WEBVTT - Kopi Time E130 - Shekhar Aiyar on the Case for Globalisation 0:00:05.860 --> 0:00:09.568 Hello, this is Kobe Time, a podcast series on Markets 0:00:09.579 --> 0:00:12.239 and Economies from D BS Group Research. I'm Tamur Big 0:00:12.289 --> 0:00:17.270 chief economist, welcoming you to our 130th episode. Today we 0:00:17.280 --> 0:00:20.799 have a first for copy time. After over four years 0:00:20.809 --> 0:00:23.790 of running the show, we have the first instance of 0:00:23.799 --> 0:00:26.780 two people from the same household grace. This show on 0:00:26.790 --> 0:00:30.170 separate occasions. Back in 2023 we had World Bank South 0:00:30.180 --> 0:00:34.000 Asia chief economist Francisco Zorg on episode 113. 0:00:34.360 --> 0:00:38.740 And for this episode, we have her husband Shekher is 0:00:38.750 --> 0:00:42.330 a non-resident fellow at rural, a visiting scholar at the 0:00:42.340 --> 0:00:45.528 Johns Hopkins School of Advanced International Studies and a visiting 0:00:45.540 --> 0:00:48.000 professor at the National Council of 0:00:49.909 --> 0:00:54.369 Applied Economic Research and C er until 2023 shaker held 0:00:54.380 --> 0:00:56.750 a number of senior physicians in the International Monetary Fund. 0:00:56.759 --> 0:01:00.029 More recently as division chief in the research department where 0:01:00.040 --> 0:01:02.889 he helped coordinate the fund's monitoring of the global economy 0:01:02.970 --> 0:01:05.489 and liaise with the international groups such as the G 0:01:05.500 --> 0:01:08.919 20 G7 shaker. A warm welcome to Kobe time. 0:01:10.010 --> 0:01:11.839 Thank you. Demo. Very nice to be 0:01:11.849 --> 0:01:12.190 here. 0:01:12.639 --> 0:01:16.550 It's great to have you and Shekhar. I am really 0:01:16.559 --> 0:01:20.369 keen to talk about two of your recent research output. 0:01:20.510 --> 0:01:23.589 Uh early last year, you and your colleagues at the 0:01:23.599 --> 0:01:27.399 IMF released a note on geo economic fragmentation, which subsequently 0:01:27.410 --> 0:01:31.709 became several notes and papers. So let's begin with you 0:01:31.720 --> 0:01:34.750 explaining what geo economic fragmentation means. 0:01:35.629 --> 0:01:39.669 Sure. Um So if you look at global flows of 0:01:39.680 --> 0:01:43.010 goods and capital, you'll find that they've plateaued since the 0:01:43.019 --> 0:01:46.879 global financial crisis services continue to rise from a small 0:01:46.889 --> 0:01:51.480 base but goods uh and FD I have plateaued at 0:01:51.489 --> 0:01:54.730 the same time when you look at trade restrictions, they've 0:01:54.739 --> 0:01:58.250 been rising very steeply. And since well before the pandemic, 0:01:59.029 --> 0:02:01.309 if you look at the number of new trade restrictions 0:02:01.319 --> 0:02:04.290 that are being imposed in 2022 0:02:04.629 --> 0:02:08.320 there were almost 10 times as many trade restrictions imposed 0:02:08.490 --> 0:02:11.979 as the flow of trade restrictions about one decade ago 0:02:11.990 --> 0:02:16.399 in 2012. Um This is happening at a time when 0:02:16.410 --> 0:02:19.960 geopolitical concerns are rising to the forefront all over the 0:02:19.970 --> 0:02:24.089 world on a number of different issues, whether it's Russia, Ukraine, 0:02:24.100 --> 0:02:27.600 whether it's the Middle East. Um And this is, this 0:02:27.610 --> 0:02:29.729 can again be be seen tangibly in 0:02:29.809 --> 0:02:33.209 the economic sphere. For example, the IMF releases every year, 0:02:33.220 --> 0:02:38.228 a report on every single country's exchange rate restrictions. If 0:02:38.240 --> 0:02:42.149 you look at recent reports and see how many times 0:02:42.160 --> 0:02:45.788 the the the there is mention of the word national security, 0:02:45.970 --> 0:02:48.940 this has been rising very steeply. So you can see 0:02:48.949 --> 0:02:51.750 that this is coming to the forefront of policy discussions. 0:02:52.089 --> 0:02:55.888 Um If you look at earnings calls from the corporate sector, 0:02:55.899 --> 0:03:00.070 we've done text mining on corporate sector earnings calls and 0:03:00.080 --> 0:03:02.470 you find that the number of times they, they use 0:03:02.479 --> 0:03:07.429 words like friend shoring, reshoring has been rising exponentially in 0:03:07.440 --> 0:03:10.589 recent years. So all of this suggested to us 0:03:11.258 --> 0:03:15.800 a working definition of geo economic fragmentation, which we propose 0:03:15.809 --> 0:03:18.500 in the note that you refer to and which I 0:03:18.508 --> 0:03:22.279 think has now become or gained some measure of common acceptance. 0:03:22.479 --> 0:03:27.880 We define geo economic fragmentation as a policy driven reversal 0:03:27.889 --> 0:03:33.429 of global economic integration. It's a deliberately broad definition but 0:03:33.460 --> 0:03:37.830 not the policy driven part that's important. So obviously, 0:03:38.460 --> 0:03:41.639 le let's say that there's a slowdown in trade because 0:03:41.649 --> 0:03:45.320 consumer preferences shift from goods which tend to be tradable 0:03:45.330 --> 0:03:47.360 to services, which tend to be nontradable. 0:03:47.800 --> 0:03:50.729 Um As a result, you find some kind of shrinking 0:03:50.740 --> 0:03:54.779 of trade that's not geo economic fragmentation, that's simply consumer 0:03:54.789 --> 0:03:59.009 preference is changing. Similarly, if technological shifts occur or if 0:03:59.020 --> 0:04:02.460 there are changes in transportation or communication costs and these 0:04:02.470 --> 0:04:05.520 affect trade, that's not geo economic fragmentation, it has to 0:04:05.529 --> 0:04:08.419 be policy driven. So, so that's a crucial part of 0:04:08.429 --> 0:04:10.089 the definition. And 0:04:11.029 --> 0:04:12.809 so obviously, when we do this, 0:04:13.740 --> 0:04:17.230 there are some prudential policies such as macro prudential controls 0:04:17.238 --> 0:04:20.859 on certain capital account transactions, which we think are valid 0:04:20.869 --> 0:04:24.779 and legitimate. And you know, there's good economic reasoning. You 0:04:24.790 --> 0:04:28.880 don't want that to be defined as geo economic fragmentation. 0:04:29.079 --> 0:04:30.359 So we want to exclude 0:04:30.434 --> 0:04:33.875 prudential policies. But of course, when we do so, we 0:04:33.885 --> 0:04:38.045 recognize that there is no bright line between prudential policies 0:04:38.053 --> 0:04:41.445 and protectionist policies and often, you know, the latter can 0:04:41.454 --> 0:04:44.954 masquerade as the former. So, but this is the working 0:04:44.964 --> 0:04:47.054 definition and this is what we mean by the term 0:04:47.839 --> 0:04:48.178 good. 0:04:48.589 --> 0:04:51.178 Um I want to sort of, you know, build the 0:04:51.190 --> 0:04:53.149 key messages of the paper, but I want to add 0:04:53.160 --> 0:04:56.179 a couple of more concepts to this this area. So 0:04:56.190 --> 0:05:00.980 I suppose the opposite of geo economic fragmentation shaker is globalization, 0:05:01.209 --> 0:05:04.100 which is a much maligned word these days in certain 0:05:04.109 --> 0:05:09.500 political circles. So how would you assess the impact of globalization? 0:05:09.649 --> 0:05:14.000 Say from the nineties to the mid 20 tens after 0:05:14.010 --> 0:05:17.339 which we began to see strong evidence of geo economic fragmentation. 0:05:19.019 --> 0:05:21.040 Yeah, it's a, it's a very good question and a 0:05:21.049 --> 0:05:23.079 very good way of putting it and a very good 0:05:23.089 --> 0:05:27.239 way of thinking through it. So, you know, we've amassed 0:05:27.250 --> 0:05:31.250 immense amounts of evidence in the economics literature over the 0:05:31.260 --> 0:05:36.178 last half century about the great benefits that globalization brings 0:05:36.190 --> 0:05:37.109 through multiple channels. 0:05:37.178 --> 0:05:41.040 So that's an excellent starting point. Let's start with trade. 0:05:41.049 --> 0:05:44.440 That's the most obvious. There is a lot of evidence, 0:05:44.450 --> 0:05:49.320 for example, that international trade is strongly linked to economic growth. 0:05:49.410 --> 0:05:52.459 Um And this is this is an important bridge especially 0:05:52.470 --> 0:05:55.230 for developing countries to use as a ladder of development. 0:05:55.570 --> 0:05:57.579 You know, if you if you look at, let's say 0:05:57.589 --> 0:06:00.459 the last 30 or 40 years of data and you 0:06:00.470 --> 0:06:04.809 divide developing countries into globalizer, those which opened up their 0:06:04.820 --> 0:06:07.809 trade regimes and integrated more with the world where the 0:06:07.820 --> 0:06:12.510 trade to GDP ratio went up and non globalizer which 0:06:12.519 --> 0:06:15.850 remained relatively OIC did not open up to the world, 0:06:15.859 --> 0:06:16.480 et cetera. 0:06:16.988 --> 0:06:19.359 You find that over a long period of time, I'm 0:06:19.369 --> 0:06:22.730 talking about 30 or 40 years, there is an enormous 0:06:22.738 --> 0:06:26.988 difference between the growth rate of the globalizer and the 0:06:27.000 --> 0:06:28.059 non globalizer. 0:06:28.500 --> 0:06:32.409 Also, the globalizer have grown much faster than rich countries, 0:06:32.600 --> 0:06:36.730 thereby achieving convergence and catching up with Western living standards. 0:06:36.799 --> 0:06:39.529 This is what you and I studied when we looked 0:06:39.540 --> 0:06:43.390 at neoclassical growth models or solo models in graduate school. 0:06:43.510 --> 0:06:46.558 Uh Yes, it's happening but it's only happening for those 0:06:46.570 --> 0:06:49.750 developing countries which are globalizer, which are integrating with the 0:06:49.760 --> 0:06:53.359 global economy. It's not happening with the rest. So tremendous 0:06:53.369 --> 0:06:53.959 association 0:06:54.072 --> 0:06:58.493 between between trade and growth now intimately linked to that 0:06:58.733 --> 0:07:01.613 of course, is the success that the world has had 0:07:01.622 --> 0:07:04.972 with poverty reduction because we know that poverty reduction is 0:07:04.983 --> 0:07:08.692 intimately related to growth. So trade has had an enormous 0:07:08.702 --> 0:07:12.143 impact in kind of lifting up the developing world via 0:07:12.153 --> 0:07:16.722 convergence and reducing poverty. Um Another thing I would mention 0:07:16.733 --> 0:07:19.532 about trade and this is more to do with advanced economies. 0:07:19.726 --> 0:07:24.385 There's a lot of evidence that trade reduces consumer prices 0:07:24.496 --> 0:07:29.256 uh for the poorest consumers in advanced economies. So we're 0:07:29.265 --> 0:07:33.476 talking about, we're not talking about sort of richer college 0:07:33.485 --> 0:07:36.865 educated people who anyway tend to consume larger shares of 0:07:36.876 --> 0:07:40.145 services than goods. But when you look at low income 0:07:40.156 --> 0:07:45.126 consumers in advanced economies, they have benefited tremendously from trade. 0:07:45.536 --> 0:07:49.325 Um So that's, that's just trade. Then of course, 0:07:49.609 --> 0:07:52.609 let me, let me touch on technology diffusion, which is 0:07:52.619 --> 0:07:57.309 another enormous source for convergence and for kind of the, 0:07:57.320 --> 0:08:00.649 the uplift of the entire globe. Um We have very 0:08:00.660 --> 0:08:03.549 good evidence that FD I for example, and we'll get 0:08:03.559 --> 0:08:05.739 into this later when we discuss the second paper you 0:08:05.750 --> 0:08:10.160 had in mind, um can diffuse technology across borders. So, 0:08:10.170 --> 0:08:11.869 you know, every country in the world doesn't have to 0:08:11.880 --> 0:08:12.510 re invent the 0:08:13.225 --> 0:08:16.703 uh developing countries can take advantage of progress that's been 0:08:16.714 --> 0:08:20.005 made at the technological frontier and it can catch up 0:08:20.015 --> 0:08:22.494 and during the catch up phase, it can enjoy faster 0:08:22.505 --> 0:08:25.355 rates of growth. But of course, this can only happen 0:08:25.364 --> 0:08:28.065 if there's some channel for technology diffusion and the channels 0:08:28.075 --> 0:08:31.274 for technology diffusion tend to be things like trade, foreign 0:08:31.285 --> 0:08:35.544 direct investment participation in global value chains. Um 0:08:36.190 --> 0:08:38.289 Let me come to one more which is kind of 0:08:38.299 --> 0:08:42.340 close to my heart, which is migration. Uh I know 0:08:42.349 --> 0:08:45.630 that migration can be very controversial. Certainly it's a big 0:08:45.640 --> 0:08:49.159 political football in a lot of Western countries. But again, 0:08:49.169 --> 0:08:50.369 it is one of those 0:08:50.729 --> 0:08:53.830 things which and this is true actually for millennia, it's 0:08:53.840 --> 0:08:56.929 not just true of the last five decades. Migration is 0:08:56.940 --> 0:09:02.460 a primary uh mover of the diffusion of ideas and 0:09:02.469 --> 0:09:05.949 technologies and knowledge throughout the world, right? It's, it's happened 0:09:05.960 --> 0:09:09.150 throughout history. It's, it's not a recent phenomenon. Um 0:09:09.500 --> 0:09:11.700 And you know, just to look at recent history, there's 0:09:11.710 --> 0:09:14.599 ample evidence if you look at Silicon Valley, for example, 0:09:14.750 --> 0:09:17.179 and if you look at the number of immigrants and 0:09:17.190 --> 0:09:21.468 the amount of dynamism that immigrants have brought uh to and, 0:09:21.479 --> 0:09:24.349 and that's, that's the leading edge of world technology, right? 0:09:24.809 --> 0:09:28.179 In the US, there's ample evidence that immigrants are more 0:09:28.190 --> 0:09:31.099 likely to have college degrees. They are more likely to 0:09:31.109 --> 0:09:34.739 have stem certifications. They are more likely to start a 0:09:34.750 --> 0:09:38.710 new business, they are more likely to patent. Um There's 0:09:38.719 --> 0:09:41.929 this amazing statistic which might appeal to you Temur, 0:09:42.539 --> 0:09:46.358 if you just look at ethnic Chinese and ethnic Indians 0:09:46.369 --> 0:09:49.940 living in Silicon Valley, just that small group of ethnic 0:09:49.950 --> 0:09:54.609 Indians and Chinese, the amount they account for 11% of 0:09:54.619 --> 0:09:56.950 all patents in America, 0:09:57.320 --> 0:10:01.460 their patent output is greater than the combined patent output 0:10:01.469 --> 0:10:04.409 of the bottom 28 states in the US. We're just 0:10:04.419 --> 0:10:08.099 talking about Chinese and Indian ethnic investors living in the 0:10:08.109 --> 0:10:11.380 San Francisco Bay area. So yes, I mean, you know, 0:10:11.390 --> 0:10:14.619 they're a tremendous source of dynamism and it works both ways. 0:10:14.640 --> 0:10:15.609 So when you have immigrants 0:10:15.674 --> 0:10:19.255 abroad, they send back remittances, we all know that Remittance, 0:10:19.265 --> 0:10:23.304 remittances tend to be countercyclical, remittances were much more stable 0:10:23.315 --> 0:10:27.294 during the COVID-19 pandemic than other types of capital flows. So, 0:10:27.455 --> 0:10:32.414 so they're countercyclical, they help with macroeconomic stabilization um and 0:10:32.424 --> 0:10:33.875 they help with poverty reduction. 0:10:34.219 --> 0:10:37.710 Um And then you have diaspora effect. So I think 0:10:37.719 --> 0:10:42.150 the success of, let's say the Taiwanese semiconductor industry can 0:10:42.159 --> 0:10:45.189 be traced to earlier waves of skilled immigrants who went 0:10:45.200 --> 0:10:47.929 to Silicon Valley and Wall Street and then brought this 0:10:47.940 --> 0:10:52.669 technological expertise back with them to this small, backward, agriculturally 0:10:52.679 --> 0:10:56.559 dominant island, which today is a global leader of high tech. 0:10:56.690 --> 0:10:57.299 So 0:10:57.809 --> 0:11:01.159 migration, I think is another channel which is a threat 0:11:01.349 --> 0:11:05.069 uh given geo economic fragmentation. Let, let me stop there. 0:11:05.080 --> 0:11:07.228 I could go on on this forever. But that's, that's, 0:11:07.239 --> 0:11:13.070 that's three things, trade, um FD I migration, tremendous benefits 0:11:13.080 --> 0:11:15.419 of globalization. And of course, all of this could go 0:11:15.429 --> 0:11:17.669 into reverse with geo economic fragmentation. 0:11:18.150 --> 0:11:21.239 OK. Very compelling Shekhar. You may or may not have 0:11:21.250 --> 0:11:23.960 noticed that I smiled when you talked about convergence and 0:11:23.969 --> 0:11:25.609 I'll take a personal detail to tell you why I 0:11:25.619 --> 0:11:30.309 smiled over two decades ago. I was at a party 0:11:30.320 --> 0:11:34.659 in Van Nest in Washington DC. And I heard a 0:11:34.669 --> 0:11:38.718 new economist in the IMF who just joined talk adly 0:11:38.729 --> 0:11:40.090 about conditional convergence. 0:11:40.219 --> 0:11:43.400 It was you. And so so when you mentioned that 0:11:43.719 --> 0:11:45.709 you really took me back to the very first days 0:11:45.719 --> 0:11:48.049 when I got to know you. All right, back to 0:11:48.059 --> 0:11:51.000 the uh podcast. All right. So you made very compelling 0:11:51.010 --> 0:11:55.960 arguments in terms of the benefits from globalization and the 0:11:55.969 --> 0:11:59.830 fact that there is volumes of, there are volumes of 0:11:59.840 --> 0:12:02.200 empirical evidence to, to establish that point. 0:12:02.549 --> 0:12:07.049 Um We don't have a very large data set for 0:12:07.330 --> 0:12:10.760 geo economic fragmentation, maybe a decade, maybe 12 years. So 0:12:10.770 --> 0:12:14.090 how does one measure robustly the cost of geo economic fragmentation? 0:12:15.719 --> 0:12:19.900 Uh Very good question. Um Although I should say that this, 0:12:19.909 --> 0:12:23.108 you know, your previous remarks demonstrate the perils of doing 0:12:23.119 --> 0:12:25.789 podcasts with people who remember going to parties with you 0:12:25.799 --> 0:12:30.319 20 years ago. Um Look, this is a new literature. 0:12:30.330 --> 0:12:32.949 It's a field which is in its infancy, it's just 0:12:32.960 --> 0:12:36.369 developing uh in the and because we don't have so 0:12:36.380 --> 0:12:40.150 many years of geo economic fragmentation compared to the many 0:12:40.159 --> 0:12:43.159 decades of globalization, it will be a while before we 0:12:43.169 --> 0:12:43.890 can do 0:12:44.179 --> 0:12:47.710 you know, very good analytically rigorous work on this that 0:12:47.719 --> 0:12:52.080 said in the Sdn we review four recent papers which 0:12:52.090 --> 0:12:54.569 try to estimate the cost of fragmentation 0:12:54.960 --> 0:12:58.718 the way it's done these days is through modeling exercises. 0:12:58.729 --> 0:13:02.770 So essentially all the four papers we review model geo 0:13:02.780 --> 0:13:07.059 economic fragmentation in some way. They set up blocks of countries, 0:13:07.109 --> 0:13:11.520 they introduce trade barriers or investment barriers between the blocks. 0:13:11.789 --> 0:13:15.460 They have very different assumptions about the severity of the 0:13:15.565 --> 0:13:20.473 barriers between blocks. They have different definitions of the blocks themselves. 0:13:20.484 --> 0:13:23.343 In some of the papers, non aligned countries are allowed 0:13:23.354 --> 0:13:26.005 which don't belong to either block. This is just a 0:13:26.155 --> 0:13:28.945 long way of saying that the four papers are extremely 0:13:28.955 --> 0:13:33.184 disparate from each other. Nonetheless, all of them show significant costs, 0:13:33.195 --> 0:13:36.064 global costs of geo economic fragmentation. 0:13:36.309 --> 0:13:39.729 And depending on the severity of the assumptions, uh you 0:13:39.739 --> 0:13:43.929 can get very large results up to 8% global losses 0:13:44.099 --> 0:13:48.500 and much higher losses for individual countries up to 12% 15%. 0:13:48.710 --> 0:13:51.039 Uh depending on the scenario that you have in mind. 0:13:51.450 --> 0:13:54.619 Um There are some common lessons however, that seem to 0:13:54.630 --> 0:13:56.869 emerge from these studies. Let me just go through some 0:13:56.880 --> 0:14:01.049 of them. First, the deeper the fragmentation, the larger the 0:14:01.059 --> 0:14:03.299 estimated losses. What do we, what do I mean by 0:14:03.309 --> 0:14:04.520 deeper the fragmentation, 0:14:04.979 --> 0:14:09.119 there's many parameters in these models which could act to 0:14:09.130 --> 0:14:13.630 deepen the fragmentation uh for one thing, greater barriers between 0:14:13.640 --> 0:14:16.530 the blocks, right? So typically in these models, you put 0:14:16.539 --> 0:14:18.650 some kind of tariff barriers or some kind of non 0:14:18.679 --> 0:14:22.510 tariff barriers between the two blocks, depending on how, how 0:14:22.520 --> 0:14:24.460 restrictive you make those uh 0:14:25.284 --> 0:14:29.465 you get larger output losses for the globe. Um Second, 0:14:29.474 --> 0:14:32.815 do you allow non-aligned countries? So, you know, if you 0:14:32.825 --> 0:14:36.505 allow non-aligned countries, then it's a less fragmented world. If 0:14:36.515 --> 0:14:39.135 you force every country to choose one block, then that's 0:14:39.145 --> 0:14:41.135 a deeper level of fragmentation. So 0:14:42.020 --> 0:14:45.020 the deeper the fragmentation, the higher the loss is second, 0:14:45.969 --> 0:14:50.630 if you put technological diffusion on top of trade barriers, 0:14:50.640 --> 0:14:53.109 then the losses are much greater. You know, we talked 0:14:53.119 --> 0:14:55.729 about this a bit earlier. There are modeling techniques which 0:14:55.739 --> 0:14:57.919 allow you to do this. And when you do this, 0:14:57.929 --> 0:15:00.530 you find that the losses are higher. Third, 0:15:01.229 --> 0:15:06.020 the losses vary widely among countries. They're not heterogenous. And 0:15:06.030 --> 0:15:06.929 in particular, 0:15:07.679 --> 0:15:10.929 the losses tend to be higher for emerging markets and 0:15:10.940 --> 0:15:14.739 developing economies. And this is not surprising because as we discussed, 0:15:14.750 --> 0:15:17.909 they tend to be further back from the technological frontier 0:15:18.090 --> 0:15:20.409 and they can benefit a lot from trade and FD 0:15:20.419 --> 0:15:23.710 I in terms of technological diffusion, if you remove that, 0:15:23.719 --> 0:15:25.840 you remove an important source of growth, so they are 0:15:25.849 --> 0:15:30.710 very badly hurt. And then finally, um short term elastic 0:15:30.719 --> 0:15:33.880 cities of substitution are likely to be smaller than long 0:15:33.890 --> 0:15:35.849 run elastic cities of substitution. 0:15:36.469 --> 0:15:38.840 And that means that even the cost that you see 0:15:39.000 --> 0:15:41.669 in the papers that we survey are likely to be 0:15:41.679 --> 0:15:44.140 an underestimate for the short run because these are all 0:15:44.150 --> 0:15:47.840 long run equilibrium numbers in the short run, you could 0:15:47.849 --> 0:15:52.039 have uh disruptions to output, which are many multiples of 0:15:52.049 --> 0:15:53.429 what we find in the long run. 0:15:54.190 --> 0:15:57.789 Um So that's, that's just, you know, some flavor of, 0:15:57.799 --> 0:16:00.510 of where the literature has been going. I should also 0:16:00.520 --> 0:16:04.150 mention that, that I did some work with IMF co 0:16:04.289 --> 0:16:08.830 authors specifically on FD I fragmentation and FD I fragmentation 0:16:08.840 --> 0:16:12.000 is quite interesting because it's taking the literature in a 0:16:12.010 --> 0:16:15.250 new direction I think. And the basic idea is simple, 0:16:15.260 --> 0:16:16.599 what we do is we take 0:16:17.099 --> 0:16:20.479 voting patterns from the United Nations and there's a few 0:16:20.489 --> 0:16:23.539 academics at Harvard who have built up a database which, 0:16:23.549 --> 0:16:27.849 which show for any pair of countries how far apart 0:16:27.859 --> 0:16:32.159 they are geopolitically based on un voting patterns. And the 0:16:32.169 --> 0:16:34.280 nice thing about this database is that it gives you 0:16:34.289 --> 0:16:35.830 a geopolitical distance 0:16:36.260 --> 0:16:40.320 of bilateral geopolitical distance which is time varying. So then 0:16:40.330 --> 0:16:44.429 this is like extremely nice for all kinds of econometric work. 0:16:44.590 --> 0:16:46.719 And the way we do it is we try to 0:16:46.729 --> 0:16:51.359 establish relationships, you know, we divide the world into quintiles. 0:16:51.380 --> 0:16:53.609 And in the first quintiles are countries which are quite 0:16:53.619 --> 0:16:57.239 close together geopolitically according to this measure that I've talked about. 0:16:57.539 --> 0:16:59.919 And then the final quintile is countries which are quite 0:16:59.929 --> 0:17:01.479 far apart from each other. 0:17:01.809 --> 0:17:05.359 And when you follow these quintiles over time, you find 0:17:05.369 --> 0:17:08.540 that they are becoming very important in determining trade patterns. 0:17:08.550 --> 0:17:12.030 In particular, the share of world trade that is taking 0:17:12.040 --> 0:17:14.639 place among countries which are similar to each other in 0:17:14.650 --> 0:17:17.909 terms of geopolitical alignment is going up steeply. 0:17:18.640 --> 0:17:22.979 So it's already more important than geographical distance. And over time, 0:17:23.260 --> 0:17:26.560 its importance seems to be increasing further. Then you can 0:17:26.569 --> 0:17:28.530 be more rigorous about it and put it into a 0:17:28.540 --> 0:17:31.979 gravity model, which we do. And indeed, we find that 0:17:31.989 --> 0:17:37.280 there's a strong correlation between FD patterns and geopolitical distance. 0:17:38.099 --> 0:17:40.300 So let me, let me give you, let me try 0:17:40.310 --> 0:17:44.919 to distill so you know, consider the geopolitical distance between 0:17:45.050 --> 0:17:49.130 France and the UK. They, they're quite close together, obviously, 0:17:49.369 --> 0:17:53.359 um if you move from that geopolitical distance to the 0:17:53.369 --> 0:17:58.150 distance between France and India, which are much further apart geopolitically, 0:17:58.439 --> 0:18:02.410 you would, you can, you can relate that to a 17% 0:18:02.420 --> 0:18:03.589 decline in FD I. 0:18:04.180 --> 0:18:06.790 So that just gives you an idea of how economically 0:18:06.800 --> 0:18:10.160 powerful geopolitical distance can be. Um 0:18:10.979 --> 0:18:14.449 And then, yeah, then so once you have those parameters about, 0:18:14.459 --> 0:18:17.810 you know how important geo economic fragmentation can be for 0:18:17.819 --> 0:18:20.458 FD I, then you can feed that into a model. 0:18:20.650 --> 0:18:24.260 So we use the IMF SGM model to divide the 0:18:24.270 --> 0:18:27.439 world into different blocks and then to put barriers between 0:18:27.449 --> 0:18:28.449 blocks and then 0:18:28.560 --> 0:18:32.810 use these gravity estimates to parameterize the model and calculate 0:18:32.819 --> 0:18:35.439 global losses. And we find that you can get global 0:18:35.449 --> 0:18:39.209 losses from FD I fragmentation of 2% but the losses 0:18:39.219 --> 0:18:42.040 are much higher for some countries than for others. In particular, 0:18:42.050 --> 0:18:46.130 they are much higher for developing countries than for advanced economies. 0:18:47.359 --> 0:18:49.859 Um Shekar two follow up questions. One is if I 0:18:49.869 --> 0:18:53.129 were to rank all the economies in the world by 0:18:53.140 --> 0:18:56.380 say trade GDP ratio, so you know, a proxy for 0:18:56.390 --> 0:19:00.170 openness or trade intensity, uh would it be fair to 0:19:00.180 --> 0:19:04.280 say that that ranking would be congruent with the vulnerability 0:19:04.290 --> 0:19:07.699 of those economies that the most integrated are the most 0:19:07.709 --> 0:19:09.739 vulnerable to economic fragmentation? 0:19:12.130 --> 0:19:17.030 Yes, although I think there are some subtleties. So, so yes, 0:19:17.040 --> 0:19:19.569 I mean, what what you can do and what we 0:19:19.579 --> 0:19:22.819 do in the in the Rio chapter. Uh for example, 0:19:22.880 --> 0:19:25.989 is to develop an index of geo economic vulnerability 0:19:26.219 --> 0:19:28.819 and it's quite easy to do that because you have 0:19:28.829 --> 0:19:32.810 this index of um geopolitical distance. So what you can 0:19:32.819 --> 0:19:36.290 do is you can wait geopolitical distance by trade shares. 0:19:36.520 --> 0:19:39.609 And if you're trading a lot with countries that are 0:19:39.619 --> 0:19:42.708 very far away from you geopolitically, then mechanically you are 0:19:42.719 --> 0:19:44.040 more vulnerable, right? 0:19:44.900 --> 0:19:45.439 Um 0:19:46.089 --> 0:19:48.569 Now, the reason I'm saying is this is a little 0:19:48.579 --> 0:19:51.739 more subtle is that more recently, people are talking about 0:19:51.750 --> 0:19:56.000 connector countries. And the idea here is that if you 0:19:56.010 --> 0:20:00.949 have a diversified trading base, then in some sense that 0:20:00.959 --> 0:20:05.390 builds resilience because if there's a geopolitical spat with one 0:20:05.400 --> 0:20:07.149 particular set of, of global 0:20:07.319 --> 0:20:10.859 factors because of your diversification and the fact that you're 0:20:10.869 --> 0:20:14.069 trading with a lot of people, there's some insulation from 0:20:14.079 --> 0:20:17.770 that shock. Uh People are also talking about connectors in 0:20:17.780 --> 0:20:20.910 terms of linking hostile blocks to each other. So people 0:20:20.920 --> 0:20:24.319 like Laura Pau at Harvard have been looking at countries 0:20:24.329 --> 0:20:27.599 like Mexico, which is getting a lot of FT I 0:20:27.609 --> 0:20:28.410 from China 0:20:28.910 --> 0:20:31.198 at the same time that it's increasing its exports to 0:20:31.209 --> 0:20:33.669 the US. So in some sense, you could think of 0:20:33.680 --> 0:20:37.510 Mexico as being a connected country between China and the US. So, 0:20:37.520 --> 0:20:40.550 so lots of work going on in this area. Um Yes, 0:20:40.560 --> 0:20:43.810 if you trade a lot with a lot of diverse countries, 0:20:43.849 --> 0:20:44.579 uh you may be 0:20:44.655 --> 0:20:48.275 vulnerable to the extent that you're trading with with people 0:20:48.285 --> 0:20:50.994 who are geopolitically distant from you. On the other hand, 0:20:51.005 --> 0:20:53.895 if you're doing this very diversely and you're doing it 0:20:53.905 --> 0:20:55.954 with a lot of people, then you can be a 0:20:55.964 --> 0:20:59.555 connector as well. Uh So, so there's some pluses and 0:20:59.564 --> 0:21:00.494 some minuses, 0:21:01.084 --> 0:21:05.074 right? I'm compelled by the Mexico example because I think 0:21:05.244 --> 0:21:09.113 Mexico has been a big beneficiary of some of these 0:21:09.125 --> 0:21:11.555 restriction measures that the US has been putting in and 0:21:11.564 --> 0:21:12.555 countries like China 0:21:12.930 --> 0:21:16.839 uh have thousands of companies if not more uh in 0:21:16.849 --> 0:21:20.410 Mexico trading with the US under US MC A. But 0:21:20.420 --> 0:21:24.050 I think this has been noticed by the proponent of 0:21:24.060 --> 0:21:27.050 socio-economic fragmentation and therefore, we could see an undermining of 0:21:27.060 --> 0:21:29.839 us MC A uh if say Trump were to come 0:21:29.849 --> 0:21:32.149 to power because I think there's also 0:21:32.390 --> 0:21:36.119 been some sort of promise made that if Chinese made 0:21:36.130 --> 0:21:39.280 EVs from Mexico or have their way to the US, 0:21:39.290 --> 0:21:41.650 there will be like 100% tired from them US MC 0:21:41.660 --> 0:21:45.290 A be damned. Um So uh so yeah, uh Mexico 0:21:45.300 --> 0:21:47.680 is indeed an interesting example of how at least in 0:21:47.689 --> 0:21:50.270 the short term it can be a buffer, but uh 0:21:50.280 --> 0:21:51.400 it's a dynamic game, 0:21:51.410 --> 0:21:51.729 right? 0:21:52.390 --> 0:21:54.250 Let me, let me jump in. I think that's an 0:21:54.260 --> 0:21:56.869 excellent observation and I think this kind of brings us 0:21:56.880 --> 0:22:00.079 to the cutting edge of the literature. So uh so yes, 0:22:00.089 --> 0:22:02.579 so there are these papers talking about Mexico as a 0:22:02.589 --> 0:22:05.209 great connector. I share your skepticism 0:22:05.560 --> 0:22:09.339 if you saw Donald Trump's speech at the Republican National Convention, 0:22:09.349 --> 0:22:13.520 he explicitly mentioned we're not going to allow the Chinese 0:22:13.530 --> 0:22:17.420 to build automobile factories in Mexico and then export them 0:22:17.430 --> 0:22:20.760 to the US. Uh That's on the Republican side. But 0:22:20.770 --> 0:22:24.129 if you look at the trade representative Catherine Sai under 0:22:24.140 --> 0:22:28.260