken over the world everything. I mean, it just 1156 01:10:16,240 --> 01:10:23,280 Speaker 1: looks like, uh, it's just a huge winner um on. 1157 01:10:23,479 --> 01:10:26,400 Speaker 1: But on the same score, I mean, there are these 1158 01:10:26,439 --> 01:10:30,000 Speaker 1: informational gaps and and powerful tools like these can be 1159 01:10:30,080 --> 01:10:34,240 Speaker 1: used to exploit the vulnerable as well. Uh, so you've 1160 01:10:34,240 --> 01:10:38,040 Speaker 1: got more ways to cheat you know, sort of grandmothers 1161 01:10:38,040 --> 01:10:41,360 Speaker 1: out of their savings than you used to you used 1162 01:10:41,360 --> 01:10:45,080 Speaker 1: to have to actually go to the front door and knock. 1163 01:10:45,439 --> 01:10:49,320 Speaker 1: So so there's a like most things, there's a kind 1164 01:10:49,320 --> 01:10:51,439 Speaker 1: of flip side to all these coins. But but I 1165 01:10:51,479 --> 01:10:56,400 Speaker 1: think that's not an impossible problem to deal with. And uh, 1166 01:10:56,479 --> 01:10:58,720 Speaker 1: if a recent college grad came up to you and 1167 01:10:58,840 --> 01:11:02,080 Speaker 1: said they were interested in a career in either academia 1168 01:11:02,240 --> 01:11:07,280 Speaker 1: or economics, what sort of advice would you give them. Well, 1169 01:11:07,360 --> 01:11:10,200 Speaker 1: I mean, I'd say try it, you know, I mean, 1170 01:11:10,680 --> 01:11:13,320 Speaker 1: I think that people are very different from each other, 1171 01:11:13,400 --> 01:11:16,160 Speaker 1: so and you just don't know, so you've got to experiment. 1172 01:11:16,280 --> 01:11:20,840 Speaker 1: I would experiment and without kind of pre determining what 1173 01:11:20,960 --> 01:11:25,200 Speaker 1: the outcome is. Um and the other advice that I 1174 01:11:25,240 --> 01:11:27,879 Speaker 1: tend to give it and it's not necessarily what everybody 1175 01:11:27,880 --> 01:11:31,679 Speaker 1: else would give, which is I think planning one's career 1176 01:11:32,080 --> 01:11:34,960 Speaker 1: beyond a certain point is not really a good idea. 1177 01:11:35,439 --> 01:11:39,559 Speaker 1: I think a certain amount of you know, following one's nose, 1178 01:11:40,920 --> 01:11:43,280 Speaker 1: you know, going to the next thing that's really interesting. 1179 01:11:43,320 --> 01:11:46,040 Speaker 1: But I think the most important thing people do. Happy 1180 01:11:46,080 --> 01:11:50,360 Speaker 1: people are not you know, not achieving some goal, but 1181 01:11:50,680 --> 01:11:53,160 Speaker 1: enjoying the process of getting there, doing something they love 1182 01:11:53,640 --> 01:11:57,479 Speaker 1: every morning. You know, to me, that's uh, you know 1183 01:11:57,520 --> 01:12:01,240 Speaker 1: your family life and and not a career so much 1184 01:12:01,280 --> 01:12:05,719 Speaker 1: as UM a vocation. Right, it makes a lot of sense. 1185 01:12:06,439 --> 01:12:09,040 Speaker 1: And final question, what do you know about the world 1186 01:12:09,160 --> 01:12:12,640 Speaker 1: of economics and information theory today that you wish you 1187 01:12:12,720 --> 01:12:17,120 Speaker 1: knew forty or so years ago? Well, I wished I 1188 01:12:17,200 --> 01:12:21,280 Speaker 1: knew on forty years ago what the coming digital revolution. 1189 01:12:21,400 --> 01:12:23,760 Speaker 1: I think that's the thing I would have UM. I 1190 01:12:23,800 --> 01:12:26,519 Speaker 1: would have liked to see a snapshot of not for 1191 01:12:26,600 --> 01:12:31,120 Speaker 1: two reasons. One it it would just be fun and 1192 01:12:31,200 --> 01:12:33,160 Speaker 1: interesting to know this is coming and to kind of 1193 01:12:33,200 --> 01:12:36,120 Speaker 1: get ready for it, um. But the other but the 1194 01:12:36,160 --> 01:12:39,360 Speaker 1: other is um, maybe even accelerate it. But the other 1195 01:12:39,400 --> 01:12:42,840 Speaker 1: one is that it it brings into relief some things. 1196 01:12:44,320 --> 01:12:47,920 Speaker 1: Let me put it this way. Very economic theory always 1197 01:12:47,960 --> 01:12:51,920 Speaker 1: involves simplification, right, And so what what you do in 1198 01:12:52,000 --> 01:12:54,240 Speaker 1: good economic theory is you sort of throw out a 1199 01:12:54,280 --> 01:12:56,599 Speaker 1: bunch of stuff and focus on the things that are important. 1200 01:12:57,520 --> 01:12:59,960 Speaker 1: What What what happens in the course of that is 1201 01:13:00,080 --> 01:13:03,800 Speaker 1: that there are sort of embedded implicit parameters that are 1202 01:13:03,840 --> 01:13:07,840 Speaker 1: never made explicit. You know, things like transaction costs and 1203 01:13:07,880 --> 01:13:10,920 Speaker 1: whatnot that don't change very much. And then every once 1204 01:13:10,920 --> 01:13:13,120 Speaker 1: in a while something comes along and changes them, and 1205 01:13:13,120 --> 01:13:16,400 Speaker 1: then the models aren't okay because the parameters aren't visible, right. 1206 01:13:16,560 --> 01:13:18,880 Speaker 1: I think that's what's happening to us now. You know, 1207 01:13:18,920 --> 01:13:22,759 Speaker 1: we have network structures in economies that are only barely 1208 01:13:22,760 --> 01:13:26,680 Speaker 1: starting to be modeled in the sense that, you know, 1209 01:13:26,760 --> 01:13:29,200 Speaker 1: you capture the essence of the way the economy from 1210 01:13:29,280 --> 01:13:32,879 Speaker 1: We're still ignorant about these changes were yet we're mostly 1211 01:13:33,280 --> 01:13:36,439 Speaker 1: mostly we're in the process of trying to you know, build, 1212 01:13:37,400 --> 01:13:40,400 Speaker 1: build the conceptual structures that allow us to think carefully 1213 01:13:40,520 --> 01:13:44,240 Speaker 1: about these things. Quite quite fascinating. Thank you, Michael for 1214 01:13:44,280 --> 01:13:47,040 Speaker 1: being so generous with your time. We have been speaking 1215 01:13:47,040 --> 01:13:50,160 Speaker 1: with Michael Spence. He is a professor of economics at 1216 01:13:50,280 --> 01:13:53,160 Speaker 1: n y U Starned School of Business and senior advisor 1217 01:13:53,240 --> 01:13:57,280 Speaker 1: at General Atlantic Partners, a giant private equity firm. If 1218 01:13:57,360 --> 01:14:00,200 Speaker 1: you enjoy this conversation, well look up an entry down 1219 01:14:00,200 --> 01:14:02,800 Speaker 1: an Inch on Apple iTunes and you could see any 1220 01:14:02,960 --> 01:14:06,800 Speaker 1: of our previous three hundred or so such conversations we've 1221 01:14:06,840 --> 01:14:10,960 Speaker 1: had over the past five years. We love your comments, feedback, 1222 01:14:10,960 --> 01:14:14,479 Speaker 1: in suggestions. Write to us at m IB podcast at 1223 01:14:14,479 --> 01:14:17,439 Speaker 1: Bloomberg dot net, Go to Apple iTunes and give us 1224 01:14:17,439 --> 01:14:20,559 Speaker 1: a review. Be sure and check out my weekly column 1225 01:14:20,560 --> 01:14:24,320 Speaker 1: on Bloomberg dot com. Follow me on Twitter at rid Halts. 1226 01:14:24,360 --> 01:14:26,840 Speaker 1: I would be remiss if I did not thank the 1227 01:14:26,920 --> 01:14:30,559 Speaker 1: crack staff that helps put together these conversations each week. 1228 01:14:31,000 --> 01:14:35,840 Speaker 1: Colin O'Brien is our audio engineer, Michael Boyle is our booker. 1229 01:14:35,920 --> 01:14:40,519 Speaker 1: Slash producer Atico val Bron is our project director. Michael 1230 01:14:40,560 --> 01:14:44,479 Speaker 1: Batnick is my head of research. I'm Barry Retults. You've 1231 01:14:44,520 --> 01:14:47,599 Speaker 1: been listening to Masters in Business on Bloomberg Radio