WEBVTT - What a 150-Year Old Indian Railway System Tells Us About Trade 0:00:00.160 --> 0:00:02.320 But knowledge to work and grow your business with c 0:00:02.520 --> 0:00:06.680 i T from transportation to healthcare to manufacturing. C i 0:00:06.760 --> 0:00:10.520 T offers commercial lending, leasing, and treasury management services for 0:00:10.600 --> 0:00:13.480 small and middle market businesses. Learn more at c i 0:00:13.560 --> 0:00:16.400 T dot com Put Knowledge to Work. We want to 0:00:16.400 --> 0:00:18.400 take a quick moment to let you know about something 0:00:18.640 --> 0:00:23.479 really new that's cool from Bloomberg. Starting right now, you 0:00:23.520 --> 0:00:26.120 can use our io s app or Google Chrome extension 0:00:26.200 --> 0:00:29.560 to scan the news, look at any story on any website, 0:00:29.600 --> 0:00:34.240 and instantly bring up news and information from Bloomberg relevant 0:00:34.280 --> 0:00:36.519 to what you're reading about. 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Learn more at Bloomberg dot com slash lens. 0:01:30.080 --> 0:01:33.960 Hello and welcome to another edition of the Ad Thoughts Podcast. 0:01:34.000 --> 0:01:37.800 I'm Tracy Alloway and I'm Joe wisntal So, Joe, I'm 0:01:37.840 --> 0:01:41.840 trying to think have we ever had a Nobel Prize 0:01:41.840 --> 0:01:46.720 winner on the show? Can you remember? I don't think so. 0:01:47.120 --> 0:01:49.040 I don't think we have. Yeah, I don't think we 0:01:49.080 --> 0:01:51.240 have either um and not to get your hopes up, 0:01:51.480 --> 0:01:55.559 we don't have one UM for this episode either. Shoot. Yeah, 0:01:56.080 --> 0:01:59.320 very disappointing. We do have someone who has a really 0:01:59.320 --> 0:02:02.920 good shot at eventually, UM getting one. We actually have 0:02:03.200 --> 0:02:07.480 the recent winner of the latest John Bates Clark Metal. 0:02:07.920 --> 0:02:11.399 It's a medal that's given by the American Economics Association 0:02:11.560 --> 0:02:15.800 to economists under forty who make big contributions to the 0:02:15.840 --> 0:02:19.720 field of economics. Isn't it also true that among economists 0:02:19.760 --> 0:02:22.040 the John Bates Clark Medal is actually considered to be 0:02:22.120 --> 0:02:26.959 more impressive and prestigious than the Nobel Prize. You're probably right, 0:02:27.280 --> 0:02:30.680 I mean, statistically, I think the recipients go on to 0:02:30.720 --> 0:02:33.399 win a Nobel and of course the John Bates one 0:02:33.560 --> 0:02:39.240 is confined to young economists, so that's pretty impressive. Another thing, uh, 0:02:39.480 --> 0:02:42.799 that sort of will make today's episode distinct is it's 0:02:42.880 --> 0:02:45.120 kind of timely. So I kind of like the fact 0:02:45.160 --> 0:02:48.839 that on our podcast we usually talk about things that 0:02:48.919 --> 0:02:51.440 aren't really in the news. We sort of give people 0:02:51.520 --> 0:02:54.839 a rest bite from the top stories of the day 0:02:54.919 --> 0:02:57.480 and just let them explore something completely out there. But 0:02:57.800 --> 0:03:01.880 this one might actually be a little bit on the news. Yeah, 0:03:02.000 --> 0:03:06.240 So we are definitely huge fans of financial and markets 0:03:06.280 --> 0:03:10.160 and economic history. And the guy that won this medal, 0:03:10.200 --> 0:03:11.919 I should just go ahead and say his name. It's 0:03:12.120 --> 0:03:16.400 David Donaldson. He's an associate professor of economics at Stanford. 0:03:16.720 --> 0:03:20.880 But Professor Donaldson is basically famous for being a sort 0:03:20.919 --> 0:03:25.560 of trade economic historian. And he's famous for one paper 0:03:25.600 --> 0:03:29.920 in particular. It's called Railroads of the Raj and it 0:03:30.040 --> 0:03:33.880 basically went back and looked at the railway network that 0:03:33.960 --> 0:03:37.400 was built up by the British in the late eighteen 0:03:37.440 --> 0:03:40.880 hundreds early nineteen hundreds in India and he used that 0:03:41.000 --> 0:03:45.200 to check what impact building that infrastructure has on trade 0:03:45.400 --> 0:03:49.800 and general incomes in India. So I mean imagine putting 0:03:49.840 --> 0:03:53.840 that paper together, right, It definitely sounds like it would 0:03:53.840 --> 0:03:57.080 be quite a task. All right, well should we just 0:03:57.120 --> 0:04:09.960 go ahead and ask him. Let's let's bring him in. David, 0:04:10.000 --> 0:04:12.440 thank you so much for joining us today. Hey guys, 0:04:12.440 --> 0:04:16.279 thanks for having me. You know, we just mentioned probably 0:04:16.400 --> 0:04:20.000 your most famous paper on railroads of the raj and 0:04:20.240 --> 0:04:22.800 that was a paper you started, I think when you 0:04:22.839 --> 0:04:25.800 were a grad student. Um, but you worked on it 0:04:25.880 --> 0:04:28.000 for years and years and years, and if I'm right, 0:04:28.080 --> 0:04:33.159 it's it's still actually hasn't been published. It's still forthcoming, right. Yeah, 0:04:33.200 --> 0:04:38.240 I've been I've been very slow in many ways. So um, 0:04:38.279 --> 0:04:41.040 but yeah, it's something that you know, has occupied my 0:04:41.360 --> 0:04:43.880 interest for a long time. It struck me as a 0:04:44.839 --> 0:04:47.240 you know, among other episodes in world history, one of 0:04:47.360 --> 0:04:50.760 one of the great episodes that integrated economy has made 0:04:50.760 --> 0:04:53.320 it easier for one market to trade with another market, 0:04:54.400 --> 0:04:58.479 and that trade, that integration happens obviously internationally, things like 0:04:58.520 --> 0:05:01.120 the Panama Canal or the sewer Is Canal, the invention 0:05:01.160 --> 0:05:04.599 of the steamship did that across oceans, and then you know, 0:05:04.600 --> 0:05:07.160 the railroads sort of did that within countries like India 0:05:07.200 --> 0:05:10.679 and of course famously also the United States. So before 0:05:10.680 --> 0:05:14.120 we even get to what you did to work on 0:05:14.160 --> 0:05:19.200 this paper and or even the conclusions, What originally sparked 0:05:19.240 --> 0:05:23.320 your interest in this particular event from history. Well, actually 0:05:23.320 --> 0:05:25.799 I was. I was a grad student, and I knew 0:05:25.839 --> 0:05:29.080 I was interested in trade, but and I knew I 0:05:29.120 --> 0:05:33.200 was particularly interested in intra national trade, that is, kind 0:05:33.200 --> 0:05:36.360 of trade across regions of the same country, which has 0:05:36.440 --> 0:05:39.920 typically always been hard to study, just for for lack 0:05:39.960 --> 0:05:43.640 of data. Basically, when when goods cross international borders, customs 0:05:43.680 --> 0:05:46.880 agencies have always tax that you know, not always, but 0:05:47.120 --> 0:05:50.320 the most part most countries throughout history of taxed international trade, 0:05:50.320 --> 0:05:52.479 and so they've kept a record of the flow of 0:05:52.520 --> 0:05:56.160 international trades, whereas typically when goods moved within countries they 0:05:56.200 --> 0:06:00.320 don't get either tax or or recorded, and we don't 0:06:00.320 --> 0:06:03.520 know much about it. But I was always interested in India, 0:06:03.640 --> 0:06:05.920 and I got got wind to the fact that at 0:06:05.920 --> 0:06:08.280 the time when I was doing my doctoral work, and 0:06:08.400 --> 0:06:11.480 in fact almost still to this to this day, India 0:06:11.680 --> 0:06:16.320 had had sort of domestic tariffs, tariffs on a movement 0:06:16.320 --> 0:06:20.159 of goods across state boundaries within India, and I wanted 0:06:20.200 --> 0:06:22.080 to know more about that. I wanted to kind of 0:06:22.120 --> 0:06:24.120 scope it out. So I went to India for for 0:06:24.160 --> 0:06:26.440 a couple of weeks one summer and in order to 0:06:26.480 --> 0:06:30.599 meet with people in government, people in academia, and librarians 0:06:30.720 --> 0:06:32.160 in in order to try to sort of find out 0:06:32.240 --> 0:06:34.719 the facts. And actually it was it was in that 0:06:34.760 --> 0:06:37.919 process that I met Hovant. You know, just heard this 0:06:38.040 --> 0:06:40.440 kind of endless refrain. Well, you know, the thing that 0:06:40.520 --> 0:06:43.960 really brought India together is was the railroads opposite of 0:06:43.960 --> 0:06:46.839 about a hundred years ago? And that, I guess pique 0:06:46.880 --> 0:06:48.719 my interest. I assume there would be no data, you 0:06:48.760 --> 0:06:50.960 know that that obviously the further you go back in time, 0:06:51.000 --> 0:06:55.160 that's typically the the less data that survives. And so 0:06:55.240 --> 0:06:56.920 I thought, well, I guess I could at least sort 0:06:56.960 --> 0:06:59.880 of explore what exists in the in the libraries on 0:07:00.040 --> 0:07:03.479 that historical year. And I was just shocked to learn 0:07:03.560 --> 0:07:06.320 what was actually available, you know, um, what they've recorded 0:07:06.320 --> 0:07:09.360 back down back then, what they published, and what still survived. 0:07:09.840 --> 0:07:13.800 So one thing about the British railway system is it's 0:07:13.880 --> 0:07:18.240 famous for being absolutely massive in India, I mean hundreds 0:07:18.400 --> 0:07:23.080 of thousands of miles worth of railway. How how did 0:07:23.120 --> 0:07:28.960 you go about collecting that data and then isolating certain 0:07:29.320 --> 0:07:32.800 effects away from other effects because I imagine, like, if 0:07:32.800 --> 0:07:35.640 you look at the fact that one railway line has 0:07:35.680 --> 0:07:39.960 been built between one county and another town or whatever, 0:07:40.720 --> 0:07:43.160 that's going to have an impact on all sorts of things. Right, 0:07:43.160 --> 0:07:46.440 It's almost like a tree diagram, Like the impact just 0:07:46.560 --> 0:07:49.920 keeps spreading and spreading. So how do you I have 0:07:50.000 --> 0:07:52.840 so many questions, how do you go about kind of 0:07:53.280 --> 0:07:59.040 limiting that impact? There's no easy, easy way to do that, 0:07:59.080 --> 0:08:02.720 and I don't claim to have um necessarily nailed it 0:08:02.760 --> 0:08:06.120 by any means. You know, the the ideal way you 0:08:06.160 --> 0:08:09.400 know that any scientist learns about the world around us 0:08:09.560 --> 0:08:12.720 is by something like an experiment, right, either a formal 0:08:12.800 --> 0:08:16.840 lab experiment or in less controlled settings where you can't 0:08:16.960 --> 0:08:19.080 you know, a lab experiments just advantageous because you know 0:08:19.120 --> 0:08:23.160 that you can control everything except the one treatment that 0:08:23.160 --> 0:08:25.360 you're trying to sort of study the effect of. But 0:08:25.480 --> 0:08:27.840 sometimes you can sort of make sure that the treatment 0:08:28.000 --> 0:08:31.160 is randomly assigned. That way, you know that on average 0:08:31.640 --> 0:08:34.040 across the sort of subjects, if you like in in 0:08:34.080 --> 0:08:38.360 the experiment, those differences will cancel out on average. And 0:08:38.400 --> 0:08:41.560 so social scientists like me are always you know, looking 0:08:41.600 --> 0:08:44.440 for things that that hopefully. You know, we we think 0:08:44.480 --> 0:08:47.640 there's a case to be made for your features of 0:08:47.800 --> 0:08:50.800 the way that the sub program was allocated to the 0:08:50.800 --> 0:08:54.960 world having some quasi sort of random element to them. 0:08:55.400 --> 0:08:57.120 And of course we never know if that's actually the case, 0:08:57.120 --> 0:09:01.600 except in rare, rare instances where social scientists him herself 0:09:01.720 --> 0:09:05.760 or the policymaker actually explicitly decided to randomize. But that's 0:09:05.760 --> 0:09:08.600 of course extremely rare. Much more likely is the case 0:09:08.720 --> 0:09:12.000 like the Indian railroads, where they had a complicated decision 0:09:12.040 --> 0:09:15.600 process about where and when to um to roll out 0:09:15.600 --> 0:09:18.320 the network. And and I should say, all that is 0:09:18.520 --> 0:09:21.000 um it doesn't even really address the heart of your question, 0:09:21.080 --> 0:09:23.000 which is that there will be spillovers. You know. That 0:09:23.120 --> 0:09:26.120 is to say, in a classic lab experiment, you have 0:09:26.160 --> 0:09:28.559 a treatment group that randomly is chosen to get the 0:09:28.600 --> 0:09:31.720 drug or something in the control group is randomly chosen 0:09:31.760 --> 0:09:34.720 not to. But there's a serious problem if you think 0:09:34.760 --> 0:09:38.360 that it's possible that your control group is affected. Sort 0:09:38.360 --> 0:09:40.440 Of one way to think about it is they're the 0:09:40.559 --> 0:09:42.800 very fact that they were the control group means they're affected. 0:09:42.800 --> 0:09:44.240 The other way to think about it as closer to 0:09:44.280 --> 0:09:46.280 what you said, which is that some of the treatment 0:09:46.360 --> 0:09:48.920 spills over onto them. They're sort of partially effected. Even 0:09:48.960 --> 0:09:52.080 they weren't directly affected, they were partially affected. Okay, so 0:09:52.120 --> 0:09:55.480 that's the big backdrop against which we tend to think 0:09:55.480 --> 0:09:58.520 about these things. UM and you know you need you 0:09:58.559 --> 0:10:01.120 need help from the re I think it's fair to 0:10:01.120 --> 0:10:04.400 say that we don't. There's no you know, I I did. 0:10:04.400 --> 0:10:06.400 In principle, you could design an experiment that which is 0:10:06.520 --> 0:10:09.720 kind of completely ideally nail everything. But in the in 0:10:09.760 --> 0:10:13.280 the real world, we don't have enough statistical power to 0:10:13.280 --> 0:10:16.040 to follow all those spillover effects in their kind of 0:10:16.080 --> 0:10:18.360 manifold directions. As you said, this kind of four king 0:10:18.360 --> 0:10:21.400 tree just makes it. It's sort of that basically impossible 0:10:21.400 --> 0:10:23.200 with the kind of data, the kind of world we 0:10:23.240 --> 0:10:27.080 live in. UM, so you need so economists you know 0:10:27.480 --> 0:10:33.280 like me sort of turned to um hopefully fairly uncontroversial 0:10:33.880 --> 0:10:39.640 notions in economic theory to to help structure those you know, what, 0:10:39.720 --> 0:10:42.040 we where we expect to find those spillovers and sort 0:10:42.040 --> 0:10:44.559 of where therefore to sort of shine the light and 0:10:44.559 --> 0:10:47.480 and try to see them. H And another way were 0:10:47.520 --> 0:10:49.559 to say it would be we can sort of structure 0:10:49.600 --> 0:10:52.079 things so that we know the kinds of places that 0:10:52.120 --> 0:10:54.679 are likely to have had a full treatment. Other places 0:10:54.679 --> 0:10:56.840 maybe that we we think they whatever the treatment was, 0:10:56.880 --> 0:10:58.880 we know that some other place had three quarters of that, 0:10:59.040 --> 0:11:01.040 or half of that, or a quarter of that, or 0:11:01.080 --> 0:11:03.320 maybe finally just a place that we're pretty confident would 0:11:03.320 --> 0:11:06.800 have been almost an affected by the event. So we're 0:11:06.800 --> 0:11:09.720 always trying to do that sort of thing. And I 0:11:09.720 --> 0:11:11.680 guess that's a high level overview of how I thought 0:11:11.679 --> 0:11:14.400 about the problem. I want to ask you to talk 0:11:14.440 --> 0:11:16.680 a little bit more about the process, Like how many 0:11:16.760 --> 0:11:19.680 times did you have to travel to India, how many 0:11:19.720 --> 0:11:21.720 libraries did you have to visit? What was it like 0:11:21.800 --> 0:11:24.840 digging up the archives of all this data that you 0:11:24.920 --> 0:11:28.240 discovered about the uh you know what specific data you 0:11:28.400 --> 0:11:30.200 ended up looking at, and then also you know what 0:11:30.280 --> 0:11:34.400 did you discover about the ramifications of the railway build up? Yeah, 0:11:34.440 --> 0:11:39.400 I mean the process was certainly unlike anything I imagine 0:11:39.440 --> 0:11:42.840 I would end up doing doing my PhD. Um the 0:11:42.880 --> 0:11:44.280 first thing that you just have to find out what 0:11:44.360 --> 0:11:47.360 data exists, So that's UM kind of a scoping project. 0:11:48.240 --> 0:11:50.559 I this never would have been possible. I lived in 0:11:50.600 --> 0:11:52.200 London at the time I was doing my PhD. At 0:11:52.200 --> 0:11:54.040 the London School of Economics. And none of this would 0:11:54.040 --> 0:11:55.480 have been possible if it weren't for the fact that 0:11:55.520 --> 0:11:59.000 the main libraries in London actually had as good a 0:11:59.040 --> 0:12:03.880 collection of kind of official government British Indian publications as 0:12:03.960 --> 0:12:07.800 did any library in India, almost better maybe because things 0:12:07.880 --> 0:12:10.319 have been preserved better. Yeah, so I that, I mean, 0:12:10.360 --> 0:12:12.960 that made it all sort of possible given where I 0:12:13.000 --> 0:12:17.040 was based, and also made it possible because just getting 0:12:17.120 --> 0:12:20.200 the sort of numbers out of the books was of 0:12:20.240 --> 0:12:25.160 course just a huge, huge project because obviously I think, 0:12:25.240 --> 0:12:27.680 I it's complete back at the envelope calculation. But I 0:12:27.720 --> 0:12:29.800 once convinced myself there was somewhere between fifty and a 0:12:29.920 --> 0:12:32.480 hundred man you know, but a person years worth of 0:12:32.800 --> 0:12:35.160 the work involved in typing in the numbers. So I 0:12:35.160 --> 0:12:37.800 was never going to do it myself. And so that 0:12:37.880 --> 0:12:40.320 but that process, you know, very Fortunately, just around the 0:12:40.320 --> 0:12:42.079 time I was doing this, in the mid two thousands, 0:12:42.200 --> 0:12:45.360 late two thousands, was just when people started talking about 0:12:45.400 --> 0:12:48.880 business process outsourcing, you know, the ability to hire, um, 0:12:48.960 --> 0:12:52.000 somebody to do a fairly low skilled task like typing 0:12:52.000 --> 0:12:55.040 in numbers on a computer terminal far away. If you know, 0:12:55.040 --> 0:12:58.240 if only you can find them, you know, And of 0:12:58.280 --> 0:13:00.760 course that wasn't hard by that period of time. And 0:13:00.800 --> 0:13:03.480 pay them that was also relatively easy once I could 0:13:03.559 --> 0:13:06.400 raise the money and then finally just get the get 0:13:06.400 --> 0:13:09.480 the raw materials to them. And I could never send 0:13:09.480 --> 0:13:11.920 these books obviously they're they're way too big and and 0:13:11.920 --> 0:13:14.840 and they're obviously they're held in libraries. But but I 0:13:14.880 --> 0:13:17.200 was able to organize a team of people to take 0:13:17.360 --> 0:13:20.000 kind of digital photographs of every page. It kind of 0:13:20.040 --> 0:13:23.840 like scanning, but way faster. You know, scanning is super slow. Um. 0:13:24.240 --> 0:13:26.720 But luckily digital cameras just became good enough around that 0:13:26.720 --> 0:13:30.960 time where archived based historians like me were able to um, 0:13:31.160 --> 0:13:33.320 kind of skip the scanner, skip the photocopy, and just 0:13:33.360 --> 0:13:35.520 go straight to a good digital camera. Anyway, So I 0:13:35.559 --> 0:13:39.160 sent you a fifty thousand JPEGs or something. Took almost 0:13:39.200 --> 0:13:41.640 half a year just to organize the jpeg send them 0:13:41.640 --> 0:13:46.360 to a number of firms in in India actually, and uh, 0:13:46.520 --> 0:13:49.319 and they sent back spreadsheets full of you know, the 0:13:49.600 --> 0:13:53.720 numbers typed in. Um. You know, the the original publications 0:13:53.720 --> 0:13:59.120 were kind of too low quality to trust optical digital 0:13:59.200 --> 0:14:03.200 character recognition. And uh, at least at that period of 0:14:03.240 --> 0:14:06.160 that technology. Yeah, so I guess you know that that's 0:14:06.240 --> 0:14:10.080 the basic idea of how I and many people like 0:14:10.240 --> 0:14:13.480 me kind of would convert the sort of archival paper 0:14:13.480 --> 0:14:20.360 records into digital machine readable versions. And then right, so 0:14:20.400 --> 0:14:21.840 I guess you know. There are a number of things 0:14:21.840 --> 0:14:24.960 I looked at. I looked at trade actual volumes of 0:14:25.000 --> 0:14:28.960 trade flows. I looked at prices, that is, um, whether 0:14:29.000 --> 0:14:32.480 there was evidence that um, you know, it gets a 0:14:32.520 --> 0:14:36.000 basic prediction that is, when two markets get connected by 0:14:37.200 --> 0:14:40.080 via some technology that makes it easier to move something 0:14:40.160 --> 0:14:42.360 between the markets, then the price of that thing should 0:14:42.440 --> 0:14:45.520 look more similar. That's often just the basic notion of arbitrage. 0:14:45.680 --> 0:14:47.640 If the price were different, then yet it didn't cost 0:14:47.720 --> 0:14:49.600 much to move it. You can always just sort of 0:14:49.600 --> 0:14:52.880 buy in the cheap place and then sell the good 0:14:52.920 --> 0:14:55.120 at the high at the high price place. So we 0:14:55.240 --> 0:14:58.480 kind of think that if if arbitrage is likely to 0:14:58.560 --> 0:15:02.640 be like that likely to happen, and it certainly did 0:15:03.040 --> 0:15:06.440 in this environment of British India, then we would expect 0:15:06.440 --> 0:15:09.560 that the railroads should narrow the difference in prices of 0:15:09.600 --> 0:15:12.760 exactly the same good over over to say two points 0:15:12.760 --> 0:15:15.800 in space. And there's a the data was consistent with 0:15:15.840 --> 0:15:18.800 that obviously as well that as railroads connected places, their 0:15:18.800 --> 0:15:22.160 prices started to converge. And then finally I looked at 0:15:22.280 --> 0:15:25.920 um the kind of consequences for income. As you put it, 0:15:26.560 --> 0:15:29.840 there's such a stresses sort of aggregate income, think of 0:15:29.920 --> 0:15:32.880 kind of like a county or a district as they 0:15:32.920 --> 0:15:35.000 were called in British India. We're looking at the just 0:15:35.680 --> 0:15:37.800 this is is not individual people. I wasn't able to 0:15:37.840 --> 0:15:40.600 look at inequality or anything important like that, but on aggregate, 0:15:40.640 --> 0:15:42.960 the sort of the closest notion we could get to, 0:15:43.120 --> 0:15:46.480 sort of g d P of of a county kind 0:15:46.480 --> 0:15:49.840 of went up and on average went up. And you're right, 0:15:49.840 --> 0:15:52.640 because the spillovers and and the fact that everybody's experience 0:15:52.680 --> 0:15:55.120 was different. These averages can be of course a little misleading, 0:15:55.160 --> 0:15:57.240 but but they represent the average, and the average was 0:15:57.360 --> 0:16:00.360 an increase of about eighteen percent in g d P. Huh. 0:16:00.840 --> 0:16:07.080 We are going to pause for a short break, but 0:16:07.240 --> 0:16:09.640 knowledge to work and grow your business with c i T. 0:16:10.200 --> 0:16:14.120 From transportation to healthcare to manufacturing. C i T offers 0:16:14.160 --> 0:16:18.000 commercial lending, leasing, and treasury management services for small and 0:16:18.040 --> 0:16:21.400 middle market businesses. Learn more at c i T dot com. 0:16:21.400 --> 0:16:29.920 Put knowledge to work, David. I know the Railroads of 0:16:30.000 --> 0:16:33.600 the RAJ paper is probably your most famous work, but 0:16:33.800 --> 0:16:36.640 a lot of people were pointing out when you won 0:16:36.720 --> 0:16:40.800 the John Bates Metal that it was fantastic that you 0:16:40.840 --> 0:16:45.920 had won it. All the two hundredth anniversary or birthday 0:16:46.080 --> 0:16:49.920 of the notion of comparative advantage, which is of course 0:16:50.520 --> 0:16:53.360 a big, big deal in economics, and it's basically the 0:16:53.440 --> 0:16:58.360 idea that people can specialize in one type of industry 0:16:58.480 --> 0:17:00.880 or production, and then they can trade with other people 0:17:00.920 --> 0:17:05.320 who are also specialized and everyone can eventually benefit. Walk 0:17:05.400 --> 0:17:08.200 us through your work when it comes to comparative advantage. 0:17:08.200 --> 0:17:11.440 What have you been looking at and what have you found? All? 0:17:11.520 --> 0:17:14.439 Everything I've done on comparative advantage. In fact, most of 0:17:14.480 --> 0:17:17.600 my work really since my doctoral thesis has been joined 0:17:17.640 --> 0:17:19.560 with a guy at m I T named are No 0:17:19.760 --> 0:17:23.920 Costano and and so we um we started thinking about 0:17:24.920 --> 0:17:28.080 comparative advantage and you're you're right. It was about two 0:17:28.359 --> 0:17:30.520 d years ago to this kind of day that David 0:17:30.600 --> 0:17:34.520 Ricardo really wrote down the first logical argument about why 0:17:35.119 --> 0:17:38.360 trade between two people or two regions or two countries 0:17:38.359 --> 0:17:42.320 would would you know, should benefit both of those people? 0:17:42.320 --> 0:17:46.120 In that trade, and and the essence of his argument 0:17:46.280 --> 0:17:49.240 was just a kind of simple example with two activities. 0:17:49.280 --> 0:17:52.040 They were cloth and wine, and his example that and 0:17:52.119 --> 0:17:55.440 two countries. These were England and Portugal, and the jet. 0:17:55.480 --> 0:17:58.640 The example completely generalizes to as many countries and products 0:17:58.640 --> 0:18:00.399 as you want. But but the two by two was 0:18:00.400 --> 0:18:03.160 the minimum ingredient to kind of see the point. So 0:18:04.040 --> 0:18:06.119 the one kind of catch that are know and I 0:18:06.160 --> 0:18:08.240 got interested in, and this this point was well known, 0:18:08.320 --> 0:18:11.159 but we kind of we were were a little bit 0:18:11.200 --> 0:18:14.359 taken aback when we discovered it for ourselves, was that, Um, 0:18:15.119 --> 0:18:18.840 once those two regions are actually trading, you know, I 0:18:18.840 --> 0:18:21.879 suppose you observe them today, suppose you were observed England, 0:18:22.040 --> 0:18:24.200 or even in David Kardo's time, you observe England and 0:18:24.320 --> 0:18:27.040 Portugal they're actually trading cloth and line with each other. 0:18:27.640 --> 0:18:30.439 Then you know, according to that very same model in 0:18:30.480 --> 0:18:32.720 which that that you're using to explain the trade and 0:18:32.720 --> 0:18:36.760 the understand the consequences of trade in that exact same model, um, 0:18:36.800 --> 0:18:39.320 actually it would have to be the case that one 0:18:39.400 --> 0:18:41.560 of one of the two activities is not being done 0:18:41.640 --> 0:18:44.720 in one of the two countries, at least as as 0:18:44.760 --> 0:18:47.240 you put it kind of there will be specialization and 0:18:47.240 --> 0:18:49.159 and and that means, you know, in the sense that 0:18:49.240 --> 0:18:51.639 there's something that somebody's not doing. So I guess it 0:18:51.680 --> 0:18:53.800 won't surprise you that we kind of predict you'd expect 0:18:53.880 --> 0:18:57.600 that England was not producing much or any wine, right, Um, 0:18:57.680 --> 0:19:00.119 they that was sort of the result of specialization, was 0:19:00.160 --> 0:19:03.040 that England was not producing wine. And the theory tells 0:19:03.080 --> 0:19:05.679 us that there's good reasons for that. It's that England 0:19:05.720 --> 0:19:08.080 is relatively worse at making wine than they aren't making 0:19:08.080 --> 0:19:11.440 cloth relative to Portugal. But the question is how bad 0:19:11.440 --> 0:19:13.720 are they making wine? And we kind of know they're bad, 0:19:13.920 --> 0:19:16.359 both from introspection and also from the theory. We know 0:19:16.440 --> 0:19:18.520 that they couldn't have been good, or else they would 0:19:18.560 --> 0:19:22.240 have been producing it. But how bad and so And 0:19:22.440 --> 0:19:25.480 of course that basic idea pervades all of economic life. 0:19:25.520 --> 0:19:27.480 I mean, I know I benefit from not doing my 0:19:27.520 --> 0:19:30.000 own dental work, But how bad would life be if 0:19:30.040 --> 0:19:32.159 I had to be my own dentist? You know, I 0:19:32.160 --> 0:19:33.959 I don't know, but I know it would be awful. 0:19:34.480 --> 0:19:37.439 And that kind of unknown number, it's not just unknown, 0:19:37.440 --> 0:19:40.480 it's kind of unknowable, right, I mean, Um, it's so 0:19:40.560 --> 0:19:43.280 that so basically everything I've been working on with comparative 0:19:43.280 --> 0:19:47.160 advantage with our no has this flavor of how could 0:19:47.160 --> 0:19:50.399 an economist ever hope to know that fourth number in 0:19:50.480 --> 0:19:54.080 Ricardo's example, That's the basic idea we got interested in 0:19:54.119 --> 0:19:58.960 where one could know that in general that sort of 0:19:58.960 --> 0:20:03.280 fourth number, or generally, just how good are regions at 0:20:03.320 --> 0:20:06.920 doing the things they don't do? And um, the first 0:20:06.960 --> 0:20:09.800 example that came to our mind was agriculture. We thought, 0:20:10.600 --> 0:20:15.320 that's a case where you know, um, nowadays, regions of 0:20:15.400 --> 0:20:20.359 let's say Iowa, most most farms grow either corn or 0:20:20.520 --> 0:20:23.639 soy or maybe wheat. You know, other regions of the 0:20:23.680 --> 0:20:27.679 US do different crops. And but so the question again 0:20:27.760 --> 0:20:30.280 comes up, well, how bad how bad would life be 0:20:30.440 --> 0:20:34.359 if we didn't sort of outsource all of our specialization, 0:20:34.720 --> 0:20:37.359 all of our output of corn and soy to to 0:20:37.520 --> 0:20:39.359 the corn and soy belt, right, I mean, what if 0:20:39.400 --> 0:20:41.320 we had to do it ourselves in New St New England, 0:20:41.400 --> 0:20:44.800 or in California or in the southeast. You know, we were, 0:20:44.880 --> 0:20:47.400 of course, you know, since discovered, but deep down weren't 0:20:47.440 --> 0:20:49.760 too surprised to learn that that there are, of course, 0:20:49.920 --> 0:20:53.440 entire scientific fields, typically under the name of agronomy where 0:20:53.480 --> 0:20:56.280 their goal is to, in a sense kind of just 0:20:56.400 --> 0:20:59.520 try to tell farmers, you know, how, given your soil, 0:20:59.560 --> 0:21:02.880 given your climate, given everything else about your local environment, 0:21:03.280 --> 0:21:06.840 how good would you be at growing any of the 0:21:06.880 --> 0:21:09.480 following kind of list of crops. You know, corn and soy, 0:21:09.600 --> 0:21:13.840 but also cotton, and and and and wheat and peanuts, 0:21:13.920 --> 0:21:16.920 you know, And and it's that advice that agronomous give 0:21:16.960 --> 0:21:19.000 to farmers that of course helps those farmers make the 0:21:19.080 --> 0:21:21.879 right decision about what to grow. But we so we 0:21:21.920 --> 0:21:24.080 sort of in a sense kind of downloaded that advice 0:21:24.119 --> 0:21:25.960 in a in a data file, you know, the advice 0:21:25.960 --> 0:21:28.800 from the agronomous, the actual numbers on you know, if 0:21:28.840 --> 0:21:30.840 this small parcel of land somewhere in the U s. 0:21:30.880 --> 0:21:33.320 Divide the US up into about a million small parcels 0:21:33.320 --> 0:21:35.800 of land, and the agronomos will tell you how good 0:21:35.840 --> 0:21:39.080 each parcel would be at a whole, at virtually any crop. 0:21:39.640 --> 0:21:41.760 And of course it doesn't mean the agronomous are necessarily right, 0:21:41.760 --> 0:21:43.560 but they but they have kind of hundreds of years 0:21:43.600 --> 0:21:47.120 of their own randomized trials in a sense, and and 0:21:47.720 --> 0:21:51.080 greenhouse trials, etcetera, and all physiological knowledge of how crops 0:21:51.080 --> 0:21:53.720 work too in order to build up those numbers. So anyway, 0:21:53.760 --> 0:21:55.920 so we were kind of designed to study around that 0:21:56.119 --> 0:21:58.399 information because we thought it was it was it was 0:21:58.440 --> 0:22:01.840