1 00:00:00,320 --> 00:00:04,080 Speaker 1: Big data. In much of the developed world, it can 2 00:00:04,120 --> 00:00:07,560 Speaker 1: refer to the analysis of vast quantities of information to 3 00:00:07,640 --> 00:00:12,559 Speaker 1: search for valuable trends. Yet in developing nations, where actual 4 00:00:12,680 --> 00:00:17,320 Speaker 1: economic data happens to be pretty scarce, using big data 5 00:00:17,480 --> 00:00:21,000 Speaker 1: is the latest way to truly understand what is happening 6 00:00:21,120 --> 00:00:24,400 Speaker 1: in a country's economy. And this just happens to be 7 00:00:24,600 --> 00:00:29,280 Speaker 1: pretty done interesting, from satellites the track oil storage facilities 8 00:00:29,280 --> 00:00:33,040 Speaker 1: in China to using cell phone data to monitor poverty 9 00:00:33,080 --> 00:00:46,720 Speaker 1: across Africa, where simply going where government figures don't welcome 10 00:00:46,760 --> 00:00:50,240 Speaker 1: to benchmark. I'm Scott Landman, an economics editor with Bloomberg 11 00:00:50,240 --> 00:00:54,360 Speaker 1: in Washington, and I'm Daniel Moss, global economics writer at 12 00:00:54,360 --> 00:00:58,400 Speaker 1: Bloomberg View in New York. So, Dan, we take it 13 00:00:58,440 --> 00:01:02,680 Speaker 1: for granted the existence of reliable economic data in our jobs. 14 00:01:02,680 --> 00:01:05,160 Speaker 1: I mean, I edit our u S economy coverage, we 15 00:01:05,200 --> 00:01:11,640 Speaker 1: get bountiful data unemployment, GDP, inflation. But that's not the 16 00:01:11,640 --> 00:01:20,040 Speaker 1: case everywhere, right, Corporations, governments, lender's investors, everyone's making decisions 17 00:01:20,280 --> 00:01:25,080 Speaker 1: based on assumptions about the economic trajectory. But what if 18 00:01:25,120 --> 00:01:29,440 Speaker 1: those assumptions are built on data that isn't reliable. Well, 19 00:01:29,440 --> 00:01:31,720 Speaker 1: that's what we're going to talk about today. Before we 20 00:01:31,760 --> 00:01:35,440 Speaker 1: bring in Joshua Blumenstock, a professor who does research based 21 00:01:35,480 --> 00:01:37,840 Speaker 1: on big data, I wanted to talk first with a 22 00:01:37,920 --> 00:01:41,840 Speaker 1: Bloomberg colleague who has taken a keen interest in this topic. 23 00:01:42,360 --> 00:01:45,960 Speaker 1: Jeff Karns is our China Economy editor, and he joins 24 00:01:46,000 --> 00:01:49,720 Speaker 1: us from Beijing. Jeff, it's pretty late night where you are, 25 00:01:50,000 --> 00:01:55,560 Speaker 1: so I'll say, Wan Chong, how hid? And I'll say, Jeff, 26 00:01:55,840 --> 00:01:57,880 Speaker 1: can you tell us a little bit about how people 27 00:01:57,920 --> 00:02:01,760 Speaker 1: are using unusual sources of to understand what's going on 28 00:02:01,880 --> 00:02:04,280 Speaker 1: in China's economy. Since you've spent a lot of time 29 00:02:04,280 --> 00:02:07,720 Speaker 1: on this topic, well, there's a lot going on. I 30 00:02:07,760 --> 00:02:10,200 Speaker 1: think that it's Uh, it's a really unique case because 31 00:02:10,240 --> 00:02:13,120 Speaker 1: not only is this the you know, the second largest 32 00:02:13,160 --> 00:02:16,040 Speaker 1: economy in the world, but it's also it's growing very 33 00:02:16,080 --> 00:02:19,720 Speaker 1: quickly and on pace to become the largest. But it's 34 00:02:19,800 --> 00:02:23,519 Speaker 1: so much less well understood than a lot of other 35 00:02:23,560 --> 00:02:26,320 Speaker 1: countries out there, and the depth and breadth of the 36 00:02:26,440 --> 00:02:30,480 Speaker 1: data are really not what folks like yourself, uh, and 37 00:02:30,520 --> 00:02:33,880 Speaker 1: a lot of institutional investors and economists might be used to, 38 00:02:34,639 --> 00:02:39,320 Speaker 1: and that makes it really a great proving ground for 39 00:02:39,400 --> 00:02:43,280 Speaker 1: new technologies to look at everything from consumers spending to 40 00:02:44,040 --> 00:02:47,600 Speaker 1: crude oil storage, to kind of get past the government 41 00:02:47,680 --> 00:02:51,799 Speaker 1: data which may be spotty or not trusted, and use 42 00:02:52,560 --> 00:02:56,519 Speaker 1: new and independent ways of looking at these things to 43 00:02:57,280 --> 00:02:59,760 Speaker 1: get past the curtain of that. And so that's something 44 00:02:59,760 --> 00:03:03,600 Speaker 1: that we've written a few things about and hope to 45 00:03:03,639 --> 00:03:07,640 Speaker 1: do more. We're really interested in the speed of things 46 00:03:07,639 --> 00:03:10,400 Speaker 1: that are being put out in real time, from consumer 47 00:03:11,000 --> 00:03:16,800 Speaker 1: credit card swipes to spending on mobile apps, and also 48 00:03:17,000 --> 00:03:22,520 Speaker 1: looking at the bigger picture of the commodities market for oil, 49 00:03:22,639 --> 00:03:26,320 Speaker 1: which is China's the biggest consumer of oil, and we're 50 00:03:26,360 --> 00:03:29,640 Speaker 1: really excited about all the new things that are coming out. So, Jeff, 51 00:03:29,680 --> 00:03:33,080 Speaker 1: if I understand what you're saying correctly, China's economy is 52 00:03:33,120 --> 00:03:37,240 Speaker 1: so big yet so little understood. It's almost like was 53 00:03:37,280 --> 00:03:41,760 Speaker 1: the fertile ground just waiting for this big data test 54 00:03:41,840 --> 00:03:45,880 Speaker 1: case and this is it. Well, it is a test case. 55 00:03:46,000 --> 00:03:48,160 Speaker 1: Is probably a good point. You reached an issue that 56 00:03:48,560 --> 00:03:52,000 Speaker 1: for economists, they like to have the long series going 57 00:03:52,040 --> 00:03:56,320 Speaker 1: back decades to look at very different economic eras across time. 58 00:03:56,720 --> 00:03:58,600 Speaker 1: But when you are going to look at these kind 59 00:03:58,600 --> 00:04:01,520 Speaker 1: of new things, whether they're it's data coming from satellites 60 00:04:02,120 --> 00:04:05,680 Speaker 1: or from Internet traffic or something like that. You're not 61 00:04:05,680 --> 00:04:07,600 Speaker 1: gonna be able to go back very far, so there 62 00:04:07,640 --> 00:04:09,840 Speaker 1: is a definite trade off. You'll get the speed and 63 00:04:09,880 --> 00:04:13,160 Speaker 1: immediacy of being able to do something very quickly, but 64 00:04:13,320 --> 00:04:15,880 Speaker 1: you may only be able to go back five years. 65 00:04:15,880 --> 00:04:18,520 Speaker 1: So there's a real trade off for what economists like 66 00:04:18,640 --> 00:04:22,640 Speaker 1: to have in terms of both the length and the 67 00:04:22,720 --> 00:04:26,360 Speaker 1: quality of the data. This isn't just in China, Jeff. 68 00:04:26,360 --> 00:04:29,800 Speaker 1: This is also something's happening in the rest of the world. Right, 69 00:04:30,200 --> 00:04:34,839 Speaker 1: that's right. The advanced and wealthier economies have generally pretty 70 00:04:34,880 --> 00:04:38,479 Speaker 1: good government data that's that's trusted and can complete and 71 00:04:38,520 --> 00:04:43,320 Speaker 1: measures things like wages and inflation and all of these 72 00:04:43,320 --> 00:04:47,800 Speaker 1: different things that have been independently measured and checked by 73 00:04:47,839 --> 00:04:52,680 Speaker 1: government specialists for form decades or more. And when you 74 00:04:52,960 --> 00:04:54,920 Speaker 1: when you get beyond that and you go to places 75 00:04:55,000 --> 00:04:59,160 Speaker 1: like Africa and see that there's maybe only in the 76 00:04:59,160 --> 00:05:02,600 Speaker 1: whole cluster of African nations, even only a handful of 77 00:05:02,960 --> 00:05:06,479 Speaker 1: countries that have this data, it maybe is more appealing 78 00:05:06,520 --> 00:05:10,320 Speaker 1: to turn to an alternative like satellites that can track 79 00:05:10,560 --> 00:05:14,720 Speaker 1: how much light the countries are throwing off at night 80 00:05:15,240 --> 00:05:19,120 Speaker 1: to kind of serve as a proxy for small countries 81 00:05:19,160 --> 00:05:21,760 Speaker 1: that may be producing very little economic data or not 82 00:05:21,880 --> 00:05:23,880 Speaker 1: at all. You know, Jeff, one of the things that's 83 00:05:23,920 --> 00:05:26,920 Speaker 1: really cool when I hear you talk about this is 84 00:05:27,160 --> 00:05:30,719 Speaker 1: too often people consider the study of economics to be 85 00:05:31,160 --> 00:05:35,640 Speaker 1: a marriage to dry statistics. Yet you're talking about satellites 86 00:05:35,839 --> 00:05:41,040 Speaker 1: flying over car parks, you're talking about credit cards, swipes, 87 00:05:41,680 --> 00:05:46,280 Speaker 1: you're talking about pretty hip things. Well, these are very hip, 88 00:05:46,320 --> 00:05:48,680 Speaker 1: they're also they're also very new. One of the things 89 00:05:48,720 --> 00:05:52,320 Speaker 1: that's interesting about China specifically is that there's a high 90 00:05:52,400 --> 00:05:55,000 Speaker 1: degree of centralization that probably people and most of the 91 00:05:55,040 --> 00:05:58,960 Speaker 1: rest of the world wouldn't really recognize. For bank cards, 92 00:05:59,000 --> 00:06:02,200 Speaker 1: there's really one company called Union Pay that handles everything, 93 00:06:02,720 --> 00:06:06,120 Speaker 1: and beyond that, there's two platforms, we Chat and Ali 94 00:06:06,160 --> 00:06:09,640 Speaker 1: Baba's Ali Pay, where people use their phones to pay, 95 00:06:09,720 --> 00:06:11,640 Speaker 1: and that's about it. You can kind of live on 96 00:06:11,680 --> 00:06:16,120 Speaker 1: those things forever. And these things are very centralized, and 97 00:06:16,160 --> 00:06:19,760 Speaker 1: so when you have all of the bank card information 98 00:06:20,240 --> 00:06:24,640 Speaker 1: going through one provider, you can drill into all kinds 99 00:06:24,640 --> 00:06:28,760 Speaker 1: of different parts of the economy like karaoke bars and 100 00:06:30,120 --> 00:06:34,479 Speaker 1: hot pot restaurants and see these things at one stop shop. 101 00:06:35,279 --> 00:06:38,000 Speaker 1: Last question, Jeff before we let you go to bed, 102 00:06:38,480 --> 00:06:41,359 Speaker 1: And we have to address this. Every time there's a 103 00:06:41,400 --> 00:06:46,719 Speaker 1: discussion about Chinese numbers, whether they're perceived as good numbers 104 00:06:46,800 --> 00:06:50,960 Speaker 1: or bad numbers, there's this cry, oh, they're fake. Everyone 105 00:06:51,040 --> 00:06:54,840 Speaker 1: knows they're made up. The government said, so, where does 106 00:06:54,880 --> 00:06:57,839 Speaker 1: all this come from? My understanding, as it comes from 107 00:06:58,000 --> 00:07:02,920 Speaker 1: a leaked cable based on a discussion between Prime Minister 108 00:07:03,000 --> 00:07:07,600 Speaker 1: Leak Cheng, then a provincial official and a US diplomat 109 00:07:07,600 --> 00:07:10,400 Speaker 1: ten years ago, where does this idea that the data 110 00:07:10,560 --> 00:07:15,200 Speaker 1: is fake come from? Well, that's one. That's one place, 111 00:07:15,240 --> 00:07:18,360 Speaker 1: and that that is something that that did gain some notoriety. 112 00:07:19,280 --> 00:07:21,520 Speaker 1: But it's interesting because what was being discussed in that 113 00:07:21,680 --> 00:07:24,120 Speaker 1: was things that we would think of as being very old, 114 00:07:24,240 --> 00:07:29,400 Speaker 1: like rail car volumes and electricity consumption. It's a really 115 00:07:29,400 --> 00:07:34,160 Speaker 1: different economy now and these new measures are kind of 116 00:07:34,200 --> 00:07:37,160 Speaker 1: providing a way to add a different layer of truth 117 00:07:37,200 --> 00:07:42,880 Speaker 1: to that. And also the question of the vacity of 118 00:07:42,920 --> 00:07:45,720 Speaker 1: the data hasn't gone away. The government said just this 119 00:07:45,840 --> 00:07:50,040 Speaker 1: year the two provinces had faked different economic indicators. They 120 00:07:50,040 --> 00:07:52,600 Speaker 1: didn't say exactly what or how or when or who, 121 00:07:52,840 --> 00:07:57,480 Speaker 1: but this is something that the government revealed on its own, 122 00:07:57,480 --> 00:08:00,080 Speaker 1: and so no less of authorities than President She and 123 00:08:00,160 --> 00:08:03,280 Speaker 1: King has said earlier this year that officials must be 124 00:08:03,560 --> 00:08:07,200 Speaker 1: frank and forth right with their reports. So when we 125 00:08:07,240 --> 00:08:09,680 Speaker 1: look at the big data, though it really lines up 126 00:08:09,680 --> 00:08:11,360 Speaker 1: a lot with the official numbers, we don't see a 127 00:08:11,400 --> 00:08:15,360 Speaker 1: big difference. We don't see that the government numbers are phony, 128 00:08:15,480 --> 00:08:18,920 Speaker 1: and it's really the economy is growing a lot less quickly. 129 00:08:19,360 --> 00:08:22,200 Speaker 1: It's a supplemental thing instead of being a totally different 130 00:08:22,200 --> 00:08:26,840 Speaker 1: story from fake data to virtually no data. Let's turn 131 00:08:26,920 --> 00:08:31,160 Speaker 1: to a true expert on this topic now. Joshua Blumenstock 132 00:08:31,280 --> 00:08:35,000 Speaker 1: is an assistant professor at the University of California Berkeley 133 00:08:35,120 --> 00:08:38,880 Speaker 1: School of Information and the director of the Data Intensive 134 00:08:38,920 --> 00:08:42,760 Speaker 1: Development Lab. He focuses on using novel data and methods 135 00:08:42,800 --> 00:08:46,840 Speaker 1: to better understand the causes and consequences of global poverty. 136 00:08:47,280 --> 00:08:50,160 Speaker 1: Josh thanks for joining us today. Thanks for having me. Josh, 137 00:08:50,200 --> 00:08:52,440 Speaker 1: Can you first just tell us a little bit about 138 00:08:52,480 --> 00:08:56,240 Speaker 1: yourself and how you came to this research specialty. So, 139 00:08:56,280 --> 00:08:59,440 Speaker 1: I was a liberal arts major in college, and I 140 00:08:59,440 --> 00:09:01,160 Speaker 1: guess I was fortunate to have spent a lot of 141 00:09:01,200 --> 00:09:05,679 Speaker 1: time abroad and growing up. I was always interested in understanding, 142 00:09:05,960 --> 00:09:09,400 Speaker 1: you know, why people were poor and thinking about ways 143 00:09:09,480 --> 00:09:13,200 Speaker 1: that might help people in difficult circumstances. But at the 144 00:09:13,200 --> 00:09:16,439 Speaker 1: same time, I always knew that my comparative advantage was 145 00:09:16,840 --> 00:09:20,040 Speaker 1: working with numbers and data and doing sort of computation. 146 00:09:20,240 --> 00:09:22,840 Speaker 1: So to be honest, I I sort of floundered around 147 00:09:22,840 --> 00:09:25,280 Speaker 1: a little bit because for a long time there wasn't 148 00:09:25,320 --> 00:09:28,880 Speaker 1: a real way to piece together data and quantitative modeling 149 00:09:28,880 --> 00:09:32,200 Speaker 1: with the problems that were really salient and developing countries. 150 00:09:32,760 --> 00:09:36,319 Speaker 1: But starting around ten or fifteen years ago, there tended 151 00:09:36,360 --> 00:09:39,080 Speaker 1: to be more and more sources of really large scale 152 00:09:39,160 --> 00:09:42,440 Speaker 1: big data coming from even the poorest countries in Sub 153 00:09:42,480 --> 00:09:46,400 Speaker 1: Saharan Africa, coming from mobile phone networks, coming from satellite 154 00:09:46,400 --> 00:09:49,120 Speaker 1: imagery and so forth, and so that really created an 155 00:09:49,160 --> 00:09:53,680 Speaker 1: opportunity for sort of quantitative wonks like me to think 156 00:09:53,679 --> 00:09:57,280 Speaker 1: about how those data could be used in development economics research. 157 00:09:57,320 --> 00:10:00,480 Speaker 1: And that's what I do. Now. The two big has 158 00:10:00,520 --> 00:10:05,160 Speaker 1: taken on rather sinister connotations in the US. What you're 159 00:10:05,240 --> 00:10:09,320 Speaker 1: saying is that, in terms of studying poverty and figuring 160 00:10:09,320 --> 00:10:12,320 Speaker 1: out the best way to alleviate that poverty, Big diighter 161 00:10:12,440 --> 00:10:15,240 Speaker 1: is a godsend. Yeah. I mean, you guys really hit 162 00:10:15,240 --> 00:10:18,000 Speaker 1: it on the head and the motivation. Just to pull 163 00:10:18,040 --> 00:10:22,960 Speaker 1: a couple examples. In Angola, the last census prior to 164 00:10:23,160 --> 00:10:26,680 Speaker 1: two thousand fourteen was in nineteen seventy and in between 165 00:10:26,679 --> 00:10:29,840 Speaker 1: those two census is the population of the country grew 166 00:10:29,920 --> 00:10:33,679 Speaker 1: from roughly six million to roughly twenty five million. And 167 00:10:33,720 --> 00:10:36,840 Speaker 1: so you can imagine that if a policymaker or a 168 00:10:36,920 --> 00:10:40,760 Speaker 1: researcher is relying on those data to understand the complexion 169 00:10:40,760 --> 00:10:43,840 Speaker 1: of the country, um, those numbers are just totally off. 170 00:10:43,880 --> 00:10:47,280 Speaker 1: I mean, when the jobs numbers in the US jump 171 00:10:47,320 --> 00:10:49,679 Speaker 1: by a quarter of a point, the it's sort of 172 00:10:49,760 --> 00:10:52,679 Speaker 1: the shock waves reverberate throughout the economy. This is this 173 00:10:52,720 --> 00:10:55,640 Speaker 1: is like a three d fifty percent change um that 174 00:10:55,840 --> 00:10:58,440 Speaker 1: is just not reflected in the official statistics. And so 175 00:10:58,840 --> 00:11:01,560 Speaker 1: I think the idea is, as Jeff was sort of 176 00:11:01,600 --> 00:11:04,920 Speaker 1: hitting on this too, not that big data would replace 177 00:11:05,040 --> 00:11:09,960 Speaker 1: these national official statistics, but when you don't have other information, 178 00:11:10,040 --> 00:11:12,200 Speaker 1: this can provide a supplement to fill in some of 179 00:11:12,200 --> 00:11:14,840 Speaker 1: the gaps. Can you tell us a little bit more 180 00:11:14,960 --> 00:11:19,319 Speaker 1: about how you used cell phone data in Africa to 181 00:11:19,880 --> 00:11:23,640 Speaker 1: analyze poverty or population or whatever it might be. I mean, 182 00:11:23,679 --> 00:11:27,160 Speaker 1: I just I find it really fascinating. With traditional economic data, 183 00:11:27,200 --> 00:11:30,080 Speaker 1: there's a lot of government surveying involved, but you know, 184 00:11:30,160 --> 00:11:33,360 Speaker 1: here you I guess you have vast troves of private 185 00:11:33,440 --> 00:11:36,680 Speaker 1: data that are are helping you find novel ways to 186 00:11:36,840 --> 00:11:40,120 Speaker 1: do research. Yeah, and and Jeff alluded to this as well. 187 00:11:40,160 --> 00:11:42,679 Speaker 1: This is not sort of totally new. People have been 188 00:11:42,760 --> 00:11:46,120 Speaker 1: using satellite data, in particular night light data. So this 189 00:11:46,200 --> 00:11:49,800 Speaker 1: is imagery taken by satellites when it's dark to measure 190 00:11:49,800 --> 00:11:52,400 Speaker 1: economic activity. And for a long time people have known that, 191 00:11:52,800 --> 00:11:55,640 Speaker 1: you know, cities and places that shine brightly tend to 192 00:11:55,640 --> 00:11:58,680 Speaker 1: be wealthier. The problem is those methods don't actually really 193 00:11:58,679 --> 00:12:02,760 Speaker 1: work that well in Africa because there are vast areas 194 00:12:02,760 --> 00:12:05,559 Speaker 1: of the continent that don't have much light that's detectable 195 00:12:05,600 --> 00:12:08,760 Speaker 1: from outer space at night. That doesn't mean they're abjectly poor, 196 00:12:09,360 --> 00:12:11,319 Speaker 1: It just means that they're not They don't have street 197 00:12:11,360 --> 00:12:13,439 Speaker 1: lamps and the sort of things that satellites pick up. 198 00:12:13,960 --> 00:12:17,240 Speaker 1: And so over the last several years, myself and other 199 00:12:17,280 --> 00:12:20,080 Speaker 1: researchers have been looking to all for alternatives to night 200 00:12:20,160 --> 00:12:22,520 Speaker 1: lights data. And so some of the work I've done 201 00:12:22,600 --> 00:12:26,480 Speaker 1: shows that looking at the activity of mobile phone calls 202 00:12:26,760 --> 00:12:30,760 Speaker 1: within a country, and these are fairly precisely located geographically 203 00:12:30,800 --> 00:12:34,000 Speaker 1: because of the physical layout of the mobile phone network. 204 00:12:34,480 --> 00:12:37,880 Speaker 1: That that sort of information contains signs that allow you 205 00:12:37,920 --> 00:12:42,560 Speaker 1: to measure with fairly high accuracy subregional poverty and wealth. 206 00:12:43,240 --> 00:12:46,720 Speaker 1: More recently people have used daytime satellite imagery, the sort 207 00:12:46,720 --> 00:12:49,080 Speaker 1: of images you see when you look on Google Maps 208 00:12:49,360 --> 00:12:51,160 Speaker 1: and you see pictures of your house that you know, 209 00:12:51,520 --> 00:12:53,880 Speaker 1: and when you do that in Africa and now the 210 00:12:53,920 --> 00:12:57,960 Speaker 1: whole world is is imaged fairly frequently. Um, there are 211 00:12:58,000 --> 00:13:01,040 Speaker 1: also correlates of poverty, and you can take fancy machine 212 00:13:01,120 --> 00:13:04,200 Speaker 1: learning algorithms and apply them to the raw data and 213 00:13:04,320 --> 00:13:07,160 Speaker 1: spit out on the other end something like a best 214 00:13:07,240 --> 00:13:10,520 Speaker 1: guess of whether a village is below the poverty line 215 00:13:10,559 --> 00:13:14,319 Speaker 1: or above the poverty line. So that's the poverty identification. 216 00:13:14,480 --> 00:13:17,480 Speaker 1: What about the next step, the alleviation. Does Big Day 217 00:13:17,520 --> 00:13:20,920 Speaker 1: to help with that as well? That's a great question, 218 00:13:21,000 --> 00:13:24,120 Speaker 1: and I think, to be honest, the answer is not yet, 219 00:13:24,160 --> 00:13:27,240 Speaker 1: at least not really. I think right now this is 220 00:13:27,280 --> 00:13:30,480 Speaker 1: really the test balloon phase of this sort of work. 221 00:13:30,520 --> 00:13:33,440 Speaker 1: I mean, these data have really been around for only 222 00:13:33,480 --> 00:13:36,560 Speaker 1: the last five years, and right now the research community 223 00:13:36,640 --> 00:13:39,760 Speaker 1: is working with them very actively, trying to figure out 224 00:13:39,760 --> 00:13:43,280 Speaker 1: basic things like what can you measure accurately and doing 225 00:13:43,320 --> 00:13:45,360 Speaker 1: the sort of things that the research community should do, 226 00:13:45,440 --> 00:13:48,920 Speaker 1: like validate those measurements to make sure they're actually accurate. 227 00:13:49,400 --> 00:13:50,800 Speaker 1: And I think, to be honest, it will take a 228 00:13:50,880 --> 00:13:54,840 Speaker 1: few years before these tools migrate into the policy community. 229 00:13:54,920 --> 00:13:59,080 Speaker 1: I think already you see collaborations forming between people like 230 00:13:59,160 --> 00:14:03,160 Speaker 1: myself and local governments. And I work in places like 231 00:14:03,200 --> 00:14:07,840 Speaker 1: Afghanistan and Malawi and Rwanda and have had many conversations 232 00:14:07,880 --> 00:14:11,480 Speaker 1: and discussions and are fleshing out sort of joint cooperative 233 00:14:11,480 --> 00:14:14,520 Speaker 1: projects with these governments. But not yet have you seen 234 00:14:15,040 --> 00:14:18,760 Speaker 1: active use of these methods to like totally change how 235 00:14:18,920 --> 00:14:22,560 Speaker 1: government policies being made well. That brings us to another question, 236 00:14:22,640 --> 00:14:25,880 Speaker 1: which is, you know, I spend time in China. Chinese 237 00:14:25,880 --> 00:14:28,680 Speaker 1: government likes to keep a very tight lid on the 238 00:14:28,760 --> 00:14:31,560 Speaker 1: kind of statistics that are out there. There's even been 239 00:14:31,600 --> 00:14:34,760 Speaker 1: some private numbers that have kind of disappeared over the 240 00:14:34,840 --> 00:14:36,840 Speaker 1: years when they maybe cut a little too close to 241 00:14:36,880 --> 00:14:39,760 Speaker 1: the bone. You know, in Africa, some of these governments 242 00:14:39,800 --> 00:14:44,920 Speaker 1: aren't known for being well functioning democracies for that matter. Josh, 243 00:14:44,920 --> 00:14:49,160 Speaker 1: you are these governments welcoming of studies like the ones 244 00:14:49,360 --> 00:14:53,360 Speaker 1: that you're doing. I think the answer is always going 245 00:14:53,400 --> 00:14:55,840 Speaker 1: to be it depends by and large, the people that 246 00:14:56,080 --> 00:14:59,880 Speaker 1: I've encountered are actually very receptive to these sort of 247 00:15:00,000 --> 00:15:03,320 Speaker 1: methods and measurements, at least the people that matter are. 248 00:15:03,600 --> 00:15:06,160 Speaker 1: For instance, we're working with the government of Afghanistan and 249 00:15:06,640 --> 00:15:09,640 Speaker 1: they believe that they've never really had an accurate account 250 00:15:09,680 --> 00:15:11,960 Speaker 1: of the number of people in the country, and for 251 00:15:12,040 --> 00:15:15,040 Speaker 1: the top level bureaucrats, this is really something that that 252 00:15:15,080 --> 00:15:17,640 Speaker 1: could be valuable for them, I think at a at 253 00:15:17,680 --> 00:15:20,160 Speaker 1: a higher level, Um, and you guys were touching on 254 00:15:20,200 --> 00:15:24,480 Speaker 1: this earlier. These data also provide some sort of force 255 00:15:24,600 --> 00:15:27,720 Speaker 1: transparency in a sense. Um. So, speaking of China, you know, 256 00:15:27,760 --> 00:15:31,600 Speaker 1: there's studies that use satellite imagery to measure air pollution, 257 00:15:31,920 --> 00:15:34,320 Speaker 1: and what you can see is that the satellite based 258 00:15:34,480 --> 00:15:37,520 Speaker 1: measures of air pollution, which by most accounts should be 259 00:15:37,520 --> 00:15:41,760 Speaker 1: fairly objective, systematically diverge from what have been reported in 260 00:15:41,800 --> 00:15:44,760 Speaker 1: the local provinces. And so I guess the thought is 261 00:15:44,800 --> 00:15:47,840 Speaker 1: that when people know that there are these more objective 262 00:15:47,880 --> 00:15:50,960 Speaker 1: measurements out there, it creates a little bit more of 263 00:15:51,000 --> 00:15:55,600 Speaker 1: a strategic tension when people want to systematically fudge those numbers. Jeff, 264 00:15:55,800 --> 00:15:57,640 Speaker 1: how do you see it from China? Does that make 265 00:15:57,680 --> 00:16:00,640 Speaker 1: sense from from your perspective of I mean, what have 266 00:16:00,680 --> 00:16:02,800 Speaker 1: you seen happen just in the last couple of years 267 00:16:02,840 --> 00:16:06,479 Speaker 1: with these kinds of private statistics, well as private statistics 268 00:16:06,520 --> 00:16:11,680 Speaker 1: there you're your observation about them being subject to higher 269 00:16:11,760 --> 00:16:15,600 Speaker 1: forces is one that's noted. There have been some new 270 00:16:15,600 --> 00:16:18,720 Speaker 1: and exciting numbers that have come out, and not all 271 00:16:18,760 --> 00:16:22,720 Speaker 1: of them are fill around. And as Dan mentioned earlier, 272 00:16:22,720 --> 00:16:26,200 Speaker 1: there's also been questions about the government. But the thing 273 00:16:26,200 --> 00:16:29,280 Speaker 1: about China is that it is so vast and there's 274 00:16:29,320 --> 00:16:32,120 Speaker 1: so much that this stuff is going to come together 275 00:16:32,200 --> 00:16:36,960 Speaker 1: in some kind of colorescent form. The three big technology 276 00:16:37,000 --> 00:16:40,160 Speaker 1: companies Buy Do, Ali, Baba, and ten Cents, the last 277 00:16:40,200 --> 00:16:44,800 Speaker 1: of which operates we Chat, the ubiquitous messaging and financial 278 00:16:44,800 --> 00:16:49,880 Speaker 1: platform increasingly are spitting out more and more ways to 279 00:16:50,400 --> 00:16:54,720 Speaker 1: look at the consumer and mobility and all kinds of 280 00:16:54,720 --> 00:16:57,680 Speaker 1: things that no one's ever been able to look at before, 281 00:16:57,800 --> 00:17:00,560 Speaker 1: and so the top down control of and kind of 282 00:17:00,600 --> 00:17:05,520 Speaker 1: cuts both ways. And these companies, with their total market 283 00:17:05,560 --> 00:17:11,000 Speaker 1: share of of tracking pretty much everybody, uh cast a 284 00:17:11,080 --> 00:17:14,879 Speaker 1: very wide net. And when you have a platform like 285 00:17:14,920 --> 00:17:20,240 Speaker 1: ali Baba that is so deeply enmeshed in a consumer 286 00:17:20,880 --> 00:17:25,120 Speaker 1: e commerce world like this that's bigger than anything that's 287 00:17:25,119 --> 00:17:29,920 Speaker 1: ever existed, you contract things very closely and start seeing 288 00:17:29,960 --> 00:17:33,200 Speaker 1: things over time that you never even would have expected. 289 00:17:33,680 --> 00:17:36,120 Speaker 1: And then you layer on top of that other things 290 00:17:36,160 --> 00:17:40,320 Speaker 1: like location, which the Bidy search engine knows where you are, 291 00:17:40,560 --> 00:17:43,600 Speaker 1: the way they can track who's going to shopping malls 292 00:17:43,800 --> 00:17:48,200 Speaker 1: and office parks and tourist sites and things like that 293 00:17:48,520 --> 00:17:52,000 Speaker 1: in real time, and they can create employment indexes from this, 294 00:17:52,680 --> 00:17:56,600 Speaker 1: where that's a great data point in a place where 295 00:17:56,600 --> 00:17:59,800 Speaker 1: the official unemployment rate nobody really looks at because it's 296 00:17:59,800 --> 00:18:03,719 Speaker 1: not reliable. Well, definitely a space to watch in the future. 297 00:18:04,200 --> 00:18:06,520 Speaker 1: Josh bloomin Stock Jeff Kerns, thank you so much for 298 00:18:06,600 --> 00:18:09,520 Speaker 1: joining us today on the podcast. Thank you guys. Thanks guys. 299 00:18:13,560 --> 00:18:15,919 Speaker 1: Benchmark will be back next week and until then, you 300 00:18:15,960 --> 00:18:18,800 Speaker 1: can find us on the Bloomberg terminal, Bloomberg dot com, 301 00:18:18,880 --> 00:18:22,240 Speaker 1: or Bloomberg App, as well as on Apple Podcasts, pocketcasts, 302 00:18:22,320 --> 00:18:24,720 Speaker 1: and Stitcher. While you're there, take a minute to rate 303 00:18:24,760 --> 00:18:27,040 Speaker 1: and review the show. So more listeners can find us 304 00:18:27,359 --> 00:18:29,160 Speaker 1: and let us know what you thought of the show. 305 00:18:29,520 --> 00:18:32,600 Speaker 1: You can follow me on Twitter at at scott Landman. 306 00:18:32,920 --> 00:18:37,600 Speaker 1: Dan You're at most Underschool Echo, Jeff you are at 307 00:18:37,960 --> 00:18:40,720 Speaker 1: Jeff Kern j E F F K E A r 308 00:18:40,800 --> 00:18:44,840 Speaker 1: n F and Josh you are at J bloomin stock 309 00:18:45,040 --> 00:18:46,720 Speaker 1: J B l U M E N S t O 310 00:18:46,760 --> 00:18:50,280 Speaker 1: c K. Benchmark is produced by Sarah Patterson. The head 311 00:18:50,280 --> 00:18:54,000 Speaker 1: of Bloomberg Podcasts is Francesca Levy. Thanks for listening, See 312 00:18:54,000 --> 00:18:54,560 Speaker 1: you next time.